Comparison of ITRF2014 station coordinate input time series of DORIS, VLBI and GNSS

NASA Astrophysics Data System (ADS)

Tornatore, Vincenza; Tanır Kayıkçı, Emine; Roggero, Marco

2016-12-01

In this paper station coordinate time series from three space geodesy techniques that have contributed to the realization of the International Terrestrial Reference Frame 2014 (ITRF2014) are compared. In particular the height component time series extracted from official combined intra-technique solutions submitted for ITRF2014 by DORIS, VLBI and GNSS Combination Centers have been investigated. The main goal of this study is to assess the level of agreement among these three space geodetic techniques. A novel analytic method, modeling time series as discrete-time Markov processes, is presented and applied to the compared time series. The analysis method has proven to be particularly suited to obtain quasi-cyclostationary residuals which are an important property to carry out a reliable harmonic analysis. We looked for common signatures among the three techniques. Frequencies and amplitudes of the detected signals have been reported along with their percentage of incidence. Our comparison shows that two of the estimated signals, having one-year and 14 days periods, are common to all the techniques. Different hypotheses on the nature of the signal having a period of 14 days are presented. As a final check we have compared the estimated velocities and their standard deviations (STD) for the sites that co-located the VLBI, GNSS and DORIS stations, obtaining a good agreement among the three techniques both in the horizontal (1.0 mm/yr mean STD) and in the vertical (0.7 mm/yr mean STD) component, although some sites show larger STDs, mainly due to lack of data, different data spans or noisy observations.

Bio-optical profile data report: Joint Global Ocean Flux Study, Hawaii Ocean Time-Series, HOT-3, R/V Moana Wave, 6-10 January 1989

NASA Technical Reports Server (NTRS)

Collins, Donald J.; Rhea, W. Joseph; Tran, An Van

1990-01-01

Time-series measurements of the incident surface downwelling irradiance and vertical profiles of the Bio-optical properties of the ocean have been measured during the third cruise of the Hawaii Ocean Time-Series to the ALOHA site, 22 degrees 56.4 minutes N, 157 degrees 54.6 minutes W, north of the island of Oahu, Hawaii, during the period January 6 to 10, 1989. A summary of these data is presented to permit investigators an overview of the data collected. The data are available in digital form for scientific investigators.

The Oceanic Flux Program: A three decade time-series of particle flux in the deep Sargasso Sea

NASA Astrophysics Data System (ADS)

Weber, J. C.; Conte, M. H.

2010-12-01

The Oceanic Flux Program (OFP), 75 km SE of Bermuda, is the longest running time-series of its kind. Initiated in 1978, the OFP has produced an unsurpassed, nearly continuous record of temporal variability in deep ocean fluxes, with a >90% temporal coverage at 3200m depth. The OFP, in conjunction with the co-located Bermuda-Atlantic Time Series (BATS) and the Bermuda Testbed Mooring (BTM) time-series, has provided key observations enabling detailed assessment of how seasonal and non-seasonal variability in the deep ocean is linked with the overlying physical and biogeochemical environment. This talk will focus on the short-term flux variability that overlies the seasonal flux pattern in the Sargasso Sea, emphasizing episodic extreme flux events. Extreme flux events are responsible for much of the year-to-year variability in mean annual flux and are most often observed during early winter and late spring when surface stratification is weak or transient. In addition to biological phenomena (e.g. salp blooms), passage of productive meso-scale features such as eddies, which alter surface water mixing characteristics and surface export fluxes, may initiate some extreme flux events. Yet other productive eddies show a minimal influence on the deep flux, underscoring the importance of upper ocean ecosystem structure and midwater processes on the coupling between the surface ocean environment and deep fluxes. Using key organic and inorganic tracers, causative processes that influence deep flux generation and the strength of the coupling with the surface ocean environment can be identified.

GNSS Network Time Series Analysis

NASA Astrophysics Data System (ADS)

Balodis, J.; Janpaule, I.; Haritonova, D.; Normand, M.; Silabriedis, G.; Zarinjsh, A.; Zvirgzds, J.

2012-04-01

Time series of GNSS station results of both the EUPOS®-RIGA and LATPOS networks has been developed at the Institute of Geodesy and Geoinformation (University of Latvia) using Bernese v.5.0 software. The base stations were selected among the EPN and IGS stations in surroundings of Latvia. In various day solutions the base station selection has been miscellaneous. Most frequently 5 - 8 base stations were selected from a set of stations {BOR1, JOEN, JOZE, MDVJ, METS, POLV, PULK, RIGA, TORA, VAAS, VISO, VLNS}. The rejection of "bad base stations" was performed by Bernese software depending on the quality of proper station data in proper day. This caused a reason of miscellaneous base station selection in various days. The results of time series are analysed. The question aroused on the nature of some outlying situations. The seasonal effect of the behaviour of the network has been identified when distance and elevation changes between stations has been analysed. The dependence from various influences has been recognised.

GNSS Network time series analysis

NASA Astrophysics Data System (ADS)

Normand, M.; Balodis, J.; Janpaule, I.; Haritonova, D.

2012-12-01

Time series of GNSS station results of both the EUPOS®-Riga and LatPos networks have been developed at the Institute of Geodesy and Geoinformation (University of Latvia) using Bernese v.5.0 software. The base stations were selected among the EPN and IGS stations in surroundings of Latvia at the distances up to 700 km. The results of time series are analysed and coordinate velocity vectors have been determined. The background of the map of tectonic faults helps to interpret the GNSS station coordinate velocity vector behaviour in proper environment. The outlying situations recognized. The question still aroused on the nature of the some of outlying situations. The dependence from various influences has been tested.

Synthesis of Natural Electric and Magnetic Time Series Using Impulse Responses of Inter-station Transfer Functions and a Reference

NASA Astrophysics Data System (ADS)

Wang, H.; Cheng, J.

2017-12-01

A method to Synthesis natural electric and magnetic Time series is proposed whereby the time series of local site are derived using an Impulse Response and a reference (STIR). The method is based on the assumption that the external source of magnetic fields are uniform, and the electric and magnetic fields acquired at the surface satisfy a time-independent linear relation in frequency domain.According to the convolution theorem, we can synthesize natural electric and magnetic time series using the impulse responses of inter-station transfer functions with a reference. Applying this method, two impulse responses need to be estimated: the quasi-MT impulse response tensor and the horizontal magnetic impulse response tensor. These impulse response tensors relate the local horizontal electric and magnetic components with the horizontal magnetic components at a reference site, respectively. Some clean segments of times series are selected to estimate impulse responses by using least-square (LS) method. STIR is similar with STIN (Wang, 2017), but STIR does not need to estimate the inter-station transfer functions, and the synthesized data are more accurate in high frequency, where STIN fails when the inter-station transfer functions are contaminated severely. A test with good quality of MT data shows that synthetic time-series are similar to natural electric and magnetic time series. For contaminated AMT example, when this method is used to remove noise present at the local site, the scatter of MT sounding curves are clear reduced, and the data quality are improved. *This work is funded by National Key R&D Program of China(2017YFC0804105),National Natural Science Foundation of China (41604064, 51574250), State Key Laboratory of Coal Resources and Safe Mining ,China University of Mining & Technology,(SKLCRSM16DC09)

Study on common seasonal signals in GPS time series and environmental loadings using Multichannel Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Bogusz, Janusz

2017-04-01

Seasonal oscillations in the GPS position time series can arise from real geophysical effects and numerical artefacts. According to Dong et al. (2002) environmental loading effects can account for approximately 40% of the total variance of the annual signals in GPS time series, however using generally acknowledged methods (e.g. Least Squares Estimation, Wavelet Decomposition, Singular Spectrum Analysis) to model seasonal signals we are not able to separate real from spurious signals (effects of mismodelling aliased into annual period as well as draconitic). Therefore, we propose to use Multichannel Singular Spectrum Analysis (MSSA) to determine seasonal oscillations (with annual and semi-annual periods) from GPS position time series and environmental loading displacement models. The MSSA approach is an extension of the classical Karhunen-Loève method and it is a special case of SSA for multivariate time series. The main advantage of MSSA is the possibility to extract common seasonal signals for stations from selected area and to investigate the causality between a set of time series as well. In this research, we explored the ability of MSSA application to separate real geophysical effects from spurious effects in GPS time series. For this purpose, we used GPS position changes and environmental loading models. We analysed the topocentric time series from 250 selected stations located worldwide, delivered from Network Solution obtained by the International GNSS Service (IGS) as a contribution to the latest realization of the International Terrestrial Reference System (namely ITRF2014, Rebishung et al., 2016). We also researched atmospheric, hydrological and non-tidal oceanic loading models provided by the EOST/IPGS Loading Service in the Centre-of-Figure (CF) reference frame. The analysed displacements were estimated from ERA-Interim (surface pressure), MERRA-land (soil moisture and snow) as well as ECCO2 ocean bottom pressure. We used Multichannel Singular Spectrum

Extracting the regional common-mode component of GPS station position time series from dense continuous network

NASA Astrophysics Data System (ADS)

Tian, Yunfeng; Shen, Zheng-Kang

2016-02-01

We develop a spatial filtering method to remove random noise and extract the spatially correlated transients (i.e., common-mode component (CMC)) that deviate from zero mean over the span of detrended position time series of a continuous Global Positioning System (CGPS) network. The technique utilizes a weighting scheme that incorporates two factors—distances between neighboring sites and their correlations of long-term residual position time series. We use a grid search algorithm to find the optimal thresholds for deriving the CMC that minimizes the root-mean-square (RMS) of the filtered residual position time series. Comparing to the principal component analysis technique, our method achieves better (>13% on average) reduction of residual position scatters for the CGPS stations in western North America, eliminating regional transients of all spatial scales. It also has advantages in data manipulation: less intervention and applicable to a dense network of any spatial extent. Our method can also be used to detect CMC irrespective of its origins (i.e., tectonic or nontectonic), if such signals are of particular interests for further study. By varying the filtering distance range, the long-range CMC related to atmospheric disturbance can be filtered out, uncovering CMC associated with transient tectonic deformation. A correlation-based clustering algorithm is adopted to identify stations cluster that share the common regional transient characteristics.

GEODIS: A Portable Ocean Bottom Very Broadband Seismic Station

NASA Astrophysics Data System (ADS)

KARCZEWSKI, J.; MONTAGNER, J.; BEGUERY, L.; STUTZMANN, E.; ROULT, G.; LOGNONNE, P.; CACHO, S.; KOENIG, J.; SAVARY, J.

2001-12-01

The last ten years have seen the simultaneous development of a global seismic network coordinated through the FDSN (Federation of Digital Seismograph Networks) and of portable broadband seismic arrays. The same approach can be followed for improving our scientific understanding of the Earth processes below oceanic areas. Both components of ocean bottom geophysical networks, will be coordinated by ION (international Ocean Network). They are complementary since they enable to investigate the Earth structure and processes at different spatial and temporal scales. Geophysical Ocean bottom observatories (hereafter referred as GOBO) and portable seismic stations are sharing common technological problems. However, the issues of power supply and real-time data transmission are more crucial for a GOBO than for a portable temporary station. Since 1999, our group is developing a new "portable" geophysical ocean bottom autonomous station, named GEODIS. This station might be a basic element for a GOBO. It relies on the use of adapted VBB sensors issued from space experiments and technology and on improved electronics compared with previous ocean bottom experiments (SISMOBS/OFM 1992; MOISE 1997). The main characteristics of GEODIS are the following: - 3 axes VBB seismic sensors with a classical flat velocity response 360-0.2s. at 2500V/m/s (intrinsic noise level smaller than LNM). - Automatic (under software control) installation, levelling, centring of the 3 component seismic sensors. - 24 bit digitiser recording at 20sps, 3 seismic component and 1 infrasonic sensor. - Recording by a 16 bit converter at 1sps of the sea temperature in the vicinity of the instrument and housekeeping parameters (temperature, inclinations, power,...). - 1 year autonomy by using Lithium batteries. - Storage of data on Flash card and recording on hard disk every day. - Weight of GEODIS: 186kg in air and 110kg in water. - Overall dimensions: 930 x 930 x 440 mm. GEODIS can be easily installed by a

An Investigation of Acoustic Interaction with the Ocean Bottom from Experimental Time Series Generated by Explosive Sources.

DTIC Science & Technology

1983-12-01

near the turbidity channels. Furthermore, Hastrup concludes, after an analysis of time series data taken from the Tyrrhenian abyssal plain, that the top...Bottom-Interacting Ocean Acoustics edited by W. A. Kuperman and F. B. Jensen (Plenum Press, N York, 1980). 84 24. 0. F. Hastrup , "Digital Analysis of

Reconstructing surface ocean circulation with 129I time series records from corals

USGS Publications Warehouse

Chang, Ching-Chih; Burr, George S.; Jull, A. J. Timothy; Russell, Joellen L.; Biddulph, Dana; White, Lara; Prouty, Nancy G.; Chen, Yue-Gau; Chuan-Chou Shen,; Zhou, Weijian; Lam, Doan Dinh

2016-01-01

The long-lived radionuclide 129I (half-life: 15.7 × 106 yr) is well-known as a useful environmental tracer. At present, the global 129I in surface water is about 1–2 orders of magnitude higher than pre-1960 levels. Since the 1990s, anthropogenic 129I produced from industrial nuclear fuels reprocessing plants has been the primary source of 129I in marine surface waters of the Atlantic and around the globe. Here we present four coral 129I time series records from: 1) Con Dao and 2) Xisha Islands, the South China Sea, 3) Rabaul, Papua New Guinea and 4) Guam. The Con Dao coral 129I record features a sudden increase in 129I in 1959. The Xisha coral shows similar peak values for 129I as the Con Dao coral, punctuated by distinct low values, likely due to the upwelling in the central South China Sea. The Rabaul coral features much more gradual 129I increases in the 1970s, similar to a published record from the Solomon Islands. The Guam coral 129I record contains the largest measured values for any site, with two large peaks, in 1955 and 1959. Nuclear weapons testing was the primary 129I source in the Western Pacific in the latter part of the 20th Century, notably from testing in the Marshall Islands. The Guam 1955 peak and Con Dao 1959 increases are likely from the 1954 Castle Bravo test, and the Operation Hardtack I test is the most likely source of the 1959 peak observed at Guam. Radiogenic iodine found in coral was carried primarily through surface ocean currents. The coral 129I time series data provide a broad picture of the surface distribution and depth penetration of 129I in the Pacific Ocean over the past 60 years.

A Deep-Ocean Observatory with Near Real-time Telemetry

NASA Astrophysics Data System (ADS)

Berger, J.; Orcutt, J. A.; Laske, G.

2014-12-01

We describe an autonomously deployable, deep-ocean observatory designed to provide long term and near-real-time observations from sites far offshore. The key feature of this new system is its ability to telemeter sensor data from the seafloor to shore without a cable or moored surface buoy. In the future the observatory will be deployable without a ship. The first application of this system is seismology. While permanent ocean seismic stations on the seafloor have long been a goal of global seismology, today there are still no ocean bottom stations in the Global Seismographic Network, mostly for reasons of life-cycle costs. Yet real-time data from stations in oceanic areas are critical for both national and international agencies in monitoring and characterizing earthquakes, tsunamis, and nuclear explosions. The system comprises an ocean bottom instrumentation package and a free-floating surface communications gateway, which uses a Liquid Robotics wave glider. The glider consists of a surfboard-sized float propelled by a tethered, submerged glider, which converts wave motion into thrust. For navigation, the wave gliders are equipped with a small computer, a GPS receiver, a rudder, solar panels and batteries, and an Iridium satellite modem. Wave gliders have demonstrated trans-oceanic range combined with long-term station holding. The 'communications gateway,' which provides the means of communicating between the ocean bottom package and land comprises a wave glider and a towed acoustic communications 'tow body'. Acoustic communications connect the subsea instruments and the surface gateway while communications between the gateway and land is provided by the Iridium satellite constellation. Tests of the surface gateway in 4350 m of water demonstrated the ability to send four channels of compressed 24-bit, 1 sample per second data from the ocean bottom to the gateway with an average power draw of approximately 0.2 W.

Seasonality in ocean microbial communities.

PubMed

Giovannoni, Stephen J; Vergin, Kevin L

2012-02-10

Ocean warming occurs every year in seasonal cycles that can help us to understand long-term responses of plankton to climate change. Rhythmic seasonal patterns of microbial community turnover are revealed when high-resolution measurements of microbial plankton diversity are applied to samples collected in lengthy time series. Seasonal cycles in microbial plankton are complex, but the expansion of fixed ocean stations monitoring long-term change and the development of automated instrumentation are providing the time-series data needed to understand how these cycles vary across broad geographical scales. By accumulating data and using predictive modeling, we gain insights into changes that will occur as the ocean surface continues to warm and as the extent and duration of ocean stratification increase. These developments will enable marine scientists to predict changes in geochemical cycles mediated by microbial communities and to gauge their broader impacts.

Decadal variability in the oxygen inventory of North Atlantic Subtropical Underwater captured by sustained, long-term oceanographic time-series observations

NASA Astrophysics Data System (ADS)

Montes, E.; Muller-Karger, F. E.; Cianca, A.; Lomas, M. W.; Lorenzoni, L.; Habtes, S. Y.

2016-02-01

Historical observations of potential temperature (θ), salinity (S), and dissolved oxygen concentrations (O2) in the subtropical North Atlantic (0-500 m; 0-40°N, 10-80°W) were examined to understand decadal-scale changes in O2 in Subtropical Underwater (STUW). STUW is observed at four of the longest, sustained ocean biogeochemical and ecological time-series stations, namely the CARIACO Ocean Time-Series Program (10.5°N, 64.7°W), the Bermuda Atlantic Time-series Study (BATS; 31.7°N, 64.2°W), Hydrostation "S" (32.1°N, 64.4°W), and the European Station for Time-series in the Ocean, Canary Islands (ESTOC; 29.2°N, 15.5°W). Data archived by NOAA NODC show that, between 1980 and 2013, STUW O2 (upper 300 m) has declined 0.58 μmol kg-1 yr-1 in the southeastern Caribbean Sea (10-15°N, 60-70°W), and 0.68 μmol kg-1 yr-1 in the western subtropical North Atlantic, respectively (30-35°N, 60-65°W). Observations at CARIACO (1995-2013) and BATS (1988-2012), specifically, show that STUW O2 has decreased approximately 0.61 and 0.21 μmol kg-1 yr-1, respectively. No apparent change in STUW O2 was observed at ESTOC over the course of the time series (1994-2013). Most of the observed O2 loss seems to result from shifts in ventilation associated with wind-driven mixing and slow down of STUW formation rates, rather than changes in diffusive air-sea O2 gas exchange. Variability of STUW O2 showed a strong relationship with the Atlantic Multidecadal Oscillation (AMO; R2=0.32, p < 0.001) index phase. During negative AMO years trade winds are stronger between 10°N and 30°N. These conditions stimulate the formation and ventilation of STUW. The decreasing trend in STUW O2 in the three decades spanning 1980 through 2013 thus reflects a shift from a strongly negative AMO between mid-1980's and mid-1990's to a positive AMO observed between the mid-1990's and 2013. These changes in STUW O2 were captured by the CARIACO, BATS, and Hydrostation "S" time series stations. Sustained

Dynamic and Regression Modeling of Ocean Variability in the Tide-Gauge Record at Seasonal and Longer Periods

NASA Technical Reports Server (NTRS)

Hill, Emma M.; Ponte, Rui M.; Davis, James L.

2007-01-01

Comparison of monthly mean tide-gauge time series to corresponding model time series based on a static inverted barometer (IB) for pressure-driven fluctuations and a ocean general circulation model (OM) reveals that the combined model successfully reproduces seasonal and interannual changes in relative sea level at many stations. Removal of the OM and IB from the tide-gauge record produces residual time series with a mean global variance reduction of 53%. The OM is mis-scaled for certain regions, and 68% of the residual time series contain a significant seasonal variability after removal of the OM and IB from the tide-gauge data. Including OM admittance parameters and seasonal coefficients in a regression model for each station, with IB also removed, produces residual time series with mean global variance reduction of 71%. Examination of the regional improvement in variance caused by scaling the OM, including seasonal terms, or both, indicates weakness in the model at predicting sea-level variation for constricted ocean regions. The model is particularly effective at reproducing sea-level variation for stations in North America, Europe, and Japan. The RMS residual for many stations in these areas is 25-35 mm. The production of "cleaner" tide-gauge time series, with oceanographic variability removed, is important for future analysis of nonsecular and regionally differing sea-level variations. Understanding the ocean model's strengths and weaknesses will allow for future improvements of the model.

Decadal variability in the oxygen inventory of North Atlantic subtropical underwater captured by sustained, long-term oceanographic time series observations

NASA Astrophysics Data System (ADS)

Montes, Enrique; Muller-Karger, Frank E.; Cianca, Andrés.; Lomas, Michael W.; Lorenzoni, Laura; Habtes, Sennai

2016-03-01

Historical observations of potential temperature (θ), salinity (S), and dissolved oxygen concentrations (O2) in the tropical and subtropical North Atlantic (0-500 m; 0-40°N, 10-90°W) were examined to understand decadal-scale changes in O2 in subtropical underwater (STUW). STUW is observed at four of the longest, sustained ocean biogeochemical and ecological time series stations, namely, the CArbon Retention In A Colored Ocean (CARIACO) Ocean Time Series Program (10.5°N, 64.7°W), the Bermuda Atlantic Time-series Study (BATS; 31.7°N, 64.2°W), Hydrostation "S" (32.1°N, 64.4°W), and the European Station for Time-series in the Ocean, Canary Islands (ESTOC; 29.2°N, 15.5°W). Observations over similar time periods at CARIACO (1996-2013), BATS (1988-2011), and Hydrostation S (1980-2013) show that STUW O2 has decreased approximately 0.71, 0.28, and 0.37 µmol kg-1 yr-1, respectively. No apparent change in STUW O2 was observed at ESTOC over the course of the time series (1994-2013). Ship observation data for the tropical and subtropical North Atlantic archived at NOAA National Oceanographic Data Center show that between 1980 and 2013, STUW O2 (upper ~300 m) declined 0.58 µmol kg-1 yr-1 in the southeastern Caribbean Sea (10-15°N, 60-70°W) and 0.68 µmol kg-1 yr-1 in the western subtropical North Atlantic (30-35°N, 60-65°W). A declining O2 trend was not observed in the eastern subtropical North Atlantic (25-30°N, 15-20°W) over the same period. Most of the observed O2 loss seems to result from shifts in ventilation associated with decreased wind-driven mixing and a slowing down of STUW formation rates, rather than changes in diffusive air-sea O2 gas exchange or changes in the biological oceanography of the North Atlantic. Variability of STUW O2 showed a significant relationship with the wintertime (January-March) Atlantic Multidecadal Oscillation index (AMO, R2 = 0.32). During negative wintertime AMO years trade winds are typically stronger between 10°N and 30

Reconstructing surface ocean circulation with 129I time series records from corals.

PubMed

Chang, Ching-Chih; Burr, George S; Jull, A J Timothy; Russell, Joellen L; Biddulph, Dana; White, Lara; Prouty, Nancy G; Chen, Yue-Gau; Shen, Chuan-Chou; Zhou, Weijian; Lam, Doan Dinh

2016-12-01

The long-lived radionuclide 129 I (half-life: 15.7 × 10 6  yr) is well-known as a useful environmental tracer. At present, the global 129 I in surface water is about 1-2 orders of magnitude higher than pre-1960 levels. Since the 1990s, anthropogenic 129 I produced from industrial nuclear fuels reprocessing plants has been the primary source of 129 I in marine surface waters of the Atlantic and around the globe. Here we present four coral 129 I time series records from: 1) Con Dao and 2) Xisha Islands, the South China Sea, 3) Rabaul, Papua New Guinea and 4) Guam. The Con Dao coral 129 I record features a sudden increase in 129 I in 1959. The Xisha coral shows similar peak values for 129 I as the Con Dao coral, punctuated by distinct low values, likely due to the upwelling in the central South China Sea. The Rabaul coral features much more gradual 129 I increases in the 1970s, similar to a published record from the Solomon Islands. The Guam coral 129 I record contains the largest measured values for any site, with two large peaks, in 1955 and 1959. Nuclear weapons testing was the primary 129 I source in the Western Pacific in the latter part of the 20th Century, notably from testing in the Marshall Islands. The Guam 1955 peak and Con Dao 1959 increases are likely from the 1954 Castle Bravo test, and the Operation Hardtack I test is the most likely source of the 1959 peak observed at Guam. Radiogenic iodine found in coral was carried primarily through surface ocean currents. The coral 129 I time series data provide a broad picture of the surface distribution and depth penetration of 129 I in the Pacific Ocean over the past 60 years. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Understanding Aggregation and Estimating Seasonal Abundance of Chrysaora quinquecirrha Medusae from a Fixed-station Time Series in the Choptank River, Chesapeake Bay

NASA Astrophysics Data System (ADS)

Tay, J.; Hood, R. R.

2016-02-01

Although jellyfish exert strong control over marine plankton dynamics (Richardson et al. 2009, Robison et al. 2014) and negatively impact human commercial and recreational activities (Purcell et al. 2007, Purcell 2012), jellyfish biomass is not well quantified due primarily to sampling difficulties with plankton nets or fisheries trawls (Haddock 2004). As a result, some of the longest records of jellyfish are visual shore-based surveys, such as the fixed-station time series of Chrysaora quinquecirrha that began in 1960 in the Patuxent River in Chesapeake Bay, USA (Cargo and King 1990). Time series counts from fixed-station surveys capture two signals: 1) demographic change at timescales on the order of reproductive processes and 2) spatial patchiness at shorter timescales as different parcels of water move in and out of the survey area by tidal and estuarine advection and turbulent mixing (Lee and McAlice 1979). In this study, our goal was to separate these two signals using a 4-year time series of C. quinquecirrha medusa counts from a fixed-station in the Choptank River, Chesapeake Bay. Idealized modeling of tidal and estuarine advection was used to conceptualize the sampling scheme. Change point and time series analysis was used to detect demographic changes. Indices of aggregation (Negative Binomial coefficient, Taylor's Power Law coefficient, and Morisita's Index) were calculated to describe the spatial patchiness of the medusae. Abundance estimates revealed a bloom cycle that differed in duration and magnitude for each of the study years. Indices of aggregation indicated that medusae were aggregated and that patches grew in the number of individuals, and likely in size, as abundance increased. Further inference from the conceptual modeling suggested that medusae patch structure was generally homogenous over the tidal extent. This study highlights the benefits of using fixed-station shore-based surveys for understanding the biology and ecology of jellyfish.

Direct determination of geocenter motion by combining SLR, VLBI, GNSS, and DORIS time series

NASA Astrophysics Data System (ADS)

Wu, X.; Abbondanza, C.; Altamimi, Z.; Chin, T. M.; Collilieux, X.; Gross, R. S.; Heflin, M. B.; Jiang, Y.; Parker, J. W.

2013-12-01

The longest-wavelength surface mass transport includes three degree-one spherical harmonic components involving hemispherical mass exchanges. The mass load causes geocenter motion between the center-of-mass of the total Earth system (CM) and the center-of-figure of the solid Earth surface (CF), and deforms the solid Earth. Estimation of the degree-1 surface mass changes through CM-CF and degree-1 deformation signatures from space geodetic techniques can thus complement GRACE's time-variable gravity data to form a complete change spectrum up to a high resolution. Currently, SLR is considered the most accurate technique for direct geocenter motion determination. By tracking satellite motion from ground stations, SLR determines the motion between CM and the geometric center of its ground network (CN). This motion is then used to approximate CM-CF and subsequently for deriving degree-1 mass changes. However, the SLR network is very sparse and uneven in global distribution. The average number of operational tracking stations is about 20 in recent years. The poor network geometry can have a large CN-CF motion and is not ideal for the determination of CM-CF motion and degree-1 mass changes. We recently realized an experimental Terrestrial Reference Frame (TRF) through station time series using the Kalman filter and the RTS smoother. The TRF has its origin defined at nearly instantaneous CM using weekly SLR measurement time series. VLBI, GNSS and DORIS time series are combined weekly with those of SLR and tied to the geocentric (CM) reference frame through local tie measurements and co-motion constraints on co-located geodetic stations. The unified geocentric time series of the four geodetic techniques provide a much better network geometry for direct geodetic determination of geocenter motion. Results from this direct approach using a 90-station network compares favorably with those obtained from joint inversions of GPS/GRACE data and ocean bottom pressure models. We will

Observing climate change trends in ocean biogeochemistry: when and where.

PubMed

Henson, Stephanie A; Beaulieu, Claudie; Lampitt, Richard

2016-04-01

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change-driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long-term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long-term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (<~30 years) to detect trends than other areas. In addition, we quantify the 'footprint' of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9-15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change-driven responses in the marine ecosystem. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Data imputation analysis for Cosmic Rays time series

NASA Astrophysics Data System (ADS)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Analyses of Inhomogeneities in Radiosonde Temperature and Humidity Time Series.

NASA Astrophysics Data System (ADS)

Zhai, Panmao; Eskridge, Robert E.

1996-04-01

Twice daily radiosonde data from selected stations in the United States (period 1948 to 1990) and China (period 1958 to 1990) were sorted into time series. These stations have one sounding taken in darkness and the other in sunlight. The analysis shows that the 0000 and 1200 UTC time series are highly correlated. Therefore, the Easterling and Peterson technique was tested on the 0000 and 1200 time series to detect inhomogeneities and to estimate the size of the biases. Discontinuities were detected using the difference series created from the 0000 and 1200 UTC time series. To establish that the detected bias was significant, a t test was performed to confirm that the change occurs in the daytime series but not in the nighttime series.Both U.S. and Chinese radiosonde temperature and humidity data include inhomogeneities caused by changes in radiosonde sensors and observation times. The U.S. humidity data have inhomogeneities that were caused by instrument changes and the censoring of data. The practice of reporting relative humidity as 19% when it is lower than 20% or the temperature is below 40°C is called censoring. This combination of procedural and instrument changes makes the detection of biases and adjustment of the data very difficult. In the Chinese temperatures, them are inhomogeneities related to a change in the radiation correction procedure.Test results demonstrate that a modified Easterling and Peterson method is suitable for use in detecting and adjusting time series radiosonde data.Accurate stations histories are very desirable. Stations histories can confirm that detected inhomogeneities are related to instrument or procedural changes. Adjustments can then he made to the data with some confidence.

Seasonal signatures in SFG vibrational spectra of the sea surface nanolayer at Boknis Eck Time Series Station (SW Baltic Sea)

NASA Astrophysics Data System (ADS)

Laß, K.; Bange, H. W.; Friedrichs, G.

2013-02-01

The very thin sea surface nanolayer on top of the sea surface microlayer, sometimes just one monomolecular layer thick, forms the interface between ocean and atmosphere. Due to the small dimension and tiny amount of substance, knowledge about the development of the layer in the course of the year is scarce. In this work, the sea surface nanolayer at Boknis Eck Time Series Station (BE), southwestern Baltic Sea, has been investigated over a period of three and a half years. Surface water samples were taken monthly by screen sampling and were analyzed in terms of organic content and composition by sum frequency generation spectroscopy, which is specifically sensitive to interfacial layers. A yearly periodicity has been observed with a pronounced abundance of sea surface nanolayer material (such as carbohydrate-rich material) during the summer months. On the basis of our results we conclude that the abundance of organic material in the nanolayer at Boknis Eck is not directly related to phytoplankton abundance. We suggest that indeed sloppy feeding of zooplankton together with photochemical and/or microbial processing of organic precursor compounds are responsible for the pronounced seasonality.

Seasonal signatures in SFG vibrational spectra of the sea surface nanolayer at Boknis Eck Time Series Station (SW Baltic Sea)

NASA Astrophysics Data System (ADS)

Laß, K.; Bange, H. W.; Friedrichs, G.

2013-08-01

The very thin sea surface nanolayer on top of the sea surface microlayer, sometimes just one monomolecular layer thick, forms the interface between ocean and atmosphere. Due to the small dimension and tiny amount of substance, knowledge about the development of the layer in the course of the year is scarce. In this work, the sea surface nanolayer at Boknis Eck Time Series Station (BE), southwestern Baltic Sea, has been investigated over a period of three and a half years. Surface water samples were taken monthly by screen sampling and were analyzed in terms of organic content and composition by sum frequency generation spectroscopy, which is specifically sensitive to interfacial layers. A yearly periodicity has been observed with a pronounced abundance of sea surface nanolayer material (such as carbohydrate-rich material) during the summer months. On the basis of our results we conclude that the abundance of organic material in the nanolayer at Boknis Eck is not directly related to phytoplankton abundance alone. We speculate that indeed sloppy feeding of zooplankton together with photochemical and/or microbial processing of organic precursor compounds is responsible for the pronounced seasonality.

Improving GNSS time series for volcano monitoring: application to Canary Islands (Spain)

NASA Astrophysics Data System (ADS)

García-Cañada, Laura; Sevilla, Miguel J.; Pereda de Pablo, Jorge; Domínguez Cerdeña, Itahiza

2017-04-01

The number of permanent GNSS stations has increased significantly in recent years for different geodetic applications such as volcano monitoring, which require a high precision. Recently we have started to have coordinates time series long enough so that we can apply different analysis and filters that allow us to improve the GNSS coordinates results. Following this idea we have processed data from GNSS permanent stations used by the Spanish Instituto Geográfico Nacional (IGN) for volcano monitoring in Canary Islands to obtained time series by double difference processing method with Bernese v5.0 for the period 2007-2014. We have identified the characteristics of these time series and obtained models to estimate velocities with greater accuracy and more realistic uncertainties. In order to improve the results we have used two kinds of filters to improve the time series. The first, a spatial filter, has been computed using the series of residuals of all stations in the Canary Islands without an anomalous behaviour after removing a linear trend. This allows us to apply this filter to all sets of coordinates of the permanent stations reducing their dispersion. The second filter takes account of the temporal correlation in the coordinate time series for each station individually. A research about the evolution of the velocity depending on the series length has been carried out and it has demonstrated the need for using time series of at least four years. Therefore, in those stations with more than four years of data, we calculated the velocity and the characteristic parameters in order to have time series of residuals. This methodology has been applied to the GNSS data network in El Hierro (Canary Islands) during the 2011-2012 eruption and the subsequent magmatic intrusions (2012-2014). The results show that in the new series it is easier to detect anomalous behaviours in the coordinates, so they are most useful to detect crustal deformations in volcano monitoring.

Ichthyoplankton Time Series: A Potential Ocean Observing Network to Provide Indicators of Climate Impacts on Fish Communities along the West Coast of North America

NASA Astrophysics Data System (ADS)

Koslow, J. A.; Brodeur, R.; Duffy-Anderson, J. T.; Perry, I.; jimenez Rosenberg, S.; Aceves, G.

2016-02-01

Ichthyoplankton time series available from the Bering Sea, Gulf of Alaska and California Current (Oregon to Baja California) provide a potential ocean observing network to assess climate impacts on fish communities along the west coast of North America. Larval fish abundance reflects spawning stock biomass, so these data sets provide indicators of the status of a broad range of exploited and unexploited fish populations. Analyses to date have focused on individual time series, which generally exhibit significant change in relation to climate. Off California, a suite of 24 midwater fish taxa have declined > 60%, correlated with declining midwater oxygen concentrations, and overall larval fish abundance has declined 72% since 1969, a trend based on the decline of predominantly cool-water affinity taxa in response to warming ocean temperatures. Off Oregon, there were dramatic differences in community structure and abundance of larval fishes between warm and cool ocean conditions. Midwater deoxygenation and warming sea surface temperature trends are predicted to continue as a result of global climate change. US, Canadian, and Mexican fishery scientists are now collaborating in a virtual ocean observing network to synthesize available ichthyoplankton time series and compare patterns of change in relation to climate. This will provide regional indicators of populations and groups of taxa sensitive to warming, deoxygenation and potentially other stressors, establish the relevant scales of coherence among sub-regions and across Large Marine Ecosystems, and provide the basis for predicting future climate change impacts on these ecosystems.

Noise analysis of GPS time series in Taiwan

NASA Astrophysics Data System (ADS)

Lee, You-Chia; Chang, Wu-Lung

2017-04-01

Global positioning system (GPS) usually used for researches of plate tectonics and crustal deformation. In most studies, GPS time series considered only time-independent noises (white noise), but time-dependent noises (flicker noise, random walk noise) which were found by nearly twenty years are also important to the precision of data. The rate uncertainties of stations will be underestimated if the GPS time series are assumed only time-independent noise. Therefore studying the noise properties of GPS time series is necessary in order to realize the precision and reliability of velocity estimates. The lengths of our GPS time series are from over 500 stations around Taiwan with time spans longer than 2.5 years up to 20 years. The GPS stations include different monument types such as deep drill braced, roof, metal tripod, and concrete pier, and the most common type in Taiwan is the metal tripod. We investigated the noise properties of continuous GPS time series by using the spectral index and amplitude of the power law noise. During the process we first remove the data outliers, and then estimate linear trend, size of offsets, and seasonal signals, and finally the amplitudes of the power-law and white noise are estimated simultaneously. Our preliminary results show that the noise amplitudes of the north component are smaller than that of the other two components, and the largest amplitudes are in the vertical. We also find that the amplitudes of white noise and power-law noises are positively correlated in three components. Comparisons of noise amplitudes of different monument types in Taiwan reveal that the deep drill braced monuments have smaller data uncertainties and therefore are more stable than other monuments.

Fifty Years of Ocean Observations in the Pacific Northeast

NASA Astrophysics Data System (ADS)

Whitney, Frank; Tortell, Philippe

2006-12-01

Ocean Station Papa, at 50°N, 145°W in the Alaska Gyre (Figure 1), started as a weather station in the 1940s. In 1956, oceanographers began collecting a suite of standard measurements from the cool subarctic waters at Ocean Station Papa (OSP), including temperature, salinity, oxygen, and plankton. Three years later, a series of sampling stations was added along the 1400-kilometer `Line P' from the Canadian coast to OSP, to aid in understanding ocean variability.

Volatility of linear and nonlinear time series

NASA Astrophysics Data System (ADS)

Kalisky, Tomer; Ashkenazy, Yosef; Havlin, Shlomo

2005-07-01

Previous studies indicated that nonlinear properties of Gaussian distributed time series with long-range correlations, ui , can be detected and quantified by studying the correlations in the magnitude series ∣ui∣ , the “volatility.” However, the origin for this empirical observation still remains unclear and the exact relation between the correlations in ui and the correlations in ∣ui∣ is still unknown. Here we develop analytical relations between the scaling exponent of linear series ui and its magnitude series ∣ui∣ . Moreover, we find that nonlinear time series exhibit stronger (or the same) correlations in the magnitude time series compared with linear time series with the same two-point correlations. Based on these results we propose a simple model that generates multifractal time series by explicitly inserting long range correlations in the magnitude series; the nonlinear multifractal time series is generated by multiplying a long-range correlated time series (that represents the magnitude series) with uncorrelated time series [that represents the sign series sgn(ui) ]. We apply our techniques on daily deep ocean temperature records from the equatorial Pacific, the region of the El-Ninõ phenomenon, and find: (i) long-range correlations from several days to several years with 1/f power spectrum, (ii) significant nonlinear behavior as expressed by long-range correlations of the volatility series, and (iii) broad multifractal spectrum.

Ocean time-series reveals recurring seasonal patterns of virioplankton dynamics in the northwestern Sargasso Sea.

PubMed

Parsons, Rachel J; Breitbart, Mya; Lomas, Michael W; Carlson, Craig A

2012-02-01

There are an estimated 10(30) virioplankton in the world oceans, the majority of which are phages (viruses that infect bacteria). Marine phages encompass enormous genetic diversity, affect biogeochemical cycling of elements, and partially control aspects of prokaryotic production and diversity. Despite their importance, there is a paucity of data describing virioplankton distributions over time and depth in oceanic systems. A decade of high-resolution time-series data collected from the upper 300 m in the northwestern Sargasso Sea revealed recurring temporal and vertical patterns of virioplankton abundance in unprecedented detail. An annual virioplankton maximum developed between 60 and 100 m during periods of summer stratification and eroded during winter convective mixing. The timing and vertical positioning of this seasonal pattern was related to variability in water column stability and the dynamics of specific picophytoplankton and heterotrophic bacterioplankton lineages. Between 60 and 100 m, virioplankton abundance was negatively correlated to the dominant heterotrophic bacterioplankton lineage SAR11, as well as the less abundant picophytoplankton, Synechococcus. In contrast, virioplankton abundance was positively correlated to the dominant picophytoplankton lineage Prochlorococcus, and the less abundant alpha-proteobacteria, Rhodobacteraceae. Seasonally, virioplankton abundances were highly synchronous with Prochlorococcus distributions and the virioplankton to Prochlorococcus ratio remained remarkably constant during periods of water column stratification. The data suggest that a significant fraction of viruses in the mid-euphotic zone of the subtropical gyres may be cyanophages and patterns in their abundance are largely determined by Prochlorococcus dynamics in response to water column stability. This high-resolution, decadal survey of virioplankton abundance provides insight into the possible controls of virioplankton dynamics in the open ocean.

Ocean time-series reveals recurring seasonal patterns of virioplankton dynamics in the northwestern Sargasso Sea

PubMed Central

Parsons, Rachel J; Breitbart, Mya; Lomas, Michael W; Carlson, Craig A

2012-01-01

There are an estimated 1030 virioplankton in the world oceans, the majority of which are phages (viruses that infect bacteria). Marine phages encompass enormous genetic diversity, affect biogeochemical cycling of elements, and partially control aspects of prokaryotic production and diversity. Despite their importance, there is a paucity of data describing virioplankton distributions over time and depth in oceanic systems. A decade of high-resolution time-series data collected from the upper 300 m in the northwestern Sargasso Sea revealed recurring temporal and vertical patterns of virioplankton abundance in unprecedented detail. An annual virioplankton maximum developed between 60 and 100 m during periods of summer stratification and eroded during winter convective mixing. The timing and vertical positioning of this seasonal pattern was related to variability in water column stability and the dynamics of specific picophytoplankton and heterotrophic bacterioplankton lineages. Between 60 and 100 m, virioplankton abundance was negatively correlated to the dominant heterotrophic bacterioplankton lineage SAR11, as well as the less abundant picophytoplankton, Synechococcus. In contrast, virioplankton abundance was positively correlated to the dominant picophytoplankton lineage Prochlorococcus, and the less abundant alpha-proteobacteria, Rhodobacteraceae. Seasonally, virioplankton abundances were highly synchronous with Prochlorococcus distributions and the virioplankton to Prochlorococcus ratio remained remarkably constant during periods of water column stratification. The data suggest that a significant fraction of viruses in the mid-euphotic zone of the subtropical gyres may be cyanophages and patterns in their abundance are largely determined by Prochlorococcus dynamics in response to water column stability. This high-resolution, decadal survey of virioplankton abundance provides insight into the possible controls of virioplankton dynamics in the open ocean

Earth's Surface Displacements from the GPS Time Series

NASA Astrophysics Data System (ADS)

Haritonova, D.; Balodis, J.; Janpaule, I.; Morozova, K.

2015-11-01

The GPS observations of both Latvian permanent GNSS networks - EUPOS®-Riga and LatPos, have been collected for a period of 8 years - from 2007 to 2014. Local surface displacements have been derived from the obtained coordinate time series eliminating different impact sources. The Bernese software is used for data processing. The EUREF Permanent Network (EPN) stations in the surroundings of Latvia are selected as fiducial stations. The results have shown a positive tendency of vertical displacements in the western part of Latvia - station heights are increasing, and negative velocities are observed in the central and eastern parts. Station vertical velocities are ranging in diapason of 4 mm/year. In the case of horizontal displacements, site velocities are up to 1 mm/year and mostly oriented to the south. The comparison of the obtained results with data from the deformation model NKG_RF03vel has been made. Additionally, the purpose of this study is to analyse GPS time series obtained using two different data processing strategies: Precise Point Positioning (PPP) and estimation of station coordinates relatively to the positions of fiducial stations also known as Differential GNSS.

Reprocessing the GRACE-derived gravity field time series based on data-driven method for ocean tide alias error mitigation

NASA Astrophysics Data System (ADS)

Liu, Wei; Sneeuw, Nico; Jiang, Weiping

2017-04-01

GRACE mission has contributed greatly to the temporal gravity field monitoring in the past few years. However, ocean tides cause notable alias errors for single-pair spaceborne gravimetry missions like GRACE in two ways. First, undersampling from satellite orbit induces the aliasing of high-frequency tidal signals into the gravity signal. Second, ocean tide models used for de-aliasing in the gravity field retrieval carry errors, which will directly alias into the recovered gravity field. GRACE satellites are in non-repeat orbit, disabling the alias error spectral estimation based on the repeat period. Moreover, the gravity field recovery is conducted in non-strictly monthly interval and has occasional gaps, which result in an unevenly sampled time series. In view of the two aspects above, we investigate the data-driven method to mitigate the ocean tide alias error in a post-processing mode.

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems.

PubMed

Zhang, Yingying; Wang, Juncheng; Vorontsov, A M; Hou, Guangli; Nikanorova, M N; Wang, Hongliang

2014-01-01

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.

Russian State Time and Earth Rotation Service: Observations, Eop Series, Prediction

NASA Astrophysics Data System (ADS)

Kaufman, M.; Pasynok, S.

2010-01-01

Russian State Time, Frequency and Earth Rotation Service provides the official EOP data and time for use in scientific, technical and metrological works in Russia. The observations of GLONASS and GPS on 30 stations in Russia, and also the Russian and worldwide observations data of VLBI (35 stations) and SLR (20 stations) are used now. To these three series of EOP the data calculated in two other Russian analysis centers are added: IAA (VLBI, GPS and SLR series) and MCC (SLR). Joint processing of these 7 series is carried out every day (the operational EOP data for the last day and the predicted values for 50 days). The EOP values are weekly refined and systematic errors of every individual series are corrected. The combined results become accessible on the VNIIFTRI server (ftp.imvp.ru) approximately at 6h UT daily.

Incorporating Satellite Time-Series Data into Modeling

NASA Technical Reports Server (NTRS)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

Multi-Parameter GEOSCOPE Land Stations and Ocean Bottom Observatories

NASA Astrophysics Data System (ADS)

Stutzmann, E.; Roult, G.; Montagner, J.; Karczewski, J.; Geoscope Group,.

2002-12-01

Since the mid-eighties, the number of high quality broadband stations installed over the world has increased in a spectacular way and these stations cover now most of the emerged lands, with a higher concentration in the northern hemisphere between the latitudes 0 and 60N. The GEOSCOPE program has been the first global network of 3-component broadband seismic stations and has contributed to the elaboration of the FDSN. The 25 broadband GEOSCOPE stations fulfill the FDSN criteria, the data are stored in data-centers at the IPGP and IRIS DMC and are accessible through the netdc procedure (email to netdc@ipgp.jussieu.fr) or via the web. GEOSCOPE data have contributed to the major progresses of our knowledge of the earth interior. The earthquake CMT are now determined in a routine manner and the temporal history of the seismic source is accessible for the largest earthquakes. The geographical distribution of GEOSCOPE stations, specially in the southern hemisphere, has also played an important role in improving the resolution of global tomographic models. Recent P-wave models give high resolution images of subducted slabs whereas recent S-wave models have enable to discover the two super-swells. The evolution of the GEOSCOPE program is now focused on the installation of multi-parameter stations including 3-component broadband seismic sensors, pressure gauge, thermometer and GPS receivers. These stations will enable to decrease the seismic noise, and -among others- to better characterize the background free oscillations of the earth. The seismic station earth coverage is limited by the presence of oceans and the next step is the installation of ocean bottom broadband stations. For this purpose, in 1992, a first temporary station sismobs/OFM has been working for 2 weeks, along the mid atlantic ridge. In 1997, an international multi-parameter station, MOISE has been operating during 3 months in the Monterey Bay. The further step is the installation of the station NERO in a

Spatial analysis of precipitation time series over the Upper Indus Basin

NASA Astrophysics Data System (ADS)

Latif, Yasir; Yaoming, Ma; Yaseen, Muhammad

2018-01-01

The upper Indus basin (UIB) holds one of the most substantial river systems in the world, contributing roughly half of the available surface water in Pakistan. This water provides necessary support for agriculture, domestic consumption, and hydropower generation; all critical for a stable economy in Pakistan. This study has identified trends, analyzed variability, and assessed changes in both annual and seasonal precipitation during four time series, identified herein as: (first) 1961-2013, (second) 1971-2013, (third) 1981-2013, and (fourth) 1991-2013, over the UIB. This study investigated spatial characteristics of the precipitation time series over 15 weather stations and provides strong evidence of annual precipitation by determining significant trends at 6 stations (Astore, Chilas, Dir, Drosh, Gupis, and Kakul) out of the 15 studied stations, revealing a significant negative trend during the fourth time series. Our study also showed significantly increased precipitation at Bunji, Chitral, and Skardu, whereas such trends at the rest of the stations appear insignificant. Moreover, our study found that seasonal precipitation decreased at some locations (at a high level of significance), as well as periods of scarce precipitation during all four seasons. The observed decreases in precipitation appear stronger and more significant in autumn; having 10 stations exhibiting decreasing precipitation during the fourth time series, with respect to time and space. Furthermore, the observed decreases in precipitation appear robust and more significant for regions at high elevation (>1300 m). This analysis concludes that decreasing precipitation dominated the UIB, both temporally and spatially including in the higher areas.

A new methodological approach for worldwide beryllium-7 time series analysis

NASA Astrophysics Data System (ADS)

Bianchi, Stefano; Longo, Alessandro; Plastino, Wolfango

2018-07-01

Time series analyses of cosmogenic radionuclide 7Be and 22Na atmospheric activity concentrations and meteorological data observed at twenty-five International Monitoring System (IMS) stations of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) have shown great variability in terms of noise structures, harmonic content, cross-correlation patterns and local Hurst exponent behaviour. Noise content and its structure has been extracted and characterised for the two radionuclides time series. It has been found that the yearly component, which is present in most of the time series, is not stationary, but has a percentage weight that varies with time. Analysis of atmospheric activity concentrations of 7Be, measured at IMS stations, has shown them to be influenced by distinct meteorological patterns, mainly by atmospheric pressure and temperature.

Oceanic Loading and Local Distortions at the Baksan, Russia, and Gran Sasso, Italy, Strain Stations

NASA Astrophysics Data System (ADS)

Milyukov, V. K.; Amoruso, A.; Crescentini, L.; Mironov, A. P.; Myasnikov, A. V.; Lagutkina, A. V.

2018-03-01

Reliable use of strain data in geophysical studies requires their preliminary correction for ocean loading and various local distortions. These effects, in turn, can be estimated from the tidal records which are contributed by solid and oceanic loading. In this work, we estimate the oceanic tidal loading at two European strain stations (Baksan, Russia, and Gran Sasso, Italy) by analyzing the results obtained with the different Earth and ocean models. The influence of local distortions on the strain measurements at the two stations is estimated.

Ocean sea-ice modelling in the Southern Ocean around Indian Antarctic stations

NASA Astrophysics Data System (ADS)

Kumar, Anurag; Dwivedi, Suneet; Rajak, D. Ram

2017-07-01

An eddy-resolving coupled ocean sea-ice modelling is carried out in the Southern Ocean region (9°-78°E; 51°-71°S) using the MITgcm. The model domain incorporates the Indian Antarctic stations, Maitri (11.7{°}E; 70.7{°}S) and Bharati (76.1{°}E; 69.4{°}S). The realistic simulation of the surface variables, namely, sea surface temperature (SST), sea surface salinity (SSS), surface currents, sea ice concentration (SIC) and sea ice thickness (SIT) is presented for the period of 1997-2012. The horizontal resolution of the model varies between 6 and 10 km. The highest vertical resolution of 5 m is taken near the surface, which gradually increases with increasing depths. The seasonal variability of the SST, SSS, SIC and currents is compared with the available observations in the region of study. It is found that the SIC of the model domain is increasing at a rate of 0.09% per month (nearly 1% per year), whereas, the SIC near Maitri and Bharati regions is increasing at a rate of 0.14 and 0.03% per month, respectively. The variability of the drift of the sea-ice is also estimated over the period of simulation. It is also found that the sea ice volume of the region increases at the rate of 0.0004 km3 per month (nearly 0.005 km3 per year). Further, it is revealed that the accumulation of sea ice around Bharati station is more as compared to Maitri station.

Analysis of Site Position Time Series Derived From Space Geodetic Solutions

NASA Astrophysics Data System (ADS)

Angermann, D.; Meisel, B.; Kruegel, M.; Tesmer, V.; Miller, R.; Drewes, H.

2003-12-01

This presentation deals with the analysis of station coordinate time series obtained from VLBI, SLR, GPS and DORIS solutions. We also present time series for the origin and scale derived from these solutions and discuss their contribution to the realization of the terrestrial reference frame. For these investigations we used SLR and VLBI solutions computed at DGFI with the software systems DOGS (SLR) and OCCAM (VLBI). The GPS and DORIS time series were obtained from weekly station coordinates solutions provided by the IGS, and from the joint DORIS analysis center (IGN-JPL). We analysed the time series with respect to various aspects, such as non-linear motions, periodic signals and systematic differences (biases). A major focus is on a comparison of the results at co-location sites in order to identify technique- and/or solution related problems. This may also help to separate and quantify possible effects, and to understand the origin of still existing discrepancies. Technique-related systematic effects (biases) should be reduced to the highest possible extent, before using the space geodetic solutions for a geophysical interpretation of seasonal signals in site position time series.

Buoy monitors ocean acidification

NASA Astrophysics Data System (ADS)

Zielinski, Sarah

2007-06-01

A new Gulf of Alaska buoy installed on 7 June is the first to provide data that will help scientists study ocean acidification caused by the absorption of atmospheric carbon dioxide. Sensors attached to the buoy are measuring key climate indicators in the atmosphere and ocean, including surface acidity and the air-sea exchange of carbon dioxide. The buoy was installed in collaboration with the Line P program, which has provided decades of continuous measurements from a series of oceanographic stations along line P which extends from the mouth of the Juan de Fuca Strait south of Vancouver Island to Pacific Ocean Station Papa, where the new buoy was installed.The buoy is part of a project conducted by scientists from NOAAs Pacific Marine Environmental Laboratory; the University of Washington, Seattle; Fisheries and Oceans Canada; and the Institute of Ocean Sciences in Sydney, British Columbia.

33 CFR 165.552 - Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey. 165.552 Section 165.552 Navigation and Navigable... Coast Guard District § 165.552 Security Zone; Oyster Creek Generation Station, Forked River, Ocean...

33 CFR 165.552 - Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey. 165.552 Section 165.552 Navigation and Navigable... Coast Guard District § 165.552 Security Zone; Oyster Creek Generation Station, Forked River, Ocean...

33 CFR 165.552 - Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey. 165.552 Section 165.552 Navigation and Navigable... Coast Guard District § 165.552 Security Zone; Oyster Creek Generation Station, Forked River, Ocean...

33 CFR 165.552 - Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey. 165.552 Section 165.552 Navigation and Navigable... Coast Guard District § 165.552 Security Zone; Oyster Creek Generation Station, Forked River, Ocean...

33 CFR 165.552 - Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false Security Zone; Oyster Creek Generation Station, Forked River, Ocean County, New Jersey. 165.552 Section 165.552 Navigation and Navigable... Coast Guard District § 165.552 Security Zone; Oyster Creek Generation Station, Forked River, Ocean...

Analysis of Vlbi, Slr and GPS Site Position Time Series

NASA Astrophysics Data System (ADS)

Angermann, D.; Krügel, M.; Meisel, B.; Müller, H.; Tesmer, V.

Conventionally the IERS terrestrial reference frame (ITRF) is realized by the adoption of a set of epoch coordinates and linear velocities for a set of global tracking stations. Due to the remarkable progress of the space geodetic observation techniques (e.g. VLBI, SLR, GPS) the accuracy and consistency of the ITRF increased continuously. The accuracy achieved today is mainly limited by technique-related systematic errors, which are often poorly characterized or quantified. Therefore it is essential to analyze the individual techniques' solutions with respect to systematic differences, models, parameters, datum definition, etc. Main subject of this presentation is the analysis of GPS, SLR and VLBI time series of site positions. The investigations are based on SLR and VLBI solutions computed at DGFI with the software systems DOGS (SLR) and OCCAM (VLBI). The GPS time series are based on weekly IGS station coordinates solutions. We analyze the time series with respect to the issues mentioned above. In particular we characterize the noise in the time series, identify periodic signals, and investigate non-linear effects that complicate the assignment of linear velocities for global tracking sites. One important aspect is the comparison of results obtained by different techniques at colocation sites.

An approach to constructing a homogeneous time series of soil mositure using SMOS

USDA-ARS?s Scientific Manuscript database

Overlapping soil moisture time series derived from two satellite microwave radiometers (SMOS, Soil Moisture and Ocean Salinity; AMSR-E, Advanced Microwave Scanning Radiometer - Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies f...

Analysis of Zenith Tropospheric Delay above Europe based on long time series derived from the EPN data

NASA Astrophysics Data System (ADS)

Baldysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; Kroszczynski, Krzysztof; Araszkiewicz, Andrzej

2015-04-01

In recent years, the GNSS system began to play an increasingly important role in the research related to the climate monitoring. Based on the GPS system, which has the longest operational capability in comparison with other systems, and a common computational strategy applied to all observations, long and homogeneous ZTD (Zenith Tropospheric Delay) time series were derived. This paper presents results of analysis of 16-year ZTD time series obtained from the EPN (EUREF Permanent Network) reprocessing performed by the Military University of Technology. To maintain the uniformity of data, analyzed period of time (1998-2013) is exactly the same for all stations - observations carried out before 1998 were removed from time series and observations processed using different strategy were recalculated according to the MUT LAC approach. For all 16-year time series (59 stations) Lomb-Scargle periodograms were created to obtain information about the oscillations in ZTD time series. Due to strong annual oscillations which disturb the character of oscillations with smaller amplitude and thus hinder their investigation, Lomb-Scargle periodograms for time series with the deleted annual oscillations were created in order to verify presence of semi-annual, ter-annual and quarto-annual oscillations. Linear trend and seasonal components were estimated using LSE (Least Square Estimation) and Mann-Kendall trend test were used to confirm the presence of linear trend designated by LSE method. In order to verify the effect of the length of time series on the estimated size of the linear trend, comparison between two different length of ZTD time series was performed. To carry out a comparative analysis, 30 stations which have been operating since 1996 were selected. For these stations two periods of time were analyzed: shortened 16-year (1998-2013) and full 18-year (1996-2013). For some stations an additional two years of observations have significant impact on changing the size of linear

Ocean Warming of Petermann Fjord and Glacier, North Greenland

NASA Astrophysics Data System (ADS)

Muenchow, A.; Washam, P.; Padman, L.; Nicholls, K. W.

2016-02-01

Petermann Fjord connects one of the largest floating ice shelves of Greenland to Nares Strait between northern Canada and Greenland. First ocean temperatures under the ice shelf and in the fjord were recorded in 2002 and 2003, respectively. Last observations were taken in August of 2015 as part of an interdisciplinary experiment of US, Swedish, and British scientists. The new ocean data include hydrographic sections along and across the 450-m deep sill at the entrance of the fjord, sections along and across the 200-m thick terminus of the glacier, and time series from three ocean-weather stations that collect ocean temperature, salinity, and pressure data from under the ice shelf of Petermann Gletscher in near real time. Our ocean data cover the entire 2002-2015 time period when we find statistically significant changes of ocean properties in space and time. The ocean under the ice shelf connects to ambient Nares Strait and to the grounding zone of the glacier at daily to weekly time scales via temperature and salinity correlation. More specifically, we find 1. substantial and significant ocean warming of deep fjord waters at Interannual time scales, 2. intense and rapid renewal of bottom waters inside the 1000-m deep fjord, and 3. large fluctuations of temperature and salinity within about 30-m of the glacier ice-ocean interface at daily to weekly time scales. Figure: Map of the study area with 2015 locations of CTD casts (blue and green dots), ocean-weather stations (green dots), and differential GPS (red triangles). Red contours are bottom depths at 500 and 1000-m while thick black line indicates the grounding zone where the glacier connects to the bed rock below.

Estimated SLR station position and network frame sensitivity to time-varying gravity

NASA Astrophysics Data System (ADS)

Zelensky, Nikita P.; Lemoine, Frank G.; Chinn, Douglas S.; Melachroinos, Stavros; Beckley, Brian D.; Beall, Jennifer Wiser; Bordyugov, Oleg

2014-06-01

This paper evaluates the sensitivity of ITRF2008-based satellite laser ranging (SLR) station positions estimated weekly using LAGEOS-1/2 data from 1993 to 2012 to non-tidal time-varying gravity (TVG). Two primary methods for modeling TVG from degree-2 are employed. The operational approach applies an annual GRACE-derived field, and IERS recommended linear rates for five coefficients. The experimental approach uses low-order/degree coefficients estimated weekly from SLR and DORIS processing of up to 11 satellites (tvg4x4). This study shows that the LAGEOS-1/2 orbits and the weekly station solutions are sensitive to more detailed modeling of TVG than prescribed in the current IERS standards. Over 1993-2012 tvg4x4 improves SLR residuals by 18 % and shows 10 % RMS improvement in station stability. Tests suggest that the improved stability of the tvg4x4 POD solution frame may help clarify geophysical signals present in the estimated station position time series. The signals include linear and seasonal station motion, and motion of the TRF origin, particularly in Z. The effect on both POD and the station solutions becomes increasingly evident starting in 2006. Over 2008-2012, the tvg4x4 series improves SLR residuals by 29 %. Use of the GRGS RL02 series shows similar improvement in POD. Using tvg4x4, secular changes in the TRF origin Z component double over the last decade and although not conclusive, it is consistent with increased geocenter rate expected due to continental ice melt. The test results indicate that accurate modeling of TVG is necessary for improvement of station position estimation using SLR data.

Shifting nitrous oxide source/sink behaviour in a subtropical estuary revealed by automated time series observations

NASA Astrophysics Data System (ADS)

Reading, Michael J.; Santos, Isaac R.; Maher, Damien T.; Jeffrey, Luke C.; Tait, Douglas R.

2017-07-01

The oceans are a major source of the potent greenhouse gas nitrous oxide (N2O) to the atmosphere. However, little information is available on how estuaries and the coastal ocean may contribute to N2O budgets, and on the drivers of N2O in aquatic environments. This study utilised five time series stations along the freshwater to marine continuum in a sub-tropical estuary in Australia (Coffs Creek, Australia). Each time series station captured N2O, radon (222Rn, a natural submarine groundwater discharge tracer), dissolved nitrogen, and dissolved organic carbon (DOC) concentrations for a minimum of 25 h. The use of automated time series observations enabled spatial and tidal-scale variability of N2O to be captured. Groundwater was highly enriched in N2O (up to 306 nM) compared to the receiving surface water. Dissolved N2O supersaturation as high as 386% (27.4 nM) was observed in the upstream freshwater and brackish water areas which represented only a small (∼13%) proportion of the total estuary area. A large area of N2O undersaturation (as low as 53% or 3.9 nM) was observed in the mangrove-dominated lower estuary. This undersaturated area likely resulted from N2O consumption due to nitrate/nitrite (NOx) limitation in mangrove sediments subject to shallow porewater exchange. Overall, the estuary was a minor source of N2O to the atmosphere as the lower mangrove-dominated estuary sink of N2O counteracted groundwater-dominated source of N2O in the upper estuary. Average area-weighted N2O fluxes at the water-air interface approached zero (0.2-0.7 μmol m-2 d-1, depending on piston velocity model used), and were much lower than nitrogen-rich Northern Hemisphere estuaries that are considered large sources of N2O to the atmosphere. This study revealed a temporally and spatially diverse estuary, with areas of N2O production and consumption related to oxygen and total dissolved nitrogen availability, submarine groundwater discharge, and uptake within mangroves.

Seaglider surveys at Ocean Station Papa: Circulation and water mass properties in a meander of the North Pacific Current

NASA Astrophysics Data System (ADS)

Pelland, Noel A.; Eriksen, Charles C.; Cronin, Meghan F.

2016-09-01

A Seaglider autonomous underwater vehicle augmented the Ocean Station Papa (OSP; 50°N, 145°W) surface mooring, measuring spatial structure on scales relevant to the monthly evolution of the moored time series. During each of three missions from June 2008 to January 2010, a Seaglider made biweekly 50 km × 50 km surveys in a bowtie-shaped survey track. Horizontal temperature and salinity gradients measured by these surveys were an order of magnitude stronger than climatological values and sometimes of opposite sign. Geostrophically inferred circulation was corroborated by moored acoustic Doppler current profiler measurements and AVISO satellite altimetry estimates of surface currents, confirming that glider surveys accurately resolved monthly scale mesoscale spatial structure. In contrast to climatological North Pacific Current circulation, upper-ocean flow was modestly northward during the first half of the 18 month survey period, and weakly westward during its latter half, with Rossby number O>(0.01>). This change in circulation coincided with a shift from cool and fresh to warm, saline, oxygen-rich water in the upper-ocean halocline, and an increase in vertical fine structure there and in the lower pycnocline. The anomalous flow and abrupt water mass transition were due to the slow growth of an anticyclonic meander within the North Pacific Current with radius comparable to the scale of the survey pattern, originating to the southeast of OSP.

KALREF—A Kalman filter and time series approach to the International Terrestrial Reference Frame realization

NASA Astrophysics Data System (ADS)

Wu, Xiaoping; Abbondanza, Claudio; Altamimi, Zuheir; Chin, T. Mike; Collilieux, Xavier; Gross, Richard S.; Heflin, Michael B.; Jiang, Yan; Parker, Jay W.

2015-05-01

The current International Terrestrial Reference Frame is based on a piecewise linear site motion model and realized by reference epoch coordinates and velocities for a global set of stations. Although linear motions due to tectonic plates and glacial isostatic adjustment dominate geodetic signals, at today's millimeter precisions, nonlinear motions due to earthquakes, volcanic activities, ice mass losses, sea level rise, hydrological changes, and other processes become significant. Monitoring these (sometimes rapid) changes desires consistent and precise realization of the terrestrial reference frame (TRF) quasi-instantaneously. Here, we use a Kalman filter and smoother approach to combine time series from four space geodetic techniques to realize an experimental TRF through weekly time series of geocentric coordinates. In addition to secular, periodic, and stochastic components for station coordinates, the Kalman filter state variables also include daily Earth orientation parameters and transformation parameters from input data frames to the combined TRF. Local tie measurements among colocated stations are used at their known or nominal epochs of observation, with comotion constraints applied to almost all colocated stations. The filter/smoother approach unifies different geodetic time series in a single geocentric frame. Fragmented and multitechnique tracking records at colocation sites are bridged together to form longer and coherent motion time series. While the time series approach to TRF reflects the reality of a changing Earth more closely than the linear approximation model, the filter/smoother is computationally powerful and flexible to facilitate incorporation of other data types and more advanced characterization of stochastic behavior of geodetic time series.

Time series models of decadal trends in the harmful algal species Karlodinium veneficum in Chesapeake Bay.

PubMed

Lin, Chih-Hsien Michelle; Lyubchich, Vyacheslav; Glibert, Patricia M

2018-03-01

The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time. Here, applying cross-correlations of climate-related physical factors, nutrients and prey, with abundance of K. veneficum over a 10-year (2002-2011) period, a synthesis of the interactive effects of multiple factors on this species was developed for Chesapeake Bay, where blooms of the HAB have been increasing. Significant upward trends in the time series of K. veneficum were observed in the mesohaline stations of the Bay, but not in oligohaline tributary stations. For the mesohaline regions, riverine sources of nutrients with seasonal lags, together with particulate prey with zero lag, explained 15%-46% of the variation in the K. veneficum time series. For the oligohaline regions, nutrients and particulate prey generally showed significant decreasing trends with time, likely a reflection of nutrient reduction efforts. A conceptual model of mid-Bay blooms is presented, in which K. veneficum, derived from the oceanic end member of the Bay, may experience enhanced growth if it encounters prey originating from the tributaries with different patterns of nutrient loading and which are enriched in nitrogen. For all correlation models developed herein, prey abundance was a primary factor in predicting K. veneficum abundance. Copyright © 2018 Elsevier B.V. All rights reserved.

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

NASA Astrophysics Data System (ADS)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

The local properties of ocean surface waves by the phase-time method

NASA Technical Reports Server (NTRS)

Huang, Norden E.; Long, Steven R.; Tung, Chi-Chao; Donelan, Mark A.; Yuan, Yeli; Lai, Ronald J.

1992-01-01

A new approach using phase information to view and study the properties of frequency modulation, wave group structures, and wave breaking is presented. The method is applied to ocean wave time series data and a new type of wave group (containing the large 'rogue' waves) is identified. The method also has the capability of broad applications in the analysis of time series data in general.

90Sr, 137Cs and (239,240)Pu concentration surface water time series in the Pacific and Indian Oceans--WOMARS results.

PubMed

Povinec, Pavel P; Aarkrog, Asker; Buesseler, Ken O; Delfanti, Roberta; Hirose, Katsumi; Hong, Gi Hoon; Ito, Toshimichi; Livingston, Hugh D; Nies, Hartmut; Noshkin, Victor E; Shima, Shigeki; Togawa, Orihiko

2005-01-01

Under an IAEA's Co-ordinated Research Project "Worldwide Marine Radioactivity Studies (WOMARS)" 90Sr, 137Cs and (239,240)Pu concentration surface water time series in the Pacific and Indian Oceans have been investigated. The Pacific and Indian Oceans were divided into 17 latitudinal boxes according to ocean circulation, global fallout patterns and the location of nuclear weapons test sites. The present levels and time trends in radionuclide concentrations in surface water for each box were studied and the corresponding effective half-lives were estimated. For the year 2000, the estimated average 90Sr, 137Cs and (239,240)Pu concentrations in surface waters of the Pacific and Indian Oceans varied from 0.1 to 1.5 mBq/L, 0.1 to 2.8 mBq/L, and 0.1 to 5.2 microBq/L, respectively. The mean effective half-lives for 90Sr and 137Cs in surface water were 12+/-1 years for the North, 20+/-1 years for the South and 21+/-2 years for the Equatorial Pacific. For (239,240)Pu the corresponding mean effective half-lives were 7+/-1 years for the North, 12+/-4 years for the South and 10+/-2 years for the Equatorial Pacific. For the Indian Ocean the mean effective half-lives of 137Cs and (239,240)Pu were 21+/-2 years and 9+/-1 years, respectively. There is evidence that fallout removal rates before 1970 were faster than those observed during recent decades. The estimated surface water concentrations of 90Sr, 137Cs and (239,240)Pu in latitudinal belts of the Pacific and Indian Oceans for the year 2000 may be used as the average levels so that any new contribution from nuclear facilities, nuclear weapons test sites, radioactive waste dumping sites and from possible nuclear accidents can be identified.

Noise characteristics in DORIS station positions time series derived from IGN-JPL, INASAN and CNES-CLS analysis centres

NASA Astrophysics Data System (ADS)

Khelifa, S.

2014-12-01

Using wavelet transform and Allan variance, we have analysed the solutions of weekly position residuals of 09 high latitude DORIS stations in STCD (STation Coordinate Difference) format provided from the three Analysis Centres : IGN-JPL (solution ign11wd01), INASAN (solution ina10wd01) and CNES-CLS (solution lca11wd02), in order to compare the spectral characteristics of their residual noise. The temporal correlations between the three solutions, two by two and station by station, for each component (North, East and Vertical) reveal a high correlation in the horizontal components (North and East). For the North component, the correlation average is about 0.88, 0.81 and 0.79 between, respectively, IGN-INA, IGN-LCA and INA-LCA solutions, then for the East component it is about 0.84, 0.82 and 0.76, respectively. However, the correlations for the Vertical component are moderate with an average of 0.64, 0.57 and 0.58 in, respectively, IGN-INA, IGN-LCA and INA-LCA solutions. After removing the trends and seasonal components from the analysed time series, the Allan variance analysis shows that the three solutions are dominated by a white noise in the all three components (North, East and Vertical). The wavelet transform analysis, using the VisuShrink method with soft thresholding, reveals that the noise level in the LCA solution is less important compared to IGN and INA solutions. Indeed, the standard deviation of the noise for the three components is in the range of 5-11, 5-12 and 4-9mm in the IGN, INA, and LCA solutions, respectively.

Time-Domain Simulation of Along-Track Interferometric SAR for Moving Ocean Surfaces.

PubMed

Yoshida, Takero; Rheem, Chang-Kyu

2015-06-10

A time-domain simulation of along-track interferometric synthetic aperture radar (AT-InSAR) has been developed to support ocean observations. The simulation is in the time domain and based on Bragg scattering to be applicable for moving ocean surfaces. The time-domain simulation is suitable for examining velocities of moving objects. The simulation obtains the time series of microwave backscattering as raw signals for movements of ocean surfaces. In terms of realizing Bragg scattering, the computational grid elements for generating the numerical ocean surface are set to be smaller than the wavelength of the Bragg resonant wave. In this paper, the simulation was conducted for a Bragg resonant wave and irregular waves with currents. As a result, the phases of the received signals from two antennas differ due to the movement of the numerical ocean surfaces. The phase differences shifted by currents were in good agreement with the theoretical values. Therefore, the adaptability of the simulation to observe velocities of ocean surfaces with AT-InSAR was confirmed.

Time-Domain Simulation of Along-Track Interferometric SAR for Moving Ocean Surfaces

PubMed Central

Yoshida, Takero; Rheem, Chang-Kyu

2015-01-01

A time-domain simulation of along-track interferometric synthetic aperture radar (AT-InSAR) has been developed to support ocean observations. The simulation is in the time domain and based on Bragg scattering to be applicable for moving ocean surfaces. The time-domain simulation is suitable for examining velocities of moving objects. The simulation obtains the time series of microwave backscattering as raw signals for movements of ocean surfaces. In terms of realizing Bragg scattering, the computational grid elements for generating the numerical ocean surface are set to be smaller than the wavelength of the Bragg resonant wave. In this paper, the simulation was conducted for a Bragg resonant wave and irregular waves with currents. As a result, the phases of the received signals from two antennas differ due to the movement of the numerical ocean surfaces. The phase differences shifted by currents were in good agreement with the theoretical values. Therefore, the adaptability of the simulation to observe velocities of ocean surfaces with AT-InSAR was confirmed. PMID:26067197

Evidence for a physical linkage between galactic cosmic rays and regional climate time series

USGS Publications Warehouse

Perry, C.A.

2007-01-01

The effects of solar variability on regional climate time series were examined using a sequence of physical connections between total solar irradiance (TSI) modulated by galactic cosmic rays (GCRs), and ocean and atmospheric patterns that affect precipitation and streamflow. The solar energy reaching the Earth's surface and its oceans is thought to be controlled through an interaction between TSI and GCRs, which are theorized to ionize the atmosphere and increase cloud formation and its resultant albedo. High (low) GCR flux may promote cloudiness (clear skies) and higher (lower) albedo at the same time that TSI is lowest (highest) in the solar cycle which in turn creates cooler (warmer) ocean temperature anomalies. These anomalies have been shown to affect atmospheric flow patterns and ultimately affect precipitation over the Midwestern United States. This investigation identified a relation among TSI and geomagnetic index aa (GI-AA), and streamflow in the Mississippi River Basin for the period 1878-2004. The GI-AA was used as a proxy for GCRs. The lag time between the solar signal and streamflow in the Mississippi River at St. Louis, Missouri is approximately 34 years. The current drought (1999-2007) in the Mississippi River Basin appears to be caused by a period of lower solar activity that occurred between 1963 and 1977. There appears to be a solar "fingerprint" that can be detected in climatic time series in other regions of the world, with each series having a unique lag time between the solar signal and the hydroclimatic response. A progression of increasing lag times can be spatially linked to the ocean conveyor belt, which may transport the solar signal over a time span of several decades. The lag times for any one region vary slightly and may be linked to the fluctuations in the velocity of the ocean conveyor belt.

Annual net community production and the biological carbon flux in the ocean

NASA Astrophysics Data System (ADS)

Emerson, Steven

2014-01-01

The flux of biologically produced organic matter from the surface ocean (the biological pump), over an annual cycle, is equal to the annual net community production (ANCP). Experimental determinations of ANCP at ocean time series sites using a variety of different metabolite mass balances have made it possible to evaluate the accuracy of sediment trap fluxes and satellite-determined ocean carbon export. ANCP values at the Hawaii Ocean Time-series (HOT), the Bermuda Atlantic Time-series Study (BATS), Ocean Station Papa (OSP) are 3 ± 1 mol C m-2 yr-1—much less variable than presently suggested by satellite remote sensing measurements and global circulation models. ANCP determined from mass balances at these locations are 3-4 times particulate organic carbon fluxes measured in sediment traps. When the roles of dissolved organic carbon (DOC) flux, zooplankton migration, and depth-dependent respiration are considered these differences are reconciled at HOT and OSP but not at BATS, where measured particulate fluxes are about 3 times lower than expected. Even in the cases where sediment trap fluxes are accurate, it is not possible to "scale up" these measurements to determine ANCP without independent determinations of geographically variable DOC flux and zooplankton migration. Estimates of ANCP from satellite remote sensing using net primary production determined by the carbon-based productivity model suggests less geographic variability than its predecessor (the vertically generalized productivity model) and brings predictions at HOT and OSP closer to measurements; however, satellite-predicted ANCP at BATS is still 3 times too low.

Aurora Australis over the southern Indian ocean view taken by the Expedition 29 crew

NASA Image and Video Library

2011-09-17

ISS029-E-005904 (17 Sept. 2011) --- This is one of a series of night time images photographed by one of the Expedition 29 crew members from the International Space Station. It features Aurora Australis over the southern Indian ocean. Nadir coordinates are 50.16 south latitude and 48.11 degrees east longitude.

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.

PubMed

Malkin, Zinovy

2016-04-01

The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series.

Characterizing rainfall of hot arid region by using time-series modeling and sustainability approaches: a case study from Gujarat, India

NASA Astrophysics Data System (ADS)

Machiwal, Deepesh; Kumar, Sanjay; Dayal, Devi

2016-05-01

This study aimed at characterization of rainfall dynamics in a hot arid region of Gujarat, India by employing time-series modeling techniques and sustainability approach. Five characteristics, i.e., normality, stationarity, homogeneity, presence/absence of trend, and persistence of 34-year (1980-2013) period annual rainfall time series of ten stations were identified/detected by applying multiple parametric and non-parametric statistical tests. Furthermore, the study involves novelty of proposing sustainability concept for evaluating rainfall time series and demonstrated the concept, for the first time, by identifying the most sustainable rainfall series following reliability ( R y), resilience ( R e), and vulnerability ( V y) approach. Box-whisker plots, normal probability plots, and histograms indicated that the annual rainfall of Mandvi and Dayapar stations is relatively more positively skewed and non-normal compared with that of other stations, which is due to the presence of severe outlier and extreme. Results of Shapiro-Wilk test and Lilliefors test revealed that annual rainfall series of all stations significantly deviated from normal distribution. Two parametric t tests and the non-parametric Mann-Whitney test indicated significant non-stationarity in annual rainfall of Rapar station, where the rainfall was also found to be non-homogeneous based on the results of four parametric homogeneity tests. Four trend tests indicated significantly increasing rainfall trends at Rapar and Gandhidham stations. The autocorrelation analysis suggested the presence of persistence of statistically significant nature in rainfall series of Bhachau (3-year time lag), Mundra (1- and 9-year time lag), Nakhatrana (9-year time lag), and Rapar (3- and 4-year time lag). Results of sustainability approach indicated that annual rainfall of Mundra and Naliya stations ( R y = 0.50 and 0.44; R e = 0.47 and 0.47; V y = 0.49 and 0.46, respectively) are the most sustainable and dependable

Algorithm for Compressing Time-Series Data

NASA Technical Reports Server (NTRS)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Cycles, scaling and crossover phenomenon in length of the day (LOD) time series

NASA Astrophysics Data System (ADS)

Telesca, Luciano

2007-06-01

The dynamics of the temporal fluctuations of the length of the day (LOD) time series from January 1, 1962 to November 2, 2006 were investigated. The power spectrum of the whole time series has revealed annual, semi-annual, decadal and daily oscillatory behaviors, correlated with oceanic-atmospheric processes and interactions. The scaling behavior was analyzed by using the detrended fluctuation analysis (DFA), which has revealed two different scaling regimes, separated by a crossover timescale at approximately 23 days. Flicker-noise process can describe the dynamics of the LOD time regime involving intermediate and long timescales, while Brownian dynamics characterizes the LOD time series for small timescales.

Long-term changes (1980-2003) in total ozone time series over Northern Hemisphere midlatitudes

NASA Astrophysics Data System (ADS)

Białek, Małgorzata

2006-03-01

Long-term changes in total ozone time series for Arosa, Belsk, Boulder and Sapporo stations are examined. For each station we analyze time series of the following statistical characteristics of the distribution of daily ozone data: seasonal mean, standard deviation, maximum and minimum of total daily ozone values for all seasons. The iterative statistical model is proposed to estimate trends and long-term changes in the statistical distribution of the daily total ozone data. The trends are calculated for the period 1980-2003. We observe lessening of negative trends in the seasonal means as compared to those calculated by WMO for 1980-2000. We discuss a possibility of a change of the distribution shape of ozone daily data using the Kolmogorov-Smirnov test and comparing trend values in the seasonal mean, standard deviation, maximum and minimum time series for the selected stations and seasons. The distribution shift toward lower values without a change in the distribution shape is suggested with the following exceptions: the spreading of the distribution toward lower values for Belsk during winter and no decisive result for Sapporo and Boulder in summer.

Ocean wavenumber estimation from wave-resolving time series imagery

USGS Publications Warehouse

Plant, N.G.; Holland, K.T.; Haller, M.C.

2008-01-01

We review several approaches that have been used to estimate ocean surface gravity wavenumbers from wave-resolving remotely sensed image sequences. Two fundamentally different approaches that utilize these data exist. A power spectral density approach identifies wavenumbers where image intensity variance is maximized. Alternatively, a cross-spectral correlation approach identifies wavenumbers where intensity coherence is maximized. We develop a solution to the latter approach based on a tomographic analysis that utilizes a nonlinear inverse method. The solution is tolerant to noise and other forms of sampling deficiency and can be applied to arbitrary sampling patterns, as well as to full-frame imagery. The solution includes error predictions that can be used for data retrieval quality control and for evaluating sample designs. A quantitative analysis of the intrinsic resolution of the method indicates that the cross-spectral correlation fitting improves resolution by a factor of about ten times as compared to the power spectral density fitting approach. The resolution analysis also provides a rule of thumb for nearshore bathymetry retrievals-short-scale cross-shore patterns may be resolved if they are about ten times longer than the average water depth over the pattern. This guidance can be applied to sample design to constrain both the sensor array (image resolution) and the analysis array (tomographic resolution). ?? 2008 IEEE.

Intercomparison of Satellite Derived Gravity Time Series with Inferred Gravity Time Series from TOPEX/POSEIDON Sea Surface Heights and Climatological Model Output

NASA Technical Reports Server (NTRS)

Cox, C.; Au, A.; Klosko, S.; Chao, B.; Smith, David E. (Technical Monitor)

2001-01-01

The upcoming GRACE mission promises to open a window on details of the global mass budget that will have remarkable clarity, but it will not directly answer the question of what the state of the Earth's mass budget is over the critical last quarter of the 20th century. To address that problem we must draw upon existing technologies such as SLR, DORIS, and GPS, and climate modeling runs in order to improve our understanding. Analysis of long-period geopotential changes based on SLR and DORIS tracking has shown that addition of post 1996 satellite tracking data has a significant impact on the recovered zonal rates and long-period tides. Interannual effects such as those causing the post 1996 anomalies must be better characterized before refined estimates of the decadal period changes in the geopotential can be derived from the historical database of satellite tracking. A possible cause of this anomaly is variations in ocean mass distribution, perhaps associated with the recent large El Nino/La Nina. In this study, a low-degree spherical harmonic gravity time series derived from satellite tracking is compared with a TOPEX/POSEIDON-derived sea surface height time series. Corrections for atmospheric mass effects, continental hydrology, snowfall accumulation, and ocean steric model predictions will be considered.

Atmospheric deposition fluxes and solubility over the Southern Indian Ocean: time series on Kerguelen and Crozet Islands.

NASA Astrophysics Data System (ADS)

Heimburger, Alexie; Losno, Remi; Triquet, Sylvain; Bon Nguyen, Elisabeth

2013-04-01

Atmospheric supplies bringing trace metals are suspected to have a significant impact on biogeochemical processes in High-Nutrient-Low-Chlorophyll waters of the open ocean, such as the Southern Ocean. We recorded time series of atmospheric deposition samples continuously collected over two years on Kerguelen and Crozet Islands in the Southern Indian Ocean. Dust deposition flux and scavenging ratio on Kerguelen Islands were reported in a previous publication [Heimburger et al., GBC, 2012]. Here, we present results of total atmospheric deposition fluxes for a large suite of elements (Al, As, Ba, Ca, Ce, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Nd, Ni, Pb including isotopes, Rb, S, Si, Sr, Ti, U, V, Zn), which are identified as coming either from sea-salt, crustal or anthropogenic sources. Enrichment factor variabilities for Pb, As, Cr, Cu and V and Pb isotopic ratios highlight the anthropogenic contribution during the austral winter only. For Al, Fe, Mn and Si, deposition fluxes are similar for both Kerguelen and Crozet Islands, which are situated 1300 km apart, and so can be extrapolated for the entire Southern Indian Ocean. Over the entire sampling period, those fluxes are on average equal to 53 ± 2 µg/m²/d, 33 ± 1 µg/m²/d, 0.83 ± 0.04 µg/m²/d and 88 ± 14 µg/m²/d respectively. For the other non-sea-salt elements, we observed differences between flux values from a factor of two to a factor of five with a decreasing gradient from Crozet to Kerguelen Islands. One-month field experiments were also performed during four different austral summers in order to collect rain water on an event basis. Soluble and insoluble fractions were directly separated by filtration and analysed using High Resolution - Inductively Coupled Plasma - Mass Spectrometry and Inductively Coupled Plasma-Atomic Emission Spectrometry as done for deposition samples. Concentrations in rain water samples are very low and difficult to measure accurately mainly because of possible contamination

Global Autocorrelation Scales of the Partial Pressure of Oceanic CO2

NASA Technical Reports Server (NTRS)

Li, Zhen; Adamec, David; Takahashi, Taro; Sutherland, Stewart C.

2004-01-01

A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters (pCO2) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of pCO2. For example, the global maximum of the distance at which autocorrelations exceed 0.8 averages about 140 km in the equatorial Pacific. Also, the lag distance at which the autocorrelation exceed 0.8 is greater in the vicinity of the Gulf Stream than it is near the Kuroshio, approximately 50 km near the Gulf Stream as opposed to 20 km near the Kuroshio. Separate calculations for times when the sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The pCO2 measurements at Ocean Weather Station (OWS) 'P', in the eastern subarctic Pacific (50 N, 145 W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS 'P', is highly variable. A spectral analysis of the longest four pCO2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3-6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between pCO2 and atmospheric or ocean processes problematic.

Stratus 12 Twelfth Setting of the Stratus Ocean Reference Station

DTIC Science & Technology

2012-10-01

22 - June 4, 2012 Valparaiso, Chile - Galapagos Islands, Ecuador by Sebastien Bigorre,1 Robert A. Weller,1 Jeff Lord,1 Nancy Galbraith,1 Sean Whelan,1...Institution, Woods Hole, MA 2 Universidad de Concepción, Chile 3 FICOLAB, Universidad de Concepción, Chile 4 National observer, Ecuador 5...Ocean Reference Station at 20°S, 85°W under the stratus clouds west of northern Chile is being maintained to provide ongoing climate-quality records

Tide Gauge Records Reveal Improved Processing of Gravity Recovery and Climate Experiment Time-Variable Mass Solutions over the Coastal Ocean

NASA Astrophysics Data System (ADS)

Piecuch, Christopher G.; Landerer, Felix W.; Ponte, Rui M.

2018-05-01

Monthly ocean bottom pressure solutions from the Gravity Recovery and Climate Experiment (GRACE), derived using surface spherical cap mass concentration (MC) blocks and spherical harmonics (SH) basis functions, are compared to tide gauge (TG) monthly averaged sea level data over 2003-2015 to evaluate improved gravimetric data processing methods near the coast. MC solutions can explain ≳ 42% of the monthly variance in TG time series over broad shelf regions and in semi-enclosed marginal seas. MC solutions also generally explain ˜5-32 % more TG data variance than SH estimates. Applying a coastline resolution improvement algorithm in the GRACE data processing leads to ˜ 31% more variance in TG records explained by the MC solution on average compared to not using this algorithm. Synthetic observations sampled from an ocean general circulation model exhibit similar patterns of correspondence between modeled TG and MC time series and differences between MC and SH time series in terms of their relationship with TG time series, suggesting that observational results here are generally consistent with expectations from ocean dynamics. This work demonstrates the improved quality of recent MC solutions compared to earlier SH estimates over the coastal ocean, and suggests that the MC solutions could be a useful tool for understanding contemporary coastal sea level variability and change.

Long-Term Observations of Ocean Biogeochemistry with Nitrate and Oxygen Sensors in Apex Profiling Floats

NASA Astrophysics Data System (ADS)

Johnson, K. S.; Coletti, L.; Jannasch, H.; Martz, T.; Swift, D.; Riser, S.

2008-12-01

Long-term, autonomous observations of ocean biogeochemical cycles are now feasible with chemical sensors in profiling floats. These sensors will enable decadal-scale observations of trends in global ocean biogeochemical cycles. Here, we focus on measurements on nitrate and dissolved oxygen. The ISUS (In Situ Ultraviolet Spectrophotometer) optical nitrate sensor has been adapted to operate in a Webb Research, Apex profiling float. The Apex float is of the type used in the Argo array and is designed for multi-year, expendable deployments in the ocean. Floats park at 1000 m depth and make 60 nitrate and oxygen measurements at depth intervals ranging from 50 m below 400 m to 5 m in the upper 100 m as they profile to the surface. All data are transmitted to shore using the Iridium telemetry system and they are available on the Internet in near-real time. Floats equipped with ISUS and an Aanderaa oxygen sensor are capable of making 280 vertical profiles from 1000 m. At a 5 day cycle time, the floats should have nearly a four year endurance. Three floats have now been deployed at the Hawaii Ocean Time series station (HOT), Ocean Station Papa (OSP) in the Gulf of Alaska and at 50 South, 30 East in the Southern Ocean. Two additional floats are designated for deployment at the Bermuda Atlantic Time Series station (BATS) and in the Drake Passage. The HOT float has made 56 profiles over 260 days and should continue operating for 3 more years. Nitrate concentrations are in excellent agreement with the long-term mean observed at HOT. No significant long-term drift in sensor response has occurred. A variety of features have been observed in the HOT nitrate data that are linked to contemporaneous changes in oxygen production and mesoscale dynamics. The impacts of these features will be briefly described. The Southern Ocean float has operated for 200 days and is now observing reinjection of nitrate into surface waters as winter mixing occurs(surface nitrate > 24 micromolar). We

The Hyperspectral Imager for the Coastal Ocean (HICO): Four Years Operating on the International Space Station (Invited)

NASA Astrophysics Data System (ADS)

Davis, C. O.; Nahorniak, J.; Tufillaro, N.; Kappus, M.

2013-12-01

The Hyperspectral Imager for the Coastal Ocean (HICO) is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO images selected coastal regions at 92 m spatial resolution with full spectral coverage (88 channels covering 400 to 900 nm) and a high signal-to-noise ratio to resolve the complexity of the coastal ocean. Under sponsorship of the Office of Naval Research, HICO was built by the Naval Research Laboratory, which continues to operate the sensor. HICO has been operating on the International Space Station since October 2009 and has collected over 8000 scenes for more than 50 users. As Project Scientist I have been the link to the international ocean optics community primarily through our OSU HICO website (http://hico.oregonstate.edu). HICO operations are now under NASA support and HICO data is now also be available through the NASA Ocean Color Website (http://oceancolor.gsfc.nasa.gov ). Here we give a brief overview of HICO data and operations and discuss the unique challenges and opportunities that come from operating on the International Space Station.

Coastal Atmosphere and Sea Time Series (CoASTS)

NASA Technical Reports Server (NTRS)

Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); Zibordi, Giuseppe; Berthon, Jean-Francoise; Doyle, John P.; Grossi, Stefania; vanderLinde, Dirk; Targa, Cristina; Alberotanza, Luigi; McClain, Charles R. (Technical Monitor)

2002-01-01

The Coastal Atmosphere and Sea Time Series (CoASTS) Project aimed at supporting ocean color research and applications, from 1995 up to the time of publication of this document, has ensured the collection of a comprehensive atmospheric and marine data set from an oceanographic tower located in the northern Adriatic Sea. The instruments and the measurement methodologies used to gather quantities relevant for bio-optical modeling and for the calibration and validation of ocean color sensors, are described. Particular emphasis is placed on four items: (1) the evaluation of perturbation effects in radiometric data (i.e., tower-shading, instrument self-shading, and bottom effects); (2) the intercomparison of seawater absorption coefficients from in situ measurements and from laboratory spectrometric analysis on discrete samples; (3) the intercomparison of two filter techniques for in vivo measurement of particulate absorption coefficients; and (4) the analysis of repeatability and reproducibility of the most relevant laboratory measurements carried out on seawater samples (i.e., particulate and yellow substance absorption coefficients, and pigment and total suspended matter concentrations). Sample data are also presented and discussed to illustrate the typical features characterizing the CoASTS measurement site in view of supporting the suitability of the CoASTS data set for bio-optical modeling and ocean color calibration and validation.

Precise comparisons of bottom-pressure and altimetric ocean tides

NASA Astrophysics Data System (ADS)

Ray, R. D.

2013-09-01

A new set of pelagic tide determinations is constructed from seafloor pressure measurements obtained at 151 sites in the deep ocean. To maximize precision of estimated tides, only stations with long time series are used; median time series length is 567 days. Geographical coverage is considerably improved by use of the international tsunami network, but coverage in the Indian Ocean and South Pacific is still weak. As a tool for assessing global ocean tide models, the data set is considerably more reliable than older data sets: the root-mean-square difference with a recent altimetric tide model is approximately 5 mm for the M2 constituent. Precision is sufficiently high to allow secondary effects in altimetric and bottom-pressure tide differences to be studied. The atmospheric tide in bottom pressure is clearly detected at the S1, S2, and T2 frequencies. The altimetric tide model is improved if satellite altimetry is corrected for crustal loading by the atmospheric tide. Models of the solid body tide can also be constrained. The free core-nutation effect in the K1 Love number is easily detected, but the overall estimates are not as accurate as a recent determination with very long baseline interferometry.

Precise Comparisons of Bottom-Pressure and Altimetric Ocean Tides

NASA Technical Reports Server (NTRS)

Ray, Richard D.

2013-01-01

A new set of pelagic tide determinations is constructed from seafloor pressure measurements obtained at 151 sites in the deep ocean. To maximize precision of estimated tides, only stations with long time series are used; median time series length is 567 days. Geographical coverage is considerably improved by use of the international tsunami network, but coverage in the Indian Ocean and South Pacific is still weak. As a tool for assessing global ocean tide models, the data set is considerably more reliable than older data sets : the root-mean-square difference with a recent altimetric tide model is approximately 5 mm for the M2 constituent. Precision is sufficiently high to allow secondary effects in altimetric and bottom-pressure tide differences to be studied. The atmospheric tide in bottom pressure is clearly detected at the S1, S2, and T2 frequencies. The altimetric tide model is improved if satellite altimetry is corrected for crustal loading by the atmospheric tide. Models of the solid body tide can also be constrained. The free corenutation effect in the K1 Love number is easily detected, but the overall estimates are not as accurate as a recent determination with very long baseline interferometry.

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

PubMed

Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

Real-time test of MOCS algorithm during Superflux 1980. [ocean color algorithm for remotely detecting suspended solids

NASA Technical Reports Server (NTRS)

Grew, G. W.

1981-01-01

A remote sensing experiment was conducted in which success depended upon the real-time use of an algorithm, generated from MOCS (multichannel ocean color sensor) data onboard the NASA P-3 aircraft, to direct the NOAA ship Kelez to oceanic stations where vitally needed sea truth could be collected. Remote data sets collected on two consecutive days of the mission were consistent with the sea truth for low concentrations of chlorophyll a. Two oceanic regions of special interest were located. The algorithm and the collected data are described.

The Blue Planet: Seas & Oceans. Young Discovery Library Series.

ERIC Educational Resources Information Center

de Beauregard, Diane Costa

This book is written for children ages 5 through 10. Part of a series designed to develop their curiosity, facinate them and educate them, this volume explores the physical and environmental characteristics of the world's oceans. Topics are: (1) human exploration; (2) the food chain; (3) coral reefs; (4) currents and tides; (5) waves; (6)…

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

One-Dimensional Coupled Ecosystem-Carbon Flux Model for the Simulation of Biogeochemical Parameters at Ocean Weather Station P

NASA Technical Reports Server (NTRS)

Signorini, S.; McClain, C.; Christian, J.; Wong, C. S.

2000-01-01

In this Technical Publication, we describe the model functionality and analyze its application to the seasonal and interannual variations of phytoplankton, nutrients, pCO2 and CO2 concentrations in the eastern subarctic Pacific at Ocean Weather Station P (OWSP, 50 deg. N 145 deg. W). We use a verified one-dimensional ecosystem model, coupled with newly incorporated carbon flux and carbon chemistry components, to simulate 22 years (1958-1980) of pCO2 and CO2 variability at Ocean Weather Station P (OWS P). This relatively long period of simulation verifies and extends the findings of previous studies using an explicit approach for the biological component and realistic coupling with the carbon flux dynamics. The slow currents and the horizontally homogeneous ocean in the subarctic Pacific make OWS P one of the best available candidates for modeling the chemistry of the upper ocean in one dimension. The chlorophyll and ocean currents composite for 1998 illustrates this premise. The chlorophyll concentration map was derived from SeaWiFS data and the currents are from an OGCM simulation (from R. Murtugudde).

GNSS station displacement analysis

NASA Astrophysics Data System (ADS)

Haritonova, Diana; Balodis, Janis; Janpaule, Inese; Normand, Madara

2013-04-01

Time series of GNSS station results of both the EUPOS®-Riga and LatPos networks have been developed at the Institute of Geodesy and Geoinformation (University of Latvia). The reference stations from EUREF Permanent Network (EPN) in surroundings of Latvia have been used and Bernese GPS Software, Version 5.0, in both static and kinematic modes was applied. The standard data sets were taken from IGS data base. The results of time series have been analysed and distinctive behaviour of daily and subdaily movements of EUPOS®-Riga and LatPos stations was identified. The reasons of dependence of GNSS station coordinate distribution on possible external factors such as seismic activity of some areas of Latvia and periodic processes were given.

Using a numerical model to understand the connection between the ocean and acoustic travel-time measurements.

PubMed

Powell, Brian S; Kerry, Colette G; Cornuelle, Bruce D

2013-10-01

Measurements of acoustic ray travel-times in the ocean provide synoptic integrals of the ocean state between source and receiver. It is known that the ray travel-time is sensitive to variations in the ocean at the transmission time, but the sensitivity of the travel-time to spatial variations in the ocean prior to the acoustic transmission have not been quantified. This study examines the sensitivity of ray travel-time to the temporally and spatially evolving ocean state in the Philippine Sea using the adjoint of a numerical model. A one year series of five day backward integrations of the adjoint model quantify the sensitivity of travel-times to varying dynamics that can alter the travel-time of a 611 km ray by 200 ms. The early evolution of the sensitivities reveals high-mode internal waves that dissipate quickly, leaving the lowest three modes, providing a connection to variations in the internal tide generation prior to the sample time. They are also strongly sensitive to advective effects that alter density along the ray path. These sensitivities reveal how travel-time measurements are affected by both nearby and distant waters. Temporal nonlinearity of the sensitivities suggests that prior knowledge of the ocean state is necessary to exploit the travel-time observations.

Mobile Visualization and Analysis Tools for Spatial Time-Series Data

NASA Astrophysics Data System (ADS)

Eberle, J.; Hüttich, C.; Schmullius, C.

2013-12-01

The Siberian Earth System Science Cluster (SIB-ESS-C) provides access and analysis services for spatial time-series data build on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and climate data from meteorological stations. Until now a webportal for data access, visualization and analysis with standard-compliant web services was developed for SIB-ESS-C. As a further enhancement a mobile app was developed to provide an easy access to these time-series data for field campaigns. The app sends the current position from the GPS receiver and a specific dataset (like land surface temperature or vegetation indices) - selected by the user - to our SIB-ESS-C web service and gets the requested time-series data for the identified pixel back in real-time. The data is then being plotted directly in the app. Furthermore the user has possibilities to analyze the time-series data for breaking points and other phenological values. These processings are executed on demand of the user on our SIB-ESS-C web server and results are transfered to the app. Any processing can also be done at the SIB-ESS-C webportal. The aim of this work is to make spatial time-series data and analysis functions available for end users without the need of data processing. In this presentation the author gives an overview on this new mobile app, the functionalities, the technical infrastructure as well as technological issues (how the app was developed, our made experiences).

Investigation of Noises in GPS Time Series: Case Study on Epn Weekly Solutions

NASA Astrophysics Data System (ADS)

Klos, Anna; Bogusz, Janusz; Figurski, Mariusz; Kosek, Wieslaw; Gruszczynski, Maciej

2014-05-01

The noises in GPS time series are stated to be described the best by the combination of white (Gaussian) and power-law processes. They are mainly the effect of mismodelled satellite orbits, Earth orientation parameters, atmospheric effects, antennae phase centre effects, or of monument instability. Due to the fact, that velocities of permanent stations define the kinematic reference frame, they have to fulfil the requirement of being stable at 0.1 mm/yr. The previously performed researches showed, that the wrong assumption of noise model leads to the underestimation of velocities and their uncertainties from 2 up to even 11, especially in the Up direction. This presentation focuses on more than 200 EPN (EUREF Permanent Network) stations from the area of Europe with various monument types (concrete pillars, buildings, metal masts, with or without domes, placed on the ground or on the rock) and coordinates of weekly changes (GPS weeks 0834-1459). The topocentric components (North, East, Up) in ITRF2005 which come from the EPN Re-Processing made by the Military University of Technology Local Analysis Centre (MUT LAC) were processed with Maximum Likelihood Estimation (MLE) using CATS software. We have assumed the existence of few combinations of noise models (these are: white, flicker and random walk noise with integer spectral indices and power-law noise models with fractional spectral indices) and investigated which of them EPN weekly time series are likely to follow. The results show, that noises in GPS time series are described the best by the combination of white and flicker noise model. It is strictly related to the so-called common mode error (CME) that is spatially correlated error being one of the dominant error source in GPS solutions. We have assumed CME as spatially uniform, what was a good approximation for stations located hundreds of kilometres one to another. Its removal with spatial filtering reduces the amplitudes of white and flicker noise by a

GPS Position Time Series @ JPL

NASA Technical Reports Server (NTRS)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Gravity effects obtained from global hydrology models in comparison with high precision gravimetric time series

NASA Astrophysics Data System (ADS)

Wziontek, Hartmut; Wilmes, Herbert; Güntner, Andreas; Creutzfeldt, Benjamin

2010-05-01

Water mass changes are a major source of variations in residual gravimetric time series obtained from the combination of observations with superconducting and absolute gravimeters. Changes in the local water storage are the main influence, but global variations contribute to the signal significantly. For three European gravity stations, Bad Homburg, Wettzell and Medicina, different global hydrology models are compared. The influence of topographic effects is discussed and due to the long-term stability of the combined gravity time series, inter-annual signals in model data and gravimetric observations are compared. Two sources of influence are discriminated, i.e., the effect of a local zone with an extent of a few kilometers around the gravimetric station and the global contribution beyond 50km. Considering their coarse resolution and uncertainties, local effects calculated from global hydrological models are compared with the in-situ gravity observations and, for the station Wettzell, with local hydrological monitoring data.

The CARIACO Ocean Time-Series: two decades of oceanographic observations to understand linkages between biogeochemistry, ecology, and long-term environmental variability.

NASA Astrophysics Data System (ADS)

Lorenzoni, L.; Muller-Karger, F. E.; Rueda-Roa, D. T.; Thunell, R.; Scranton, M. I.; Taylor, G. T.; benitez-Nelson, C. R.; Montes, E.; Astor, Y. M.; Rojas, J.

2016-02-01

The CARIACO Ocean Time-Series project, located in the Cariaco Basin off the coast of Venezuela, seeks to understand relationships between hydrography, primary production, community composition, microbial activity, particle fluxes, and element cycling in the water column, and how variations in these processes are preserved in sediments accumulating in this anoxic basin. CARIACO uses autonomous and shipboard measurements to understand ecological and biogeochemical changes and how these relate to regional and global climatic/ocean variability. CARIACO is a model for national ocean observing programs in Central/South America, and has been developed as a community facility platform with open access to all data (http://imars.marine.usf.edu/cariaco). Research resulting from this program has contributed to knowledge about the decomposition and cycling of particles, the biological pump, and to our understanding of the ecology and oceanography of oxygen minimum zones. Despite this basin being anoxic below 250m, remineralization rates of organic matter are comparable to those in well oxygenated waters. A dynamic microbial community significantly influences carbon and nutrient biogeochemical cycling throughout the water column. Since 1995, declining particulate organic carbon fluxes have been measured throughout the water column using sediment traps, likely in response to declining Chl-a concentrations and smaller phytoplankton which have replaced the larger taxa over the past decade. This community shift appears to be caused by regional changes in the physical regime. CARIACO also recorded marked long-term changes in surface and deep DIC in response to a combination of factors including surface water warming. The observations of CARIACO highlight the importance of a sustained, holistic approach to studying biodiversity, ecology and the marine carbon cycle to predict potential impacts of climate change on the ocean's ecosystem services and carbon sequestration efficiency.

33 CFR 334.500 - St. Johns River, Atlantic Ocean, Sherman Creek; restricted areas and danger zone, Naval Station...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false St. Johns River, Atlantic Ocean... AND RESTRICTED AREA REGULATIONS § 334.500 St. Johns River, Atlantic Ocean, Sherman Creek; restricted areas and danger zone, Naval Station Mayport, Florida. (a) The areas. (1) The St. Johns River restricted...

Recent Transport History of Fukushima Radioactivity in the Northeast Pacific Ocean.

PubMed

Smith, John N; Rossi, Vincent; Buesseler, Ken O; Cullen, Jay T; Cornett, Jack; Nelson, Richard; Macdonald, Alison M; Robert, Marie; Kellogg, Jonathan

2017-09-19

The large inventory of radioactivity released during the March, 2011 Fukushima Dai-ichi nuclear reactor accident in Japan spread rapidly across the North Pacific Ocean and was first observed at the westernmost station on Line P, an oceanographic sampling line extending 1500 km westward of British Columbia (BC), Canada in June 2012. Here, time series measurements of 134 Cs and 137 Cs in seawater on Line P and on the CLIVAR-P16N 152°W line reveal the recent transport history of the Fukushima radioactivity tracer plume through the northeast Pacific Ocean. During 2013 and 2014 the Fukushima plume spread onto the Canadian continental shelf and by 2015 and early 2016 it reached 137 Cs values of 6-8 Bq/m 3 in surface water along Line P. Ocean circulation model simulations that are consistent with the time series measurements of Fukushima 137 Cs indicate that the 2015-2016 results represent maximum tracer levels on Line P and that they will begin to decline in 2017-2018. The current elevated Fukushima 137 Cs levels in seawater in the eastern North Pacific are equivalent to fallout background levels of 137 Cs that prevailed during the 1970s and do not represent a radiological threat to human health or the environment.

Highly comparative time-series analysis: the empirical structure of time series and their methods.

PubMed

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Highly comparative time-series analysis: the empirical structure of time series and their methods

PubMed Central

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.

2013-01-01

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344

Analysis of Seasonal Signal in GPS Short-Baseline Time Series

NASA Astrophysics Data System (ADS)

Wang, Kaihua; Jiang, Weiping; Chen, Hua; An, Xiangdong; Zhou, Xiaohui; Yuan, Peng; Chen, Qusen

2018-04-01

Proper modeling of seasonal signals and their quantitative analysis are of interest in geoscience applications, which are based on position time series of permanent GPS stations. Seasonal signals in GPS short-baseline (< 2 km) time series, if they exist, are mainly related to site-specific effects, such as thermal expansion of the monument (TEM). However, only part of the seasonal signal can be explained by known factors due to the limited data span, the GPS processing strategy and/or the adoption of an imperfect TEM model. In this paper, to better understand the seasonal signal in GPS short-baseline time series, we adopted and processed six different short-baselines with data span that varies from 2 to 14 years and baseline length that varies from 6 to 1100 m. To avoid seasonal signals that are overwhelmed by noise, each of the station pairs is chosen with significant differences in their height (> 5 m) or type of the monument. For comparison, we also processed an approximately zero baseline with a distance of < 1 m and identical monuments. The daily solutions show that there are apparent annual signals with annual amplitude of 1 mm (maximum amplitude of 1.86 ± 0.17 mm) on almost all of the components, which are consistent with the results from previous studies. Semi-annual signal with a maximum amplitude of 0.97 ± 0.25 mm is also present. The analysis of time-correlated noise indicates that instead of flicker (FL) or random walk (RW) noise, band-pass-filtered (BP) noise is valid for approximately 40% of the baseline components, and another 20% of the components can be best modeled by a combination of the first-order Gauss-Markov (FOGM) process plus white noise (WN). The TEM displacements are then modeled by considering the monument height of the building structure beneath the GPS antenna. The median contributions of TEM to the annual amplitude in the vertical direction are 84% and 46% with and without additional parts of the monument, respectively. Obvious

Estimating trends in atmospheric water vapor and temperature time series over Germany

NASA Astrophysics Data System (ADS)

Alshawaf, Fadwa; Balidakis, Kyriakos; Dick, Galina; Heise, Stefan; Wickert, Jens

2017-08-01

Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil-Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between -1.5 and 2.3 mm decade-1 with standard deviations below 0.25 mm decade-1. These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade-1 with standard errors below 0.03 mm decade-1. The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the Clausius

The MOBB experiment: A prototype permanent off-shore ocean bottom broadband station

NASA Astrophysics Data System (ADS)

Romanowicz, Barbara; Stakes, Debra; Uhrhammer, Robert; McGill, Paul; Neuhauser, Doug; Ramirez, Tony; Dolenc, David

Technical accomplishments of the past 10 years in the design and deployment of sea floor broadband seismic systems are now making it possible to start addressing the issue of the limited coverage of the Earth that can be achieved through land-based installations, at the regional or global scale. In particular, the September 2002 Ocean Mantle Dynamics (OMD) workshop in Snowbird, Utah [OMD Workshop Committee, 2003] proposed the development of two “leap-frogging arrays” of about 30 broadband sea floor instruments to fill geophysically important target holes in ocean coverage for deployment periods of 1 to 2 years. The rationale for an off-shore (“Webfoot”) component of the SArray/Earth-scope “Bigfoot” array was also highlighted at this meeting, pointing out that the study of the North American continent should not stop at the ocean margin.The ocean floor environment is challenging for broadband seismology for several reasons. Broadband seismometers cannot be simply “dropped off” a ship with the expectation that they will produce useable data, particularly on the horizontal components. Several pilot experiments, [e.g., Montagner et al., 1994; OSN1, 1998; Suyehiro et al., 2002] have addressed the issue of optimal installation of ocean bottom stations, and in particular, have carried out comparisons between borehole, sea floor, and buried sea floor installations.

Monthly Surface Air Temperature Time Series Area-Averaged Over the 30-Degree Latitudinal Belts of the Globe

DOE Data Explorer

Lugina, K. M. [Department of Geography, St. Petersburg State University, St. Petersburg, Russia; Groisman, P. Ya. [National Climatic Data Center, Asheville, North Carolina USA); Vinnikov, K. Ya. [Department of Atmospheric Sciences, University of Maryland, College Park, Maryland (USA); Koknaeva, V. V. [State Hydrological Institute, St. Petersburg, Russia; Speranskaya, N. A. [State Hydrological Institute, St. Petersburg, Russia

2006-01-01

The mean monthly and annual values of surface air temperature compiled by Lugina et al. have been taken mainly from the World Weather Records, Monthly Climatic Data for the World, and Meteorological Data for Individual Years over the Northern Hemisphere Excluding the USSR. These published records were supplemented with information from different national publications. In the original archive, after removal of station records believed to be nonhomogeneous or biased, 301 and 265 stations were used to determine the mean temperature for the Northern and Southern hemispheres, respectively. The new version of the station temperature archive (used for evaluation of the zonally-averaged temperatures) was created in 1995. The change to the archive was required because data from some stations became unavailable for analyses in the 1990s. During this process, special care was taken to secure homogeneity of zonally averaged time series. When a station (or a group of stations) stopped reporting, a "new" station (or group of stations) was selected in the same region, and its data for the past 50 years were collected and added to the archive. The processing (area-averaging) was organized in such a way that each time series from a new station spans the reference period (1951-1975) and the years thereafter. It was determined that the addition of the new stations had essentially no effect on the zonally-averaged values for the pre-1990 period.

First Indications of Intraplate Deformations in Central Germany from Reprocessed GNSS Time Series and Geological Data

NASA Astrophysics Data System (ADS)

Becker, Matthias; Leinen, Stefan; Läufer, Gwendolyn; Lehné, Rouwen

2013-04-01

Six years of GPS data have been reprocessed in ITRF2008 for a regional SAPOS CORS network in the federal state of Hesse with 25 stations and some anchor sites of IGS and EPN to derive accurate and consistent coordinate time series. Based on daily network solutions coordinate time series parameters like velocities, offsets in case of antenna changes and annual periodic variation have been estimated. The estimation process includes the fitting of a sophisticated stochastic model for the time series which accounts for inherent time correlation. The results are blended with geological data to verify information from geology on potential recent deformations by the geodetic analyses. Besides of some information on the reprocessing of the GNSS the results the stochastics of the derived velocity field will be discussed in detail. Special emphasis will be on the intra-plate deformation: for the horizontal component the residual velocity field after removal of a plate rotation model is presented, while for the vertical velocities the datum-induced systematic effect is removed in order to analyze the remaining vertical motion. The residual velocity field is then matched with the geology for Hesse. Correlation of both vertical and horizontal movements with major geological structures reveals good accordance. SAPOS stations with documented significant subsidence are mainly located in tertiary Graben structures such as the Lower Hessian Basin (station Kassel), the Wetterau (station Kloppenheim) or the Upper Rhine Graben (Station Darmstadt). From the geological point of view these structures are supposed to be subsiding ones. Other major geological features, i.e. the Rhenish Shield as well as the East Hessian Bunter massif are supposed to be affected by recent uplift. SAPOS stations located in these regions match the assumed movement (e.g. Weilburg, Wiesbaden, Bingen, Fulda). Furthermore SAPOS-derived horizontal movements seem to trace tectonic movements in the region, i

Submerged AUV Charging Station

NASA Technical Reports Server (NTRS)

Jones, Jack A.; Chao, Yi; Curtin, Thomas

2014-01-01

Autonomous Underwater Vehicles (AUVs) are becoming increasingly important for military surveillance and mine detection. Most AUVs are battery powered and have limited lifetimes of a few days to a few weeks. This greatly limits the distance that AUVs can travel underwater. Using a series of submerged AUV charging stations, AUVs could travel a limited distance to the next charging station, recharge its batteries, and continue to the next charging station, thus traveling great distances in a relatively short time, similar to the Old West “Pony Express.” One solution is to use temperature differences at various depths in the ocean to produce electricity, which is then stored in a submerged battery. It is preferred to have the upper buoy submerged a reasonable distance below the surface, so as not to be seen from above and not to be inadvertently destroyed by storms or ocean going vessels. In a previous invention, a phase change material (PCM) is melted (expanded) at warm temperatures, for example, 15 °C, and frozen (contracted) at cooler temperatures, for example, 8 °C. Tubes containing the PCM, which could be paraffin such as pentadecane, would be inserted into a container filled with hydraulic oil. When the PCM is melted (expanded), it pushes the oil out into a container that is pressurized to about 3,000 psi (approx equals 20.7 MPa). When a valve is opened, the high-pressure oil passes through a hydraulic motor, which turns a generator and charges a battery. The low-pressure oil is finally reabsorbed into the PCM canister when the PCM tubes are frozen (contracted). Some of the electricity produced could be used to control an external bladder or a motor to the tether line, such that depth cycling is continued for a very long period of time. Alternatively, after the electricity is generated by the hydraulic motor, the exiting low-pressure oil from the hydraulic motor could be vented directly to an external bladder on the AUV, such that filling of the bladder

Ocean Wireless Networking and Real Time Data Management

NASA Astrophysics Data System (ADS)

Berger, J.; Orcutt, J. A.; Vernon, F. L.; Braun, H. W.; Rajasekar, A.

2001-12-01

Recent advances in technology have enabled the exploitation of satellite communications for high-speed (> 64 kbps) duplex communications with oceanographic ships at sea. Furthermore, decreasing costs for high-speed communications have made possible continuous connectivity to the global Internet for delivery of data ashore and communications with scientists and engineers on the ship. Through support from the Office of Naval Research, we have planned a series of tests using the R/V Revelle for real time data delivery of large quantities of underway data (e.g. continuous multibeam profiling) to shore for quality control, archiving, and real-time data availability. The Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics (IGPP) and the San Diego Supercomputer Center (SDSC) were funded by the NSF Information Technology Research (ITR) Program, the California Institute for Telecommunications and Information Technology [Cal-(IT)2] and the Scripps Institution of Oceanography for research entitled: "Exploring the Environment in Time: Wireless Networks & Real-Time Management." We will describe the technology to be used for the real-time seagoing experiment and the planned expansion of the project through support from the ITR grant. The short-term goal is to exercise the communications system aboard ship in various weather conditions and sea states while testing and developing the real-time data quality control and archiving methodology. The long-term goal is to enable continuous observations in the ocean, specifically supporting the goals of the DEOS (Dynamics of Earth and Ocean Systems) observatory program supported through a NSF Major Research Equipment (MRE) program - a permanent presence in the oceans. The impact on scientific work aboard ships, however, is likely to be fundamental. It will be possible to go to sea in the future with limited engineering capability for scientific operations by allowing shore-based quality control of data collected and

Spectral analysis of hydrological time series of a river basin in southern Spain

NASA Astrophysics Data System (ADS)

Luque-Espinar, Juan Antonio; Pulido-Velazquez, David; Pardo-Igúzquiza, Eulogio; Fernández-Chacón, Francisca; Jiménez-Sánchez, Jorge; Chica-Olmo, Mario

2016-04-01

Spectral analysis has been applied with the aim to determine the presence and statistical significance of climate cycles in data series from different rainfall, piezometric and gauging stations located in upper Genil River Basin. This river starts in Sierra Nevada Range at 3,480 m a.s.l. and is one of the most important rivers of this region. The study area has more than 2.500 km2, with large topographic differences. For this previous study, we have used more than 30 rain data series, 4 piezometric data series and 3 data series from gauging stations. Considering a monthly temporal unit, the studied period range from 1951 to 2015 but most of the data series have some lacks. Spectral analysis is a methodology widely used to discover cyclic components in time series. The time series is assumed to be a linear combination of sinusoidal functions of known periods but of unknown amplitude and phase. The amplitude is related with the variance of the time series, explained by the oscillation at each frequency (Blackman and Tukey, 1958, Bras and Rodríguez-Iturbe, 1985, Chatfield, 1991, Jenkins and Watts, 1968, among others). The signal component represents the structured part of the time series, made up of a small number of embedded periodicities. Then, we take into account the known result for the one-sided confidence band of the power spectrum estimator. For this study, we established confidence levels of <90%, 90%, 95%, and 99%. Different climate signals have been identified: ENSO, QBO, NAO, Sun Spot cycles, as well as others related to sun activity, but the most powerful signals correspond to the annual cycle, followed by the 6 month and NAO cycles. Nevertheless, significant differences between rain data series and piezometric/flow data series have been pointed out. In piezometric data series and flow data series, ENSO and NAO signals could be stronger than others with high frequencies. The climatic peaks in lower frequencies in rain data are smaller and the confidence

Refine of Regional Ocean Tide Model Using GPS Data

NASA Astrophysics Data System (ADS)

Wang, F.; Zhang, P.; Sun, Z.; Jiang, Z.; Zhang, Q.

2018-04-01

Due to lack of regional data constraints, all global ocean tide models are not accuracy enough in offshore areas around China, also the displacements predicted by different models are not consistency. The ocean tide loading effects have become a major source of error in the high precision GPS positioning. It is important for high precision GPS applications to build an appropriate regional ocean tide model. We first process the four offshore GPS tracking station's observation data which located in Guangdong province of China by using PPP aproach to get the time series. Then use the spectral inversion method to acquire eigenvalues of the Ocean Tidal Loading. We get the estimated value of not only 12hour period tide wave (M2, S2, N2, K2) but also 24hour period tide wave (O1, K1, P1, Q1) which has not been got in presious studies. The contrast test shows that GPS estimation value of M2, K1 is consistent with the result of five famous glocal ocean load tide models, but S2, N2, K2, O1, P1, Q1 is obviously larger.

Transient deformation from daily GPS displacement time series: postseismic deformation, ETS and evolving strain rates

NASA Astrophysics Data System (ADS)

Bock, Y.; Fang, P.; Moore, A. W.; Kedar, S.; Liu, Z.; Owen, S. E.; Glasscoe, M. T.

2016-12-01

Detection of time-dependent crustal deformation relies on the availability of accurate surface displacements, proper time series analysis to correct for secular motion, coseismic and non-tectonic instrument offsets, periodic signatures at different frequencies, and a realistic estimate of uncertainties for the parameters of interest. As part of the NASA Solid Earth Science ESDR System (SESES) project, daily displacement time series are estimated for about 2500 stations, focused on tectonic plate boundaries and having a global distribution for accessing the terrestrial reference frame. The "combined" time series are optimally estimated from independent JPL GIPSY and SIO GAMIT solutions, using a consistent set of input epoch-date coordinates and metadata. The longest time series began in 1992; more than 30% of the stations have experienced one or more of 35 major earthquakes with significant postseismic deformation. Here we present three examples of time-dependent deformation that have been detected in the SESES displacement time series. (1) Postseismic deformation is a fundamental time-dependent signal that indicates a viscoelastic response of the crust/mantle lithosphere, afterslip, or poroelastic effects at different spatial and temporal scales. It is critical to identify and estimate the extent of postseismic deformation in both space and time not only for insight into the crustal deformation and earthquake cycles and their underlying physical processes, but also to reveal other time-dependent signals. We report on our database of characterized postseismic motions using a principal component analysis to isolate different postseismic processes. (2) Starting with the SESES combined time series and applying a time-dependent Kalman filter, we examine episodic tremor and slow slip (ETS) in the Cascadia subduction zone. We report on subtle slip details, allowing investigation of the spatiotemporal relationship between slow slip transients and tremor and their

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

NASA Astrophysics Data System (ADS)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of

Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4

NASA Astrophysics Data System (ADS)

El Yazidi, Abdelhadi; Ramonet, Michel; Ciais, Philippe; Broquet, Gregoire; Pison, Isabelle; Abbaris, Amara; Brunner, Dominik; Conil, Sebastien; Delmotte, Marc; Gheusi, Francois; Guerin, Frederic; Hazan, Lynn; Kachroudi, Nesrine; Kouvarakis, Giorgos; Mihalopoulos, Nikolaos; Rivier, Leonard; Serça, Dominique

2018-03-01

This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS) and standard deviation of the background (SD) to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System) Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE), a high-mountain observatory in the south-west of France (PDM), a regional marine background site in Crete (FKL) and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the

Decadal variations in atmospheric water vapor time series estimated using GNSS, ERA-Interim, and synoptic data

NASA Astrophysics Data System (ADS)

Alshawaf, Fadwa; Dick, Galina; Heise, Stefan; Balidakis, Kyriakos; Schmidt, Torsten; Wickert, Jens

2017-04-01

Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research although they may not be sufficiently long. In this work, we compare the trend estimated from GNSS time series with that estimated from European Center for Medium-RangeWeather Forecasts Reanalysis (ERA-Interim) data and meteorological measurements.We aim at evaluating climate evolution in Central Europe by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim data, and 3) determined based on daily surface measurements of temperature and relative humidity. The other variables are available from surface meteorological stations or received from ERA-Interim. The PWV trend component estimated from GNSS data strongly correlates (>70%) with that estimated from the other data sets. The linear trend is estimated by straight line fitting over 30 years of seasonally-adjusted PWV time series obtained using the meteorological measurements. The results show a positive trend in the PWV time series with an increase of 0.2-0.7 mm/decade with a mean standard deviations of 0.016 mm/decade. In this paper, we present the results at three GNSS stations. The temporal increment of the PWV correlates with the temporal increase in the temperature levels.

International ocean services: Is it time - will it ever be time?

NASA Technical Reports Server (NTRS)

Holland, Geoffrey

1992-01-01

Considering the objectives of the Ocean Data Climate Workshop this week and trying to select a related, but not too technical subject, I decided to look at the services needed for ocean clients. I thought that it would make an interesting theme to consider how the acceptance of international ocean services is progressing over the years. Or is it? Are we closer to having a global observing system than we were twenty years ago? If so, what has changed between then and now? What is different between the efforts of twenty years or so ago to get the Integrated Global Ocean Stations System (IGOSS) off the ground and the present attempts to establish the Global Ocean Observing System (GOOS)? Is there a window of opportunity now that didn't exist then? If it doesn't happen now, a legitimate question could well be asked.... Will it ever happen? At least on historical topics I am on firm ground, having been attending IOC meetings for many years, in fact, it was in 1970 that I attended my first IGOSS meeting and a couple of years later I started what was to become regular attendances at the IOC Governing Body sessions. Despite the numbing effect of sitting through IOC related meetings for a total of what must add up to two or three years, I can still defend the IOC as an essential intergovernmental body for the oceans. One can accept the value of medicine, but one doesn't have to enjoy the taste.

Noise in DORIS station position time series provided by IGN-JPL, INASAN and CNES-CLS Analysis Centres for the ITRF2014 realization

NASA Astrophysics Data System (ADS)

Khelifa, Sofiane

2016-12-01

The purpose of this paper is to compare the noise characteristics in DORIS station positions between the three solutions derived by IGN-JPL (named as IGN), INASAN (named as INA) and CNES-CLS (named as LCA) Analysis Centres for ITRF2014 contribution, and to evaluate the improvements of these reprocessed solutions in terms of noise level with the previous ITRF2008 solutions. To the weekly STCD (STation Coordinate Difference) residual position time series of 23 stations referred to ITRF2008 and expressed in the local frame (North, East and Up), we calculated the Allan variance to identify their noise type, and applied the wavelet transform to assess their annual and semi-annual signals, and their noise level. The results reveal that the three solutions are dominated by white noise in all three components. The noise level is the lowest in the LCA solution; the average noise level in respectively, North, East and Vertical components is around 5.9 mm, 9.3 mm and 6.6 mm for LCA, 9 mm, 11.6 mm and 9 mm for IGN, and 8.7 mm, 11.6 mm and 9.1 mm for INA. The results also show that the tropical (±23.5°) stations are more distorted than mid-latitude and high latitude stations. In terms of noise level, the reprocessed LCA solution (lca14wd40) and its previous solution (lca11wd02) converge to similar results, while the reprocessed IGN (ign14wd15) and INA (ina14wd08) solutions show improvements with respect to their previous solutions (ign11wd01) and (ina12wd01) respectively, especially in the East component. Furthermore, the possible origin of the estimated annual signal was also investigated by comparing it with hydrology and atmospheric loading displacements. The annual Vertical component for the three solutions is more correlated with the GLDAS/Noah hydrology model with an average correlation of about 0.35, and shows a strong correlation of about 0.76 with ECMWF-IB and ECMWF-MOG2D atmospheric models for the station Krasnoyarsk (KRBB) in Siberia.

Downsizer - A Graphical User Interface-Based Application for Browsing, Acquiring, and Formatting Time-Series Data for Hydrologic Modeling

USGS Publications Warehouse

Ward-Garrison, Christian; Markstrom, Steven L.; Hay, Lauren E.

2009-01-01

The U.S. Geological Survey Downsizer is a computer application that selects, downloads, verifies, and formats station-based time-series data for environmental-resource models, particularly the Precipitation-Runoff Modeling System. Downsizer implements the client-server software architecture. The client presents a map-based, graphical user interface that is intuitive to modelers; the server provides streamflow and climate time-series data from over 40,000 measurement stations across the United States. This report is the Downsizer user's manual and provides (1) an overview of the software design, (2) installation instructions, (3) a description of the graphical user interface, (4) a description of selected output files, and (5) troubleshooting information.

Seaglider surveys at Ocean Station Papa: Diagnosis of upper-ocean heat and salt balances using least squares with inequality constraints

NASA Astrophysics Data System (ADS)

Pelland, Noel A.; Eriksen, Charles C.; Cronin, Meghan F.

2017-06-01

Heat and salt balances in the upper 200 m are examined using data from Seaglider spatial surveys June 2008 to January 2010 surrounding a NOAA surface mooring at Ocean Station Papa (OSP; 50°N, 145°W). A least-squares approach is applied to repeat Seaglider survey and moored measurements to solve for unknown or uncertain monthly three-dimensional circulation and vertical diffusivity. Within the surface boundary layer, the estimated heat and salt balances are dominated throughout the surveys by turbulent flux, vertical advection, and for heat, radiative absorption. When vertically integrated balances are considered, an estimated upwelling of cool water balances the net surface input of heat, while the corresponding large import of salt across the halocline due to upwelling and diffusion is balanced by surface moisture input and horizontal import of fresh water. Measurement of horizontal gradients allows the estimation of unresolved vertical terms over more than one annual cycle; diffusivity in the upper-ocean transition layer decreases rapidly to the depth of the maximum near-surface stratification in all months, with weak seasonal modulation in the rate of decrease and profile amplitude. Vertical velocity is estimated to be on average upward but with important monthly variations. Results support and expand existing evidence concerning the importance of horizontal advection in the balances of heat and salt in the Gulf of Alaska, highlight time and depth variability in difficult-to-measure vertical transports in the upper ocean, and suggest avenues of further study in future observational work at OSP.

Atmospheric Concentrations of Persistent Organic Pollutants in the Southern Ocean

NASA Astrophysics Data System (ADS)

Vlahos, P.; Edson, J.; Cifuentes, A.; McGillis, W. R.; Zappa, C.

2008-12-01

Long-range transport of persistent organic pollutant (POPs) is a global concern. Remote regions such as the Southern Ocean are greatly under-sampled though critical components in understanding POPs cycling. Over 20 high-volume air samples were collected in the Southern Ocean aboard the RV Brown during the GASEX III experiment between Mar 05 to April 9 2008. The relatively stationary platform (51S,38W) enabled the collection of a unique atmospheric time series at this open ocean station. Air sampling was also conducted across transects from Punto Arenas, Chile and to Montevideo, Uruguay. Samples were collected using glass sleeves packed with poly-urethane foam plugs and C-18 resin in order to collect target organic pollutants (per-fluorinated compounds, currently and historically used pesticides) in this under-sampled region. Here we present POPs concentrations and trends over the sampled period and compare variations with air parcel back trajectories to establish potential origins of their long-range transport.

U-Series Disequilibria across the New Southern Ocean Mantle Province, Australian-Antarctic Ridge

NASA Astrophysics Data System (ADS)

Scott, S. R.; Sims, K. W. W.; Park, S. H.; Langmuir, C. H.; Lin, J.; Kim, S. S.; Blichert-Toft, J.; Michael, P. J.; Choi, H.; Yang, Y. S.

2017-12-01

Mid-ocean ridge basalts (MORB) provide a unique window into the temporal and spatial scales of mantle evolution. Long-lived radiogenic isotopes in MORB have demonstrated that the mantle contains many different chemical components or "flavors". U-series disequilibria in MORB have further shown that different chemical components/lithologies in the mantle contribute differently to mantle melting processes beneath mid-ocean ridges. Recent Sr, Nd, Hf, and Pb isotopic analyses from newly collected basalts along the Australian-Antarctic Ridge (AAR) have revealed that a large distinct mantle province exists between the Australian-Antarctic Discordance and the Pacific-Antarctic Ridge, extending from West Antarctica and Marie Byrd Land to New Zealand and Eastern Australia (Park et al., submitted). This southern mantle province is located between the Indian-type mantle and the Pacific-type mantle domains. U-series measurements in the Southeast Indian Ridge and East Pacific Rise provinces show distinct signatures suggestive of differences in melting processes and source lithology. To examine whether the AAR mantle province also exhibits different U-series systematics we have measured U-Th-Ra disequilibria data on 38 basalts from the AAR sampled along 500 km of ridge axis from two segments that cross the newly discovered Southern Ocean Mantle province. We compare the data to those from nearby ridge segments show that the AAR possesses unique U-series disequilibria, and are thus undergoing distinct mantle melting dynamics relative to the adjacent Pacific and Indian ridges. (230Th)/(238U) excesses in zero-age basalts (i.e., those with (226Ra)/(230Th) > 1.0) range from 1.3 to 1.7, while (226Ra)/(230Th) ranges from 1.0 to 2.3. (226Ra)/(230Th) and (230Th)/(238U) are negatively correlated, consistent with the model of mixing between deep and shallow melts. The AAR data show higher values of disequilibria compared to the Indian and Pacific Ridges, which can be explained by either

New insights into soil temperature time series modeling: linear or nonlinear?

NASA Astrophysics Data System (ADS)

Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram

2018-03-01

Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and

Online Time Series Analysis of Land Products over Asia Monsoon Region via Giovanni

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2011-01-01

Time series analysis is critical to the study of land cover/land use changes and climate. Time series studies at local-to-regional scales require higher spatial resolution, such as 1km or less, data. MODIS land products of 250m to 1km resolution enable such studies. However, such MODIS land data files are distributed in 10ox10o tiles, due to large data volumes. Conducting a time series study requires downloading all tiles that include the study area for the time period of interest, and mosaicking the tiles spatially. This can be an extremely time-consuming process. In support of the Monsoon Asia Integrated Regional Study (MAIRS) program, NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has processed MODIS land products at 1 km resolution over the Asia monsoon region (0o-60oN, 60o-150oE) with a common data structure and format. The processed data have been integrated into the Giovanni system (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) that enables users to explore, analyze, and download data over an area and time period of interest easily. Currently, the following regional MODIS land products are available in Giovanni: 8-day 1km land surface temperature and active fire, monthly 1km vegetation index, and yearly 0.05o, 500m land cover types. More data will be added in the near future. By combining atmospheric and oceanic data products in the Giovanni system, it is possible to do further analyses of environmental and climate changes associated with the land, ocean, and atmosphere. This presentation demonstrates exploring land products in the Giovanni system with sample case scenarios.

HOMPRA Europe - A gridded precipitation data set from European homogenized time series

NASA Astrophysics Data System (ADS)

Rustemeier, Elke; Kapala, Alice; Meyer-Christoffer, Anja; Finger, Peter; Schneider, Udo; Venema, Victor; Ziese, Markus; Simmer, Clemens; Becker, Andreas

2017-04-01

Reliable monitoring data are essential for robust analyses of climate variability and, in particular, long-term trends. In this regard, a gridded, homogenized data set of monthly precipitation totals - HOMPRA Europe (HOMogenized PRecipitation Analysis of European in-situ data)- is presented. The data base consists of 5373 homogenized monthly time series, a carefully selected subset held by the Global Precipitation Climatology Centre (GPCC). The chosen series cover the period 1951-2005 and contain less than 10% missing values. Due to the large number of data, an automatic algorithm had to be developed for the homogenization of these precipitation series. In principal, the algorithm is based on three steps: * Selection of overlapping station networks in the same precipitation regime, based on rank correlation and Ward's method of minimal variance. Since the underlying time series should be as homogeneous as possible, the station selection is carried out by deterministic first derivation in order to reduce artificial influences. * The natural variability and trends were temporally removed by means of highly correlated neighboring time series to detect artificial break-points in the annual totals. This ensures that only artificial changes can be detected. The method is based on the algorithm of Caussinus and Mestre (2004). * In the last step, the detected breaks are corrected monthly by means of a multiple linear regression (Mestre, 2003). Due to the automation of the homogenization, the validation of the algorithm is essential. Therefore, the method was tested on artificial data sets. Additionally the sensitivity of the method was tested by varying the neighborhood series. If available in digitized form, the station history was also used to search for systematic errors in the jump detection. Finally, the actual HOMPRA Europe product is produced by interpolation of the homogenized series onto a 1° grid using one of the interpolation schems operationally at GPCC

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Consistent Long-Time Series of GPS Satellite Antenna Phase Center Corrections

NASA Astrophysics Data System (ADS)

Steigenberger, P.; Schmid, R.; Rothacher, M.

2004-12-01

The current IGS processing strategy disregards satellite antenna phase center variations (pcvs) depending on the nadir angle and applies block-specific phase center offsets only. However, the transition from relative to absolute receiver antenna corrections presently under discussion necessitates the consideration of satellite antenna pcvs. Moreover, studies of several groups have shown that the offsets are not homogeneous within a satellite block. Manufacturer specifications seem to confirm this assumption. In order to get best possible antenna corrections, consistent ten-year time series (1994-2004) of satellite-specific pcvs and offsets were generated. This challenging effort became possible as part of the reprocessing of a global GPS network currently performed by the Technical Universities of Munich and Dresden. The data of about 160 stations since the official start of the IGS in 1994 have been reprocessed, as today's GPS time series are mostly inhomogeneous and inconsistent due to continuous improvements in the processing strategies and modeling of global GPS solutions. An analysis of the signals contained in the time series of the phase center offsets demonstrates amplitudes on the decimeter level, at least one order of magnitude worse than the desired accuracy. The periods partly arise from the GPS orbit configuration, as the orientation of the orbit planes with regard to the inertial system repeats after about 350 days due to the rotation of the ascending nodes. In addition, the rms values of the X- and Y-offsets show a high correlation with the angle between the orbit plane and the direction to the sun. The time series of the pcvs mainly point at the correlation with the global terrestrial scale. Solutions with relative and absolute phase center corrections, with block- and satellite-specific satellite antenna corrections demonstrate the effect of this parameter group on other global GPS parameters such as the terrestrial scale, station velocities, the

VIIRS On-Orbit Calibration for Ocean Color Data Processing

NASA Technical Reports Server (NTRS)

Eplee, Robert E., Jr.; Turpie, Kevin R.; Fireman, Gwyn F.; Meister, Gerhard; Stone, Thomas C.; Patt, Frederick S.; Franz, Bryan; Bailey, Sean W.; Robinson, Wayne D.; McClain, Charles R.

2012-01-01

The NASA VIIRS Ocean Science Team (VOST) has the task of evaluating Suomi NPP VIIRS ocean color data for the continuity of the NASA ocean color climate data records. The generation of science quality ocean color data products requires an instrument calibration that is stable over time. Since the VIIRS NIR Degradation Anomaly directly impacts the bands used for atmospheric correction of the ocean color data (Bands M6 and M7), the VOST has adapted the VIIRS on-orbit calibration approach to meet the ocean science requirements. The solar diffuser calibration time series and the solar diffuser stability monitor time series have been used to derive changes in the instrument response and diffuser reflectance over time for bands M1-M11.

GPS Time Series Analysis of Southern California Associated with the 2010 M7.2 El Mayor/Cucapah Earthquake

NASA Technical Reports Server (NTRS)

Granat, Robert; Donnellan, Andrea

2011-01-01

The Magnitude 7.2 El-Mayor/Cucapah earthquake the occurred in Mexico on April 4, 2012 was well instrumented with continuous GPS stations in California. Large Offsets were observed at the GPS stations as a result of deformation from the earthquake providing information about the co-seismic fault slip as well as fault slip from large aftershocks. Information can also be obtained from the position time series at each station.

Climate-driven changes to the atmospheric CO2 sink in the subtropical North Pacific Ocean.

PubMed

Dore, John E; Lukas, Roger; Sadler, Daniel W; Karl, David M

2003-08-14

The oceans represent a significant sink for atmospheric carbon dioxide. Variability in the strength of this sink occurs on interannual timescales, as a result of regional and basin-scale changes in the physical and biological parameters that control the flux of this greenhouse gas into and out of the surface mixed layer. Here we analyse a 13-year time series of oceanic carbon dioxide measurements from station ALOHA in the subtropical North Pacific Ocean near Hawaii, and find a significant decrease in the strength of the carbon dioxide sink over the period 1989-2001. We show that much of this reduction in sink strength can be attributed to an increase in the partial pressure of surface ocean carbon dioxide caused by excess evaporation and the accompanying concentration of solutes in the water mass. Our results suggest that carbon dioxide uptake by ocean waters can be strongly influenced by changes in regional precipitation and evaporation patterns brought on by climate variability.

From Networks to Time Series

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

GPS coordinate time series measurements in Ontario and Quebec, Canada

NASA Astrophysics Data System (ADS)

Samadi Alinia, Hadis; Tiampo, Kristy F.; James, Thomas S.

2017-06-01

New precise network solutions for continuous GPS (cGPS) stations distributed in eastern Ontario and western Québec provide constraints on the regional three-dimensional crustal velocity field. Five years of continuous observations at fourteen cGPS sites were analyzed using Bernese GPS processing software. Several different sub-networks were chosen from these stations, and the data were processed and compared to in order to select the optimal configuration to accurately estimate the vertical and horizontal station velocities and minimize the associated errors. The coordinate time series were then compared to the crustal motions from global solutions and the optimized solution is presented here. A noise analysis model with power-law and white noise, which best describes the noise characteristics of all three components, was employed for the GPS time series analysis. The linear trend, associated uncertainties, and the spectral index of the power-law noise were calculated using a maximum likelihood estimation approach. The residual horizontal velocities, after removal of rigid plate motion, have a magnitude consistent with expected glacial isostatic adjustment (GIA). The vertical velocities increase from subsidence of almost 1.9 mm/year south of the Great Lakes to uplift near Hudson Bay, where the highest rate is approximately 10.9 mm/year. The residual horizontal velocities range from approximately 0.5 mm/year, oriented south-southeastward, at the Great Lakes to nearly 1.5 mm/year directed toward the interior of Hudson Bay at stations adjacent to its shoreline. Here, the velocity uncertainties are estimated at less than 0.6 mm/year for the horizontal component and 1.1 mm/year for the vertical component. A comparison between the observed velocities and GIA model predictions, for a limited range of Earth models, shows a better fit to the observations for the Earth model with the smallest upper mantle viscosity and the largest lower mantle viscosity. However, the

Which homogenisation method is appropriate for daily time series of relative humidity?

NASA Astrophysics Data System (ADS)

Chimani, Barbara; Nemec, Johanna; Auer, Ingeborg; Venema, Victor

2014-05-01

Data homogenisation is an essential part of reliable climate data analyses. Different tools for detecting and adjusting breaks in daily extreme temperatures (Tmin, Tmax) and daily precipitation sums were developed in the last years. Due to its influence on health, plants and construction relative humidity is another parameter of great importance. On the basis of 6 networks of measured (and homogenized with respect to the monthly means) relative humidity data, which cover different climatic areas in Austria, a synthetic data set for testing and validating homogenisation methods was built. Each network consists of 4 to 6 station time series with a minimum length of 5 years. The so-called surrogate networks resemble the statistical properties (e.g. distribution of parameter, auto- and cross correlation within the network) of the measured time series, but are extended to 100 year long time series, which are in a first step assumed to be homogeneous. For creating the best possible surrogate dataset of relative humidity detailed statistical information on potential inhomogeneities is decisive. Information on the potential breaks was taken from parallel measurements available for some Austrian locations, mostly representing changes in instrumentation and/or station relocation. Beside changes in the distribution of the parameter the analyses includes an estimation of changes in the number of missing data, global and local biases, both on a seasonal and annual basis. An additional break is to be expected in the Austrian time series due to a change in observation time in 1970/1971. Since this change occurred simultaneously at all Austrian climate stations, standard homogenisation methods, which rely on a comparison with reference stations, are not able to detect or correct this shift. Therefore an independent correction method for this type of break, to be applied before homogenisation was developed. This type of change point was not included in the surrogate network

Nineteen-year time-series sediment trap study of Coccolithus pelagicus and Emiliania huxleyi (calcareous nannoplankton) fluxes in the Bering Sea and subarctic Pacific Ocean

NASA Astrophysics Data System (ADS)

Tsutsui, Hideto; Takahashi, Kozo; Asahi, Hirofumi; Jordan, Richard W.; Nishida, Shiro; Nishiwaki, Niichi; Yamamoto, Sumito

2016-03-01

Coccolithophore fluxes at two sediment trap stations, Station AB in the Bering Sea and Station SA in the subarctic Pacific Ocean, were studied over a nineteen-year (August 1990-July 2009) interval. Two major species, Coccolithus pelagicus and Emiliania huxleyi, occur at both stations, with Gephyrocapsa oceanica, Umbilicosphaera sibogae, Braarudosphaera bigelowii, and Syracosphaera spp. as minor components. The mean coccolithophore fluxes at Stations AB and SA increased from 28.9×106 m2 d-1 and 61.9×106 m2 d-1 in 1990-1999 to 54.4×106 m2 d-1 and 130.2×106 m2 d-1 in 2002-2009, respectively. Furthermore, in late 1999 to early 2000, there was a significant shift in the most dominant species from E. huxleyi to C. pelagicus. High abundances of E. huxleyi correspond to the positive mode of the Pacific Decadal Oscillation (PDO), while those of C. pelagicus respond to the PDO negative mode and are related to water temperature changes at <45 m depth. The lag between coccolithophore species percentage at Station SA and the PDO shift is one year and four months. The coccolithophore flux maxima appear in June and October-November at both stations; the June flux maximum is caused by C. pelagicus, whereas the October-November maximum is due to E. huxleyi. At both stations the mean seawater temperature in the top 45 m from August to October increased ca. 1 °C with linear recurrence from 1990 to 2008. The coccosphere fluxes after Year 2000 at Stations AB and SA, and the shift in species dominance, may have been influenced by this warming.

Extensive under-ice turbulence microstructure measurements in the central Arctic Ocean in 2015

NASA Astrophysics Data System (ADS)

Rabe, Benjamin; Janout, Markus; Graupner, Rainer; Hoelemann, Jens; Hampe, Hendrik; Hoppmann, Mario; Horn, Myriel; Juhls, Bennet; Korhonen, Meri; Nikolopoulos, Anna; Pisarev, Sergey; Randelhoff, Achim; Savy, John-Philippe; Villacieros, Nicolas

2016-04-01

The Arctic Ocean is a strongly stratified low-energy environment, where tides are weak and the upper ocean is protected by an ice cover during much of the year. Interior mixing processes are dominated by double diffusion. The upper Arctic Ocean features a cold surface mixed layer, which, separated by a sharp halocline, protects the sea ice from the warmer underlying Atlantic- and Pacific-derived water masses. These water masses carry nutrients that are important for the Arctic ecosystem. Hence vertical fluxes of heat, salt, and nutrients are crucial components in understanding the Arctic ecosystem. Yet, direct flux measurements are difficult to obtain and hence sparse. In 2015, two multidisciplinary R/V Polarstern expeditions to the Arctic Ocean resulted in a series of under-ice turbulence microstructure measurements. These cover different locations across the Eurasian and Makarov Basins, during the melt season in spring and early summer as well as during freeze-up in late summer. Sampling was carried out from ice floes with repeated profiles resulting in 4-24 hour-long time series. 2015 featured anomalously warm atmospheric conditions during summer followed by unusually low temperatures in September. Our measurements show elevated dissipation rates at the base of the mixed layer throughout all stations, with significantly higher levels above the Eurasian continental slope when compared with the Arctic Basin. Additional peaks were found between the mixed layer and the halocline, in particular at stations where Pacific Summer water was present. This contribution provides first flux estimates and presents first conclusions regarding the impact of atmospheric and sea ice conditions on vertical mixing in 2015.

The Earth Observation Monitor - Automated monitoring and alerting for spatial time-series data based on OGC web services

NASA Astrophysics Data System (ADS)

Eberle, J.; Hüttich, C.; Schmullius, C.

2014-12-01

Spatial time series data are freely available around the globe from earth observation satellites and meteorological stations for many years until now. They provide useful and important information to detect ongoing changes of the environment; but for end-users it is often too complex to extract this information out of the original time series datasets. This issue led to the development of the Earth Observation Monitor (EOM), an operational framework and research project to provide simple access, analysis and monitoring tools for global spatial time series data. A multi-source data processing middleware in the backend is linked to MODIS data from Land Processes Distributed Archive Center (LP DAAC) and Google Earth Engine as well as daily climate station data from NOAA National Climatic Data Center. OGC Web Processing Services are used to integrate datasets from linked data providers or external OGC-compliant interfaces to the EOM. Users can either use the web portal (webEOM) or the mobile application (mobileEOM) to execute these processing services and to retrieve the requested data for a given point or polygon in userfriendly file formats (CSV, GeoTiff). Beside providing just data access tools, users can also do further time series analyses like trend calculations, breakpoint detections or the derivation of phenological parameters from vegetation time series data. Furthermore data from climate stations can be aggregated over a given time interval. Calculated results can be visualized in the client and downloaded for offline usage. Automated monitoring and alerting of the time series data integrated by the user is provided by an OGC Sensor Observation Service with a coupled OGC Web Notification Service. Users can decide which datasets and parameters are monitored with a given filter expression (e.g., precipitation value higher than x millimeter per day, occurrence of a MODIS Fire point, detection of a time series anomaly). Datasets integrated in the SOS service are

An accuracy assessment of realtime GNSS time series toward semi- real time seafloor geodetic observation

NASA Astrophysics Data System (ADS)

Osada, Y.; Ohta, Y.; Demachi, T.; Kido, M.; Fujimoto, H.; Azuma, R.; Hino, R.

2013-12-01

Large interplate earthquake repeatedly occurred in Japan Trench. Recently, the detail crustal deformation revealed by the nation-wide inland GPS network called as GEONET by GSI. However, the maximum displacement region for interplate earthquake is mainly located offshore region. GPS/Acoustic seafloor geodetic observation (hereafter GPS/A) is quite important and useful for understanding of shallower part of the interplate coupling between subducting and overriding plates. We typically conduct GPS/A in specific ocean area based on repeated campaign style using research vessel or buoy. Therefore, we cannot monitor the temporal variation of seafloor crustal deformation in real time. The one of technical issue on real time observation is kinematic GPS analysis because kinematic GPS analysis based on reference and rover data. If the precise kinematic GPS analysis will be possible in the offshore region, it should be promising method for real time GPS/A with USV (Unmanned Surface Vehicle) and a moored buoy. We assessed stability, precision and accuracy of StarFireTM global satellites based augmentation system. We primarily tested for StarFire in the static condition. In order to assess coordinate precision and accuracy, we compared 1Hz StarFire time series and post-processed precise point positioning (PPP) 1Hz time series by GIPSY-OASIS II processing software Ver. 6.1.2 with three difference product types (ultra-rapid, rapid, and final orbits). We also used difference interval clock information (30 and 300 seconds) for the post-processed PPP processing. The standard deviation of real time StarFire time series is less than 30 mm (horizontal components) and 60 mm (vertical component) based on 1 month continuous processing. We also assessed noise spectrum of the estimated time series by StarFire and post-processed GIPSY PPP results. We found that the noise spectrum of StarFire time series is similar pattern with GIPSY-OASIS II processing result based on JPL rapid orbit

A quasi-global precipitation time series for drought monitoring

USGS Publications Warehouse

Funk, Chris C.; Peterson, Pete J.; Landsfeld, Martin F.; Pedreros, Diego H.; Verdin, James P.; Rowland, James D.; Romero, Bo E.; Husak, Gregory J.; Michaelsen, Joel C.; Verdin, Andrew P.

2014-01-01

Estimating precipitation variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in historical context so that the severity of rainfall deficits may quickly be evaluated. To this end, scientists at the U.S. Geological Survey Earth Resources Observation and Science Center, working closely with collaborators at the University of California, Santa Barbara Climate Hazards Group, have developed a quasi-global (50°S–50°N, 180°E–180°W), 0.05° resolution, 1981 to near-present gridded precipitation time series: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data archive.

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Time Series Analysis of SOLSTICE Measurements

NASA Astrophysics Data System (ADS)

Wen, G.; Cahalan, R. F.

2003-12-01

Solar radiation is the major energy source for the Earth's biosphere and atmospheric and ocean circulations. Variations of solar irradiance have been a major concern of scientists both in solar physics and atmospheric sciences. A number of missions have been carried out to monitor changes in total solar irradiance (TSI) [see Fröhlich and Lean, 1998 for review] and spectral solar irradiance (SSI) [e.g., SOLSTICE on UARS and VIRGO on SOHO]. Observations over a long time period reveal the connection between variations in solar irradiance and surface magnetic fields of the Sun [Lean1997]. This connection provides a guide to scientists in modeling solar irradiances [e.g., Fontenla et al., 1999; Krivova et al., 2003]. Solar spectral observations have now been made over a relatively long time period, allowing statistical analysis. This paper focuses on predictability of solar spectral irradiance using observed SSI from SOLSTICE . Analysis of predictability is based on nonlinear dynamics using an artificial neural network in a reconstructed phase space [Abarbanel et al., 1993]. In the analysis, we first examine the average mutual information of the observed time series and a delayed time series. The time delay that gives local minimum of mutual information is chosen as the time-delay for phase space reconstruction [Fraser and Swinney, 1986]. The embedding dimension of the reconstructed phase space is determined using the false neighbors and false strands method [Kennel and Abarbanel, 2002]. Subsequently, we use a multi-layer feed-forward network with back propagation scheme [e.g., Haykin, 1994] to model the time series. The predictability of solar irradiance as a function of wavelength is considered. References Abarbanel, H. D. I., R. Brown, J. J. Sidorowich, and L. Sh. Tsimring, Rev. Mod. Phys. 65, 1331, 1993. Fraser, A. M. and H. L. Swinney, Phys. Rev. 33A, 1134, 1986. Fontenla, J., O. R. White, P. Fox, E. H. Avrett and R. L. Kurucz, The Astrophysical Journal, 518, 480

Effect of different tropospheric mapping functions on the TRF, CRF and position time-series estimated from VLBI

NASA Astrophysics Data System (ADS)

Tesmer, Volker; Boehm, Johannes; Heinkelmann, Robert; Schuh, Harald

2007-06-01

This paper compares estimated terrestrial reference frames (TRF) and celestial reference frames (CRF) as well as position time-series in terms of systematic differences, scale, annual signals and station position repeatabilities using four different tropospheric mapping functions (MF): The NMF (Niell Mapping Function) and the recently developed GMF (Global Mapping Function) consist of easy-to-handle stand-alone formulae, whereas the IMF (Isobaric Mapping Function) and the VMF1 (Vienna Mapping Function 1) are determined from numerical weather models. All computations were performed at the Deutsches Geodätisches Forschungsinstitut (DGFI) using the OCCAM 6.1 and DOGS-CS software packages for Very Long Baseline Interferometry (VLBI) data from 1984 until 2005. While it turned out that CRF estimates only slightly depend on the MF used, showing small systematic effects up to 0.025 mas, some station heights of the computed TRF change by up to 13 mm. The best agreement was achieved for the VMF1 and GMF results concerning the TRFs, and for the VMF1 and IMF results concerning scale variations and position time-series. The amplitudes of the annual periodical signals in the time-series of estimated heights differ by up to 5 mm. The best precision in terms of station height repeatability is found for the VMF1, which is 5 7% better than for the other MFs.

Dynamical Interpolation of Mesoscale Flows in the TOPEX/Poseidon Diamond Surrounding the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time-Series Study Site

NASA Technical Reports Server (NTRS)

McGillicuddy, Dennis J., Jr.; Kosnyrev, V. K.

2001-01-01

An open boundary ocean model is configured in a domain bounded by the four TOPEX/Poseidon (T/P) ground tracks surrounding the US Joint Global Ocean Flux Study Bermuda Atlantic Time-Series Study (BATS) site. This implementation facilitates prescription of model boundary conditions directly from altimetric measurements (both TIP and ERS-2). The expected error characteristics for a domain of this size with periodically updated boundary conditions are established with idealized numerical experiments using simulated data. A hindcast simulation is then constructed using actual altimetric observations during the period October 1992 through September 1998. Quantitative evaluation of the simulation suggests significant skill. The correlation coefficient between predicted sea level anomaly and ERS observations in the model interior is 0.89; that for predicted versus observed dynamic height anomaly based on hydrography at the BATS site is 0.73. Comparison with the idealized experiments suggests that the main source of error in the hindcast is temporal undersampling of the boundary conditions. The hindcast simulation described herein provides a basis for retrospective analysis of BATS observations in the context of the mesoscale eddy field.

Dynamical Interpolation of Mesoscale Flows in the TOPEX/ Poseidon Diamond Surrounding the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time-Series Study Site

NASA Technical Reports Server (NTRS)

McGillicuddy, D. J.; Kosnyrev, V. K.

2001-01-01

An open boundary ocean model is configured in a domain bounded by the four TOPEX/Poseidon (TIP) ground tracks surrounding the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time-series Study (BATS) site. This implementation facilitates prescription of model boundary conditions directly from altimetric measurements (both TIP and ERS-2). The expected error characteristics for a domain of this size with periodically updated boundary conditions are established with idealized numerical experiments using simulated data. A hindcast simulation is then constructed using actual altimetric observations during the period October 1992 through September 1998. Quantitative evaluation of the simulation suggests significant skill. The correlation coefficient between predicted sea level anomaly and ERS observations in the model interior is 0.89; that for predicted versus observed dynamic height anomaly based on hydrography at the BATS site is 0.73. Comparison with the idealized experiments suggests that the main source of error in the hindcast is temporal undersampling of the boundary conditions. The hindcast simulation described herein provides a basis for retrospective analysis of BATS observations in the context of the mesoscale eddy field.

Data Rescue for precipitation station network in Slovak Republic

NASA Astrophysics Data System (ADS)

Fasko, Pavel; Bochníček, Oliver; Švec, Marek; Paľušová, Zuzana; Markovič, Ladislav

2016-04-01

Transparency of archive catalogues presents very important task for the data saving. It helps to the further activities e.g. digitalization and homogenization. For the time being visualization of time series continuation in precipitation stations (approximately 1250 stations) is under way in Slovak Republic since the beginning of observation (meteorological stations gradually began to operate during the second half of the 19th century in Slovakia). Visualization is joined with the activities like verification and accessibility of the data mentioned in the archive catalogue, station localization according to the historical annual books, conversion of coordinates into x-JTSK, y-JTSK and hydrological catchment assignment. Clustering of precipitation stations at the specific hydrological catchment in the map and visualization of the data duration (line graph) will lead to the effective assignment of corresponding precipitation stations for the prolongation of time series. This process should be followed by the process of turn or trend detection and homogenization. The risks and problems at verification of records from archive catalogues, their digitalization, repairs and the way of visualization will be seen in poster. During the searching process of the historical and often short time series, we realized the importance of mainly those stations, located in the middle and higher altitudes. They might be used as replacement for up to now quoted fictive points used at the construction of precipitation maps. Supplementing and enhancing the time series of individual stations will enable to follow changes in precipitation totals during the certain period as well as area totals for individual catchments in various time periods appreciated mainly by hydrologists and agro-climatologists.

Seasonal and Regional Variability in North Pacific Upper-Ocean Turbulence

NASA Astrophysics Data System (ADS)

Najjar, R.; Creedon, R.; Cronin, M. F.

2016-02-01

Turbulent diffusion at marine mixed layer base (MLB) plays a fundamental role in the transport of energy between the upper and abyssal ocean. Recent investigations of North Pacific mooring data at Ocean Climate Stations (OCS) Papa (50.1N,144.9W) and KEO (32.3N,144.6E) suggest seasonal and regional variability in thermal diffusivity (κT). In this investigation, it is hypothesized that these observed differences in κT are directly associated with synoptic variability in net surface heat flux (Q0), surface wind stress (τ), mixed layer depth (h), and density stratification at MLB (∂zσ|-h). To test this hypothesis, daily-averaged time series of κT are regressed against those of Q0, τ, h, and ∂zσ|-h at both Papa and KEO over a six year time period (2007-2013). Seasonality of each time series is removed before regression to capture synoptic variability of each variable. Preliminary results of the regression analysis suggest statistically significant correlations between κT and all forcing parameters at both mooring sites. These correlations have well-determined orders of magnitude and signs consistent with the hypothesis. As a result, differences in κT between Papa and KEO may be recast in terms of differences in their correlation coefficients. In order to continue investigation of these parameters and their effects on mean seasonal differences between the two regions, these results will be compared with turbulence predicted by the K-Profile Parameterization ocean turbulence model.

Satellite Ocean Color: Present Status, Future Challenges

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; McClain, Charles R.; Zukor, Dorothy J. (Technical Monitor)

2001-01-01

We are midway into our 5th consecutive year of nearly continuous, high quality ocean color observations from space. The Ocean Color and Temperature Scanner/Polarization and Directionality of the Earth's Reflectances (OCTS/POLDER: Nov. 1996 - Jun. 1997), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS: Sep. 1997 - present), and now the Moderate Resolution Imaging Spectrometer (MODIS: Sep. 2000 - present) have and are providing unprecedented views of chlorophyll dynamics on global scales. Global synoptic views of ocean chlorophyll were once a fantasy for ocean color scientists. It took nearly the entire 8-year lifetime of limited Coastal Zone Color Scanner (CZCS) observations to compile seasonal climatologies. Now SeaWIFS produces comparably complete fields in about 8 days. For the first time, scientists may observe spatial and temporal variability never before seen in a synoptic context. Even more exciting, we are beginning to plausibly ask questions of interannual variability. We stand at the beginning of long-time time series of ocean color, from which we may begin to ask questions of interdecadal variability and climate change. These are the scientific questions being addressed by users of the 18-year Advanced Very High Resolution Radiometer time series with respect to terrestrial processes and ocean temperatures. The nearly 5-year time series of ocean color observations now being constructed, with possibilities of continued observations, can put us at comparable standing with our terrestrial and physical oceanographic colleagues, and enable us to understand how ocean biological processes contribute to, and are affected by global climate change.

Multiple Indicator Stationary Time Series Models.

ERIC Educational Resources Information Center

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

High Resolution Time Series of Plankton Communities: From Early Warning of Harmful Blooms to Sentinels of Climate Change

NASA Astrophysics Data System (ADS)

Sosik, H. M.; Campbell, L.; Olson, R. J.

2016-02-01

The combination of ocean observatory infrastructure and automated submersible flow cytometry provides an unprecedented capability for sustained high resolution time series of plankton, including taxa that are harmful or early indicators of ecosystem response to environmental change. On-going time series produced with the FlowCytobot series of instruments document important ways this challenge is already being met for phytoplankton and microzooplankton. FlowCytobot and Imaging FlowCytobot use a combination of laser-based scattering and fluorescence measurements and video imaging of individual particles to enumerate and characterize cells ranging from picocyanobacteria to large chaining-forming diatoms. Over a decade of observations at the Martha's Vineyard Coastal Observatory (MVCO), a cabled facility on the New England Shelf, have been compiled from repeated instrument deployments, typically 6 months or longer in duration. These multi-year high resolution (hourly to daily) time series are providing new insights into dynamics of community structure such as blooms, seasonality, and multi-year trends linked to regional climate-related variables. Similar observations in Texas coastal waters at the Texas Observatory for Algal Succession Time series (TOAST) have repeatedly provided early warning of harmful algal bloom events that threaten human and ecosystem health. As coastal ocean observing systems mature and expand, the continued integration of these type of detailed observations of the plankton will provide unparalleled information about variability and patterns of change at the base of the marine food webs, with direct implications for informed ecosystem-based management.

Spatial and temporal Teleconnections of Sea Surface Temperature and Ocean Indices to regional Climate Variations across Thailand - a Pathway to understanding the Impact of Climate Change on Water Resources

NASA Astrophysics Data System (ADS)

Bejranonda, Werapol; Koch, Manfred

2010-05-01

(ENSO) events in the Pacific Ocean, if such teleconnections exist, one would have would have a powerful tool at hand to forecast extreme seasonal climate pattern across Thailand over a limited time period. Eventually, such a predictive tool would help to better manage the availability and adequate supply of surface water resources to the various water users in this country. In the present study the spatial and temporal relationships between the global climate circulation system and the regional weather in Thailand are assessed by various techniques of stochastic time series analysis. More specifically, the time series of the sea surface temperature (SST) and various ocean indices of the Pacific and the Indian Oceans, as well as the time series of 121 meteorological stations from 5 regions across Thailand which include humidity, evaporation, temperature and rainfall during 1950-2007 are examined using autocorrelation, ARIMA, Wavelet Transform methods. Possible teleconnections between the behaviour of the ocean states and the climate variations at meteorological stations in eastern Thailand which frequently suffers from water shortage problems are analyzed using regression, cross-correlation and the Wavelet cross-correlation method. In addition to the time series of the observed ocean and meteorological variables, 1961-2000 CGCM3 predictors of the macro-scale regional climate variations for this study area are analyzed by the methods above and correlated with the ocean indices as well. Rainfall and temperatures at selected stations are forecasted up to year 2007 using the teleconnection- relationships found by multiple linear regression with the CGCM3 predictors. In addition, autoregressive integrated moving average (ARIMA) models of these climate variable are set up that are eventually extended to include the ocean indices as external regressors. The results of these various statistical techniques show that the El-Niño 1.2 SST anomaly indice of the Pacific Ocean, which

OceanNOMADS: Real-time and retrospective access to operational U.S. ocean prediction products

NASA Astrophysics Data System (ADS)

Harding, J. M.; Cross, S. L.; Bub, F.; Ji, M.

2011-12-01

The National Oceanic and Atmospheric Administration (NOAA) National Operational Model Archive and Distribution System (NOMADS) provides both real-time and archived atmospheric model output from servers at the National Centers for Environmental Prediction (NCEP) and National Climatic Data Center (NCDC) respectively (http://nomads.ncep.noaa.gov/txt_descriptions/marRutledge-1.pdf). The NOAA National Ocean Data Center (NODC) with NCEP is developing a complementary capability called OceanNOMADS for operational ocean prediction models. An NCEP ftp server currently provides real-time ocean forecast output (http://www.opc.ncep.noaa.gov/newNCOM/NCOM_currents.shtml) with retrospective access through NODC. A joint effort between the Northern Gulf Institute (NGI; a NOAA Cooperative Institute) and the NOAA National Coastal Data Development Center (NCDDC; a division of NODC) created the developmental version of the retrospective OceanNOMADS capability (http://www.northerngulfinstitute.org/edac/ocean_nomads.php) under the NGI Ecosystem Data Assembly Center (EDAC) project (http://www.northerngulfinstitute.org/edac/). Complementary funding support for the developmental OceanNOMADS from U.S. Integrated Ocean Observing System (IOOS) through the Southeastern University Research Association (SURA) Model Testbed (http://testbed.sura.org/) this past year provided NODC the analogue that facilitated the creation of an NCDDC production version of OceanNOMADS (http://www.ncddc.noaa.gov/ocean-nomads/). Access tool development and storage of initial archival data sets occur on the NGI/NCDDC developmental servers with transition to NODC/NCCDC production servers as the model archives mature and operational space and distribution capability grow. Navy operational global ocean forecast subsets for U.S waters comprise the initial ocean prediction fields resident on the NCDDC production server. The NGI/NCDDC developmental server currently includes the Naval Research Laboratory Inter-America Seas

Automation of Precise Time Reference Stations (PTRS)

NASA Astrophysics Data System (ADS)

Wheeler, P. J.

1985-04-01

The U.S. Naval Observatory is presently engaged in a program of automating precise time stations (PTS) and precise time reference stations (PTBS) by using a versatile mini-computer controlled data acquisition system (DAS). The data acquisition system is configured to monitor locally available PTTI signals such as LORAN-C, OMEGA, and/or the Global Positioning System. In addition, the DAS performs local standard intercomparison. Computer telephone communications provide automatic data transfer to the Naval Observatory. Subsequently, after analysis of the data, results and information can be sent back to the precise time reference station to provide automatic control of remote station timing. The DAS configuration is designed around state of the art standard industrial high reliability modules. The system integration and software are standardized but allow considerable flexibility to satisfy special local requirements such as stability measurements, performance evaluation and printing of messages and certificates. The DAS operates completely independently and may be queried or controlled at any time with a computer or terminal device (control is protected for use by authorized personnel only). Such DAS equipped PTS are operational in Hawaii, California, Texas and Florida.

Wet tropospheric delays forecast based on Vienna Mapping Function time series analysis

NASA Astrophysics Data System (ADS)

Rzepecka, Zofia; Kalita, Jakub

2016-04-01

It is well known that the dry part of the zenith tropospheric delay (ZTD) is much easier to model than the wet part (ZTW). The aim of the research is applying stochastic modeling and prediction of ZTW using time series analysis tools. Application of time series analysis enables closer understanding of ZTW behavior as well as short-term prediction of future ZTW values. The ZTW data used for the studies were obtained from the GGOS service hold by Vienna technical University. The resolution of the data is six hours. ZTW for the years 2010 -2013 were adopted for the study. The International GNSS Service (IGS) permanent stations LAMA and GOPE, located in mid-latitudes, were admitted for the investigations. Initially the seasonal part was separated and modeled using periodic signals and frequency analysis. The prominent annual and semi-annual signals were removed using sines and consines functions. The autocorrelation of the resulting signal is significant for several days (20-30 samples). The residuals of this fitting were further analyzed and modeled with ARIMA processes. For both the stations optimal ARMA processes based on several criterions were obtained. On this basis predicted ZTW values were computed for one day ahead, leaving the white process residuals. Accuracy of the prediction can be estimated at about 3 cm.

GPS Time Series and Geodynamic Implications for the Hellenic Arc Area, Greece

NASA Astrophysics Data System (ADS)

Hollenstein, Ch.; Heller, O.; Geiger, A.; Kahle, H.-G.; Veis, G.

The quantification of crustal deformation and its temporal behavior is an important contribution to earthquake hazard assessment. With GPS measurements, especially from continuous operating stations, pre-, co-, post- and interseismic movements can be recorded and monitored. We present results of a continuous GPS network which has been operated in the Hellenic Arc area, Greece, since 1995. In order to obtain coordinate time series of high precision which are representative for crustal deformation, a main goal was to eliminate effects which are not of tectonic origin. By applying different steps of improvement, non-tectonic irregularities were reduced significantly, and the precision could be improved by an average of 40%. The improved time series are used to study the crustal movements in space and time. They serve as a base for the estimation of velocities and for the visualization of the movements in terms of trajectories. Special attention is given to large earthquakes (M>6), which occurred near GPS sites during the measuring time span.

Seasonal and annual precipitation time series trend analysis in North Carolina, United States

NASA Astrophysics Data System (ADS)

Sayemuzzaman, Mohammad; Jha, Manoj K.

2014-02-01

The present study performs the spatial and temporal trend analysis of the annual and seasonal time-series of a set of uniformly distributed 249 stations precipitation data across the state of North Carolina, United States over the period of 1950-2009. The Mann-Kendall (MK) test, the Theil-Sen approach (TSA) and the Sequential Mann-Kendall (SQMK) test were applied to quantify the significance of trend, magnitude of trend, and the trend shift, respectively. Regional (mountain, piedmont and coastal) precipitation trends were also analyzed using the above-mentioned tests. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation of precipitation data series. The application of the above-mentioned procedures has shown very notable statewide increasing trend for winter and decreasing trend for fall precipitation. Statewide mixed (increasing/decreasing) trend has been detected in annual, spring, and summer precipitation time series. Significant trends (confidence level ≥ 95%) were detected only in 8, 7, 4 and 10 nos. of stations (out of 249 stations) in winter, spring, summer, and fall, respectively. Magnitude of the highest increasing (decreasing) precipitation trend was found about 4 mm/season (- 4.50 mm/season) in fall (summer) season. Annual precipitation trend magnitude varied between - 5.50 mm/year and 9 mm/year. Regional trend analysis found increasing precipitation in mountain and coastal regions in general except during the winter. Piedmont region was found to have increasing trends in summer and fall, but decreasing trend in winter, spring and on an annual basis. The SQMK test on "trend shift analysis" identified a significant shift during 1960 - 70 in most parts of the state. Finally, the comparison between winter (summer) precipitations with the North Atlantic Oscillation (Southern Oscillation) indices concluded that the variability and trend of precipitation can be explained by the

SeaWiFS technical report series. Volume 1: An overview of SeaWiFS and ocean color

NASA Technical Reports Server (NTRS)

Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); Esaias, Wayne E.; Feldman, Gene C.; Gregg, Watson W.; Mcclain, Charles R.

1992-01-01

The purpose of this series of technical reports is to provide current documentation of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project activities, instrument performance, algorithms, and operations. This documentation is necessary to ensure that critical information related to the quality and calibration of the satellite data is available to the scientific community. SeaWiFS will bring to the ocean community a welcomed and improved renewal of the ocean color remote sensing capability lost when the Nimbus-7 Coastal Zone Color Scanner (CZCS) ceased operating in 1986. The goal of SeaWiFS, scheduled to be launched in August 1993, is to examine oceanic factors that affect global change. Because of the role of phytoplankton in the global carbon cycle, data obtained from SeaWiFS will be used to assess the ocean's role in this cycle, as well as other biogeochemical cycles. SeaWiFS data will be used to help elucidate the magnitude and variability of the annual cycle of primary production by marine phytoplankton and to determine the distribution and timing of spring blooms. The observations will help to visualize the dynamics of ocean and costal currents, the physics of mixing, and the relationships between ocean physics and large-scale patterns of productivity. The data will help fill the gap in ocean biological observations between those of the CZCS and the upcoming Moderate Resolution Imaging Spectrometer (MODIS) on the Earth Observing System-A (EOS-A) satellite.

Stochastic Models in the DORIS Position Time Series: Estimates from the IDS Contribution to the ITRF2014

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Moreaux, G.

2017-12-01

This research focuses on the investigation of the deterministic and stochastic parts of the DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) weekly coordinate time series from the IDS contribution to the ITRF2014A set of 90 stations was divided into three groups depending on when the data was collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations (these three sum up to produce the Polynomial Trend Model) and a stochastic part, all being resolved with Maximum Likelihood Estimation (MLE). We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations, meaning that the most recent data are the most reliable ones. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. We examined five different noise models to be applied to the stochastic part of the DORIS time series: a pure white noise (WN), a pure power-law noise (PL), a combination of white and power-law noise (WNPL), an autoregressive process of first order (AR(1)) and a Generalized Gauss Markov model (GGM). From our study it arises that the PL process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from AR(1) to pure PL with few stations characterized by a positive spectral index.

Time series measurements of carbon fluxes from a mangrove-dominated estuary

NASA Astrophysics Data System (ADS)

Volta, C.; Ho, D. T.; Friederich, G.; Del Castillo, C. E.; Engel, V. C.; Bhat, M.

2017-12-01

Mangrove ecosystems are among the most important and productive coastal ecosystems globally, and due to their high productivity and rapid carbon cycling, these ecosystems are important modulators of carbon fluxes from the land to the ocean and between the water and the atmosphere. Therefore, they may play a crucial role in the global carbon cycle and climate. Nonetheless, to date, estimates of carbon fluxes in mangrove-dominated estuaries are associated with large uncertainties, because studies have typically focused on limited spatial and temporal scales. For the first time, continuous time series measurements of temperature, salinity, CDOM, pH and pCO2 covering both the dry and the wet seasons were made in Shark River, a tidal estuary in the largest contiguous mangrove forest in North America. The measurements were made at two permanent stations along the estuarine domain, and allowed estimates of net dissolved carbon export from the Shark River to the Gulf of Mexico, as well as the CO2 emissions to the atmosphere to be made at seasonal and annual timescales. Results reveal that, compared to the dry season, the wet season was characterized by higher dissolved carbon export and CO2 emissions, due to meteorological, hydrological, and biogeochemical processes. Additionally, an analysis of relationships between hydrodynamic control factors (i.e. water discharge and water level) in the upstream freshwater marsh and carbon fluxes in the Shark River highlighted the importance of developing good water management strategies in the future. Finally, the study estimated the social cost of carbon fluxes in the Shark River estuary as a contribution to carbon accounting in mangrove ecosystems.

Extracting Hydrologic Understanding from the Unique Space-time Sampling of the Surface Water and Ocean Topography (SWOT) Mission

NASA Astrophysics Data System (ADS)

Nickles, C.; Zhao, Y.; Beighley, E.; Durand, M. T.; David, C. H.; Lee, H.

2017-12-01

The Surface Water and Ocean Topography (SWOT) satellite mission is jointly developed by NASA, the French space agency (CNES), with participation from the Canadian and UK space agencies to serve both the hydrology and oceanography communities. The SWOT mission will sample global surface water extents and elevations (lakes/reservoirs, rivers, estuaries, oceans, sea and land ice) at a finer spatial resolution than is currently possible enabling hydrologic discovery, model advancements and new applications that are not currently possible or likely even conceivable. Although the mission will provide global cover, analysis and interpolation of the data generated from the irregular space/time sampling represents a significant challenge. In this study, we explore the applicability of the unique space/time sampling for understanding river discharge dynamics throughout the Ohio River Basin. River network topology, SWOT sampling (i.e., orbit and identified SWOT river reaches) and spatial interpolation concepts are used to quantify the fraction of effective sampling of river reaches each day of the three-year mission. Streamflow statistics for SWOT generated river discharge time series are compared to continuous daily river discharge series. Relationships are presented to transform SWOT generated streamflow statistics to equivalent continuous daily discharge time series statistics intended to support hydrologic applications using low-flow and annual flow duration statistics.

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

PubMed

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Detecting trend on ecological river status - how to deal with short incomplete bioindicator time series? Methodological and operational issues

NASA Astrophysics Data System (ADS)

Cernesson, Flavie; Tournoud, Marie-George; Lalande, Nathalie

2018-06-01

Among the various parameters monitored in river monitoring networks, bioindicators provide very informative data. Analysing time variations in bioindicator data is tricky for water managers because the data sets are often short, irregular, and non-normally distributed. It is then a challenging methodological issue for scientists, as it is in Saône basin (30 000 km2, France) where, between 1998 and 2010, among 812 IBGN (French macroinvertebrate bioindicator) monitoring stations, only 71 time series have got more than 10 data values and were studied here. Combining various analytical tools (three parametric and non-parametric statistical tests plus a graphical analysis), 45 IBGN time series were classified as stationary and 26 as non-stationary (only one of which showing a degradation). Series from sampling stations located within the same hydroecoregion showed similar trends, while river size classes seemed to be non-significant to explain temporal trends. So, from a methodological point of view, combining statistical tests and graphical analysis is a relevant option when striving to improve trend detection. Moreover, it was possible to propose a way to summarise series in order to analyse links between ecological river quality indicators and land use stressors.

Investigation of the 16-year and 18-year ZTD Time Series Derived from GPS Data Processing

NASA Astrophysics Data System (ADS)

Bałdysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; KroszczyńSki, Krzysztof

2015-08-01

The GPS system can play an important role in activities related to the monitoring of climate. Long time series, coherent strategy, and very high quality of tropospheric parameter Zenith Tropospheric Delay (ZTD) estimated on the basis of GPS data analysis allows to investigate its usefulness for climate research as a direct GPS product. This paper presents results of analysis of 16-year time series derived from EUREF Permanent Network (EPN) reprocessing performed by the Military University of Technology. For 58 stations Lomb-Scargle periodograms were performed in order to obtain information about the oscillations in ZTD time series. Seasonal components and linear trend were estimated using Least Square Estimation (LSE) and Mann—Kendall trend test was used to confirm the presence of a linear trend designated by LSE method. In order to verify the impact of the length of time series on trend value, comparison between 16 and 18 years were performed.

Visibility Graph Based Time Series Analysis

PubMed Central

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

Visibility Graph Based Time Series Analysis.

PubMed

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Real-Time Operation of the International Space Station

NASA Astrophysics Data System (ADS)

Suffredini, M. T.

2002-01-01

The International Space Station is on orbit and real-time operations are well underway. Along with the assembly challenges of building and operating the International Space Station , scientific activities are also underway. Flight control teams in three countries are working together as a team to plan, coordinate and command the systems on the International Space Station.Preparations are being made to add the additional International Partner elements including their operations teams and facilities. By October 2002, six Expedition crews will have lived on the International Space Station. Management of real-time operations has been key to these achievements. This includes the activities of ground teams in control centers around the world as well as the crew on orbit. Real-time planning is constantly challenged with balancing the requirements and setting the priorities for the assembly, maintenance, science and crew health functions on the International Space Station. It requires integrating the Shuttle, Soyuz and Progress requirements with the Station. It is also necessary to be able to respond in case of on-orbit anomalies and to set plans and commands in place to ensure the continues safe operation of the Station. Bringing together the International Partner operations teams has been challenging and intensely rewarding. Utilization of the assets of each partner has resulted in efficient solutions to problems. This paper will describe the management of the major real-time operations processes, significant achievements, and future challenges.

Unraveling multiple changes in complex climate time series using Bayesian inference

NASA Astrophysics Data System (ADS)

Berner, Nadine; Trauth, Martin H.; Holschneider, Matthias

2016-04-01

Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of observations. Unraveling such transitions yields essential information for the understanding of the observed system. The precise detection and basic characterization of underlying changes is therefore of particular importance in environmental sciences. We present a kernel-based Bayesian inference approach to investigate direct as well as indirect climate observations for multiple generic transition events. In order to develop a diagnostic approach designed to capture a variety of natural processes, the basic statistical features of central tendency and dispersion are used to locally approximate a complex time series by a generic transition model. A Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of such a transition. To systematically investigate time series for multiple changes occurring at different temporal scales, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. Thus, based on a generic transition model a probability expression is derived that is capable to indicate multiple changes within a complex time series. We discuss the method's performance by investigating direct and indirect climate observations. The approach is applied to environmental time series (about 100 a), from the weather station in Tuscaloosa, Alabama, and confirms documented instrumentation changes. Moreover, the approach is used to investigate a set of complex terrigenous dust records from the ODP sites 659, 721/722 and 967 interpreted as climate indicators of the African region of the Plio-Pleistocene period (about 5 Ma). The detailed inference unravels multiple transitions underlying the indirect climate observations coinciding with established

The Ocean's Abyssal Mass Flux Sustained Primarily By the Wind: Vector Correlation of Time Series in Upper and Abyssal Layers

NASA Astrophysics Data System (ADS)

Hancock, L. O.

2003-12-01

As Wunsch has recently noted (2002), use of the term "thermohaline circulation" is muddled. The term is used with at least seven inconsistent meanings, among them abyssal circulation, the circulation driven by density and pressure differences in the deep ocean, the global conveyor, and at least four others. The use of a single term for all these concepts can create an impression that an understanding exists whereby in various combinations the seven meanings have been demonstrated to mean the same thing. But that is not the case. A particularly important consequence of the muddle is the way in which abyssal circulation is sometimes taken to be driven mostly or entirely by temperature and density differences, and equivalent to the global conveyor. But in fact the distinction between abyssal and upper-layer circulation has not been measured. To find out whether available data justifies a distinction between the upper-layer and abyssal circulations, this study surveyed velocity time series obtained by deep current meter moorings. Altogether, 114 moorings were identified, drawn from about three dozen experiments worldwide over the period 1973-1996, each of which deployed current meters in both the upper (2003750) layers. For each pair of current meters, the Kundu and Crosby measures of vector correlation were estimated, as well as coherences for periods from 10 to 60 days. In the North Atlantic, for example, Kundu vector correlation (50-day window): 0.48 +/- .03 Crosby vector correlation (absolute value, 50 day window): 0.46 +/- .07 Coherence at 60 days: .36 +/- .07 - at 30 days: 0.40 +/- .06 - at 10 days: 0.22 +/- .05 Most figures for the South Atlantic, Pacific and Southern Oceans are similar. Those obtained in the Indian Ocean or near the Equator are somewhat different. The statistics obtained here are consistent with the work of Wunsch (1997), and tend to confirm Wunsch's result that current velocities at depth are linked with those in the

Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

NASA Astrophysics Data System (ADS)

Lück, Christina; Kusche, Jürgen; Rietbroek, Roelof; Löcher, Anno

2018-03-01

Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces

Impact of change in climate and policy from 1988 to 2007 on environmental and microbial variables at the time series station Boknis Eck, Baltic Sea

NASA Astrophysics Data System (ADS)

Hoppe, H.-G.; Giesenhagen, H. C.; Koppe, R.; Hansen, H.-P.; Gocke, K.

2013-07-01

Phytoplankton and bacteria are sensitive indicators of environmental change. The temporal development of these key organisms was monitored from 1988 to the end of 2007 at the time series station Boknis Eck in the western Baltic Sea. This period was characterized by the adaption of the Baltic Sea ecosystem to changes in the environmental conditions caused by the conversion of the political system in the southern and eastern border states, accompanied by the general effects of global climate change. Measured variables were chlorophyll, primary production, bacteria number, -biomass and -production, glucose turnover rate, macro-nutrients, pH, temperature and salinity. Negative trends with time were recorded for chlorophyll, bacteria number, bacterial biomass and bacterial production, nitrate, ammonia, phosphate, silicate, oxygen and salinity while temperature, pH, and the ratio between bacteria numbers and chlorophyll increased. Strongest reductions with time occurred for the annual maximum values, e.g. for chlorophyll during the spring bloom or for nitrate during winter, while the annual minimum values remained more stable. In deep water above sediment the negative trends of oxygen, nitrate, phosphate and bacterial variables as well as the positive trend of temperature were similar to those in the surface while the trends of salinity, ammonia and silicate were opposite to those in the surface. Decreasing oxygen, even in the surface layer, was of particular interest because it suggested enhanced recycling of nutrients from the deep hypoxic zones to the surface by vertical mixing. The long-term seasonal patterns of all variables correlated positively with temperature, except chlorophyll and salinity. Salinity correlated negatively with all bacterial variables (as well as precipitation) and positively with chlorophyll. Surprisingly, bacterial variables did not correlate with chlorophyll, which may be inherent with the time lag between the peaks of phytoplankton and

Impact of change in climate and policy from 1988 to 2007 on environmental and microbial variables at the time series station Boknis Eck, Baltic Sea

NASA Astrophysics Data System (ADS)

Hoppe, H.-G.; Giesenhagen, H. C.; Koppe, R.; Hansen, H.-P.; Gocke, K.

2012-12-01

Phytoplankton and bacteria are sensitive indicators of environmental change. The temporal development of these key organisms was monitored from 1988 to the end of 2007 at the time series station Boknis Eck in the Western Baltic Sea. This period was characterized by the adaption of the Baltic Sea ecosystem to changes in the environmental conditions caused by the collapse and conversion of the political system in the Southern and Eastern Border States, accompanied by the general effects of global climate change. Measured variables were chlorophyll, primary production, bacteria number, -biomass and -production, glucose turnover rate, macro-nutrients, pH, temperature and salinity. Negative trends with time were recorded for chlorophyll, the bacterial variables, nitrate, ammonia, phosphate, silicate, oxygen and salinity while temperature, pH, and the ratio between bacteria numbers and chlorophyll increased. The strongest reductions with time occurred for the annual maximum values, e.g. for chlorophyll during the spring bloom or for nitrate during winter, while the annual minimum values remained more stable. In deep water above sediment the negative trends of oxygen, nitrate, phosphate and bacterial variables as well as the positive trend of temperature were similar to those in the surface while the trends of salinity, ammonia and silicate were opposite to those in the surface. Decreasing oxygen even in the surface layer was of particular interest because it suggested enhanced recycling of nutrients from the deep hypoxic zones to the surface by vertical mixing. In the long run all variables correlated positively with temperature, except chlorophyll and salinity. Salinity correlated negatively with all bacterial variables as well as precipitation and positively with chlorophyll. Surprisingly, bacterial variables did not correlate with chlorophyll which may be inherent with the time lag between the peaks of phytoplankton and bacteria during spring. Compared to the 20-yr

Working Towards Deep-Ocean Temperature Monitoring by Studying the Acoustic Ambient Noise Field in the South Pacific Ocean

NASA Astrophysics Data System (ADS)

Sambell, K.; Evers, L. G.; Snellen, M.

2017-12-01

Deriving the deep-ocean temperature is a challenge. In-situ observations and satellite observations are hardly applicable. However, knowledge about changes in the deep ocean temperature is important in relation to climate change. Oceans are filled with low-frequency sound waves created by sources such as underwater volcanoes, earthquakes and seismic surveys. The propagation of these sound waves is temperature dependent and therefore carries valuable information that can be used for temperature monitoring. This phenomenon is investigated by applying interferometry to hydroacoustic data measured in the South Pacific Ocean. The data is measured at hydrophone station H03 which is part of the International Monitoring System (IMS). This network consists of several stations around the world and is in place for the verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The station consists of two arrays located north and south of Robinson Crusoe Island separated by 50 km. Both arrays consist of three hydrophones with an intersensor distance of 2 km located at a depth of 1200 m. This depth is in range of the SOFAR channel. Hydroacoustic data measured at the south station is cross-correlated for the time period 2014-2017. The results are improved by applying one-bit normalization as a preprocessing step. Furthermore, beamforming is applied to the hydroacoustic data in order to characterize ambient noise sources around the array. This shows the presence of a continuous source at a backazimuth between 180 and 200 degrees throughout the whole time period, which is in agreement with the results obtained by cross-correlation. Studies on source strength show a seasonal dependence. This is an indication that the sound is related to acoustic activity in Antarctica. Results on this are supported by acoustic propagation modeling. The normal mode technique is used to study the sound propagation from possible source locations towards station H03.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Seasonal and long-term changes in elemental concentrations and ratios of marine particulate organic matter

NASA Astrophysics Data System (ADS)

Talarmin, Agathe; Lomas, Michael W.; Bozec, Yann; Savoye, Nicolas; Frigstad, Helene; Karl, David M.; Martiny, Adam C.

2016-11-01

What is the temporal variability of the elemental stoichiometry of marine microbial communities across ocean regions? To answer this question, we present an analysis of environmental conditions, particulate organic carbon, nitrogen, and phosphorus concentrations and their ratios across 20 time series (3-25 years duration) representing estuarine, coastal, and open ocean environments. The majority of stations showed significant seasonal oscillations in particulate organic elemental concentrations and ratios. However, shorter-term changes contributed most to overall variance in particulate organic matter concentrations and ratios. We found a correlation between the seasonal oscillations of environmental conditions and elemental ratios at many coastal but not open ocean and estuarine stations. C:N peaked near the seasonal temperature minimum and nutrient maximum, but some stations showed other seasonal links. C:N ratios declined with time over the respective observation periods at all open ocean and estuarine stations as well as at five coastal station but increased at the nine other coastal stations. C:P (but not N:P) declined slightly at Bermuda Atlantic Time-series Study but showed large significant increases at Hawaii Ocean Time-series and Arendal stations. The relationships between long-term changes in environmental conditions and particulate organic matter concentrations or ratios were ambiguous, but interactions between changes in temperature and nutrient availability were important. Overall, our analysis demonstrates significant changes in elemental ratios at long-term and seasonal time scales across regions, but the underlying mechanisms are currently unclear. Thus, we need to better understand the detailed mechanisms driving the elemental composition of marine microbial ecosystems in order to predict how oceans will respond to environmental changes.

Time-Series Photographs of the Sea Floor in Western Massachusetts Bay: June 1998 to May 1999

USGS Publications Warehouse

Butman, Bradford; Alexander, P. Soupy; Bothner, Michael H.

2004-01-01

This report presents time-series photographs of the sea floor obtained from an instrumented tripod deployed at Site A in western Massachusetts Bay (42? 22.6' N., 70? 47.0' W., 30 m water depth, figure 1) from June 1998 through May 1999. Site A is approximately 1 km south of an ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. Time-series photographs and oceanographic observations were initiated at Site A in December 1989 and are anticipated to continue to September 2005. This one of a series of reports that present these images in digital form. The objective of these reports is to enable easy and rapid viewing of the photographs and to provide a medium-resolution digital archive. The images, obtained every 4 hours, are presented as a movie (in .avi format, which may be viewed using an image viewer such as QuickTime or Windows Media Player) and as individual images (.tif format). The images provide time-series observations of changes of the sea floor and near-bottom water properties.

Residual settlements detection of ocean reclaimed lands with multi-platform SAR time series and SBAS technique: a case study of Shanghai Pudong International Airport

NASA Astrophysics Data System (ADS)

Yu, Lei; Yang, Tianliang; Zhao, Qing; Pepe, Antonio; Dong, Hongbin; Sun, Zhibin

2017-09-01

Shanghai Pudong International airport is one of the three major international airports in China. The airport is located at the Yangtze estuary which is a sensitive belt of sea and land interaction region. The majority of the buildings and facilities in the airport are built on ocean-reclaimed lands and silt tidal flat. Residual ground settlement could probably occur after the completion of the airport construction. The current status of the ground settlement of the airport and whether it is within a safe range are necessary to be investigated. In order to continuously monitor the ground settlement of the airport, two Synthetic Aperture Radar (SAR) time series, acquired by X-band TerraSAR-X (TSX) and TanDEM-X (TDX) sensors from December 2009 to December 2010 and from April 2013 to July 2015, were used for analyzing with SBAS technique. We firstly obtained ground deformation measurement of each SAR subset. Both of the measurements show that obvious ground subsidence phenomenon occurred at the airport, especially in the second runway, the second terminal, the sixth cargo plane and the eighth apron. The maximum vertical ground deformation rates of both SAR subset measurements were greater than -30 mm/year, while the cumulative ground deformations reached up to -30 mm and -35 mm respectively. After generation of SBAS-retrieved ground deformation for each SAR subset, we performed a joint analysis to combine time series of each common coherent point by applying a geotechnical model. The results show that three centralized areas of ground deformation existed in the airport, mainly distributed in the sixth cargo plane, the fifth apron and the fourth apron, The maximum vertical cumulative ground subsidence was more than -70 mm. In addition, by analyzing the combined time series of four selected points, we found that the ground deformation rates of the points located at the second runway, the third runway, and the second terminal, were progressively smaller as time goes by

Real-time source deformation modeling through GNSS permanent stations at Merapi volcano (Indonesia

NASA Astrophysics Data System (ADS)

Beauducel, F.; Nurnaning, A.; Iguchi, M.; Fahmi, A. A.; Nandaka, M. A.; Sumarti, S.; Subandriyo, S.; Metaxian, J. P.

2014-12-01

Mt. Merapi (Java, Indonesia) is one of the most active and dangerous volcano in the world. A first GPS repetition network was setup and periodically measured since 1993, allowing detecting a deep magma reservoir, quantifying magma flux in conduit and identifying shallow discontinuities around the former crater (Beauducel and Cornet, 1999;Beauducel et al., 2000, 2006). After the 2010 centennial eruption, when this network was almost completely destroyed, Indonesian and Japanese teams installed a new continuous GPS network for monitoring purpose (Iguchi et al., 2011), consisting of 3 stations located at the volcano flanks, plus a reference station at the Yogyakarta Observatory (BPPTKG).In the framework of DOMERAPI project (2013-2016) we have completed this network with 5 additional stations, which are located on the summit area and volcano surrounding. The new stations are 1-Hz sampling, GNSS (GPS + GLONASS) receivers, and near real-time data streaming to the Observatory. An automatic processing has been developed and included in the WEBOBS system (Beauducel et al., 2010) based on GIPSY software computing precise daily moving solutions every hour, and for different time scales (2 months, 1 and 5 years), time series and velocity vectors. A real-time source modeling estimation has also been implemented. It uses the depth-varying point source solution (Mogi, 1958; Williams and Wadge, 1998) in a systematic inverse problem model exploration that displays location, volume variation and 3-D probability map.The operational system should be able to better detect and estimate the location and volume variations of possible magma sources, and to follow magma transfer towards the surface. This should help monitoring and contribute to decision making during future unrest or eruption.

Variability of the Denmark Strait overflow: Moored time series from 1996-2011

NASA Astrophysics Data System (ADS)

Jochumsen, Kerstin; Quadfasel, Detlef; Valdimarsson, Heã°Inn; Jónsson, SteingríMur

2012-12-01

The Denmark Strait overflow provides about half of the total dense water overflow from the Nordic Seas into the North Atlantic Ocean. The velocity of the overflow has been monitored in the Strait with two moored Acoustic Doppler Current Profilers since 1996 with several interruptions due to mooring losses or instrument failure. So far, overflow transports were only calculated when data from both moorings were available. In this work, we introduce a linear model to fill gaps in the time series when data from only one instrument is available. The mean overflow transport is 3.4 Sv and exhibits a variance of 2.0 Sv2. No significant trend was detected in the time series. The highest variability in the transport is associated with the passage of mesoscale eddies with time scales of 2-10 days (associated with a variance of 1.5 Sv2). Seasonal variability is weak and explains less than 5% of the variance in all time series, which is in contrast to the strong seasonal cycle found in high resolution model simulations. Interannual variability is on the order of 10% of the mean. A relation to atmospheric forcing such as the local wind stress curl, as well as to larger scale phenomena, e.g. the North Atlantic Oscillation, is not detected. Since 2005 data from moored temperature, conductivity and pressure recorders have been available as well, monitoring the hydrographic variability at the bottom of Denmark Strait. In recent years the temperature time series of the Denmark Strait overflow revealed a cooling, while the salinity stayed nearly constant.

Global declines in oceanic nitrification rates as a consequence of ocean acidification

PubMed Central

Beman, J. Michael; Chow, Cheryl-Emiliane; King, Andrew L.; Feng, Yuanyuan; Fuhrman, Jed A.; Andersson, Andreas; Bates, Nicholas R.; Popp, Brian N.; Hutchins, David A.

2011-01-01

Ocean acidification produced by dissolution of anthropogenic carbon dioxide (CO2) emissions in seawater has profound consequences for marine ecology and biogeochemistry. The oceans have absorbed one-third of CO2 emissions over the past two centuries, altering ocean chemistry, reducing seawater pH, and affecting marine animals and phytoplankton in multiple ways. Microbially mediated ocean biogeochemical processes will be pivotal in determining how the earth system responds to global environmental change; however, how they may be altered by ocean acidification is largely unknown. We show here that microbial nitrification rates decreased in every instance when pH was experimentally reduced (by 0.05–0.14) at multiple locations in the Atlantic and Pacific Oceans. Nitrification is a central process in the nitrogen cycle that produces both the greenhouse gas nitrous oxide and oxidized forms of nitrogen used by phytoplankton and other microorganisms in the sea; at the Bermuda Atlantic Time Series and Hawaii Ocean Time-series sites, experimental acidification decreased ammonia oxidation rates by 38% and 36%. Ammonia oxidation rates were also strongly and inversely correlated with pH along a gradient produced in the oligotrophic Sargasso Sea (r2 = 0.87, P < 0.05). Across all experiments, rates declined by 8–38% in low pH treatments, and the greatest absolute decrease occurred where rates were highest off the California coast. Collectively our results suggest that ocean acidification could reduce nitrification rates by 3–44% within the next few decades, affecting oceanic nitrous oxide production, reducing supplies of oxidized nitrogen in the upper layers of the ocean, and fundamentally altering nitrogen cycling in the sea. PMID:21173255

Global declines in oceanic nitrification rates as a consequence of ocean acidification.

PubMed

Beman, J Michael; Chow, Cheryl-Emiliane; King, Andrew L; Feng, Yuanyuan; Fuhrman, Jed A; Andersson, Andreas; Bates, Nicholas R; Popp, Brian N; Hutchins, David A

2011-01-04

Ocean acidification produced by dissolution of anthropogenic carbon dioxide (CO(2)) emissions in seawater has profound consequences for marine ecology and biogeochemistry. The oceans have absorbed one-third of CO(2) emissions over the past two centuries, altering ocean chemistry, reducing seawater pH, and affecting marine animals and phytoplankton in multiple ways. Microbially mediated ocean biogeochemical processes will be pivotal in determining how the earth system responds to global environmental change; however, how they may be altered by ocean acidification is largely unknown. We show here that microbial nitrification rates decreased in every instance when pH was experimentally reduced (by 0.05-0.14) at multiple locations in the Atlantic and Pacific Oceans. Nitrification is a central process in the nitrogen cycle that produces both the greenhouse gas nitrous oxide and oxidized forms of nitrogen used by phytoplankton and other microorganisms in the sea; at the Bermuda Atlantic Time Series and Hawaii Ocean Time-series sites, experimental acidification decreased ammonia oxidation rates by 38% and 36%. Ammonia oxidation rates were also strongly and inversely correlated with pH along a gradient produced in the oligotrophic Sargasso Sea (r(2) = 0.87, P < 0.05). Across all experiments, rates declined by 8-38% in low pH treatments, and the greatest absolute decrease occurred where rates were highest off the California coast. Collectively our results suggest that ocean acidification could reduce nitrification rates by 3-44% within the next few decades, affecting oceanic nitrous oxide production, reducing supplies of oxidized nitrogen in the upper layers of the ocean, and fundamentally altering nitrogen cycling in the sea.

Dissolved organic nitrogen dynamics in the North Sea: A time series analysis (1995-2005)

NASA Astrophysics Data System (ADS)

Van Engeland, T.; Soetaert, K.; Knuijt, A.; Laane, R. W. P. M.; Middelburg, J. J.

2010-09-01

Dissolved organic nitrogen (DON) dynamics in the North Sea was explored by means of long-term time series of nitrogen parameters from the Dutch national monitoring program. Generally, the data quality was good with little missing data points. Different imputation methods were used to verify the robustness of the patterns against these missing data. No long-term trends in DON concentrations were found over the sampling period (1995-2005). Inter-annual variability in the different time series showed both common and station-specific behavior. The stations could be divided into two regions, based on absolute concentrations and the dominant times scales of variability. Average DON concentrations were 11 μmol l -1 in the coastal region and 5 μmol l -1 in the open sea. Organic fractions of total dissolved nitrogen (TDN) averaged 38 and 71% in the coastal zone and open sea, respectively, but increased over time due to decreasing dissolved inorganic nitrogen (DIN) concentrations. In both regions intra-annual variability dominated over inter-annual variability, but DON variation in the open sea was markedly shifted towards shorter time scales relative to coastal stations. In the coastal zone a consistent seasonal DON cycle existed with high values in spring-summer and low values in autumn-winter. In the open sea seasonality was weak. A marked shift in the seasonality was found at the Dogger Bank, with DON accumulation towards summer and low values in winter prior to 1999, and accumulation in spring and decline throughout summer after 1999. This study clearly shows that DON is a dynamic actor in the North Sea and should be monitored systematically to enable us to understand fully the functioning of this ecosystem.

Timing of the departure of ocean biogeochemical cycles from the preindustrial state.

PubMed

Christian, James R

2014-01-01

Changes in ocean chemistry and climate induced by anthropogenic CO2 affect a broad range of ocean biological and biogeochemical processes; these changes are already well underway. Direct effects of CO2 (e.g. on pH) are prominent among these, but climate model simulations with historical greenhouse gas forcing suggest that physical and biological processes only indirectly forced by CO2 (via the effect of atmospheric CO2 on climate) begin to show anthropogenically-induced trends as early as the 1920s. Dates of emergence of a number of representative ocean fields from the envelope of natural variability are calculated for global means and for spatial 'fingerprints' over a number of geographic regions. Emergence dates are consistent among these methods and insensitive to the exact choice of regions, but are generally earlier with more spatial information included. Emergence dates calculated for individual sampling stations are more variable and generally later, but means across stations are generally consistent with global emergence dates. The last sign reversal of linear trends calculated for periods of 20 or 30 years also functions as a diagnostic of emergence, and is generally consistent with other measures. The last sign reversal among 20 year trends is found to be a conservative measure (biased towards later emergence), while for 30 year trends it is found to have an early emergence bias, relative to emergence dates calculated by departure from the preindustrial mean. These results are largely independent of emission scenario, but the latest-emerging fields show a response to mitigation. A significant anthropogenic component of ocean variability has been present throughout the modern era of ocean observation.

Timing of the Departure of Ocean Biogeochemical Cycles from the Preindustrial State

PubMed Central

Christian, James R.

2014-01-01

Changes in ocean chemistry and climate induced by anthropogenic CO2 affect a broad range of ocean biological and biogeochemical processes; these changes are already well underway. Direct effects of CO2 (e.g. on pH) are prominent among these, but climate model simulations with historical greenhouse gas forcing suggest that physical and biological processes only indirectly forced by CO2 (via the effect of atmospheric CO2 on climate) begin to show anthropogenically-induced trends as early as the 1920s. Dates of emergence of a number of representative ocean fields from the envelope of natural variability are calculated for global means and for spatial ‘fingerprints’ over a number of geographic regions. Emergence dates are consistent among these methods and insensitive to the exact choice of regions, but are generally earlier with more spatial information included. Emergence dates calculated for individual sampling stations are more variable and generally later, but means across stations are generally consistent with global emergence dates. The last sign reversal of linear trends calculated for periods of 20 or 30 years also functions as a diagnostic of emergence, and is generally consistent with other measures. The last sign reversal among 20 year trends is found to be a conservative measure (biased towards later emergence), while for 30 year trends it is found to have an early emergence bias, relative to emergence dates calculated by departure from the preindustrial mean. These results are largely independent of emission scenario, but the latest-emerging fields show a response to mitigation. A significant anthropogenic component of ocean variability has been present throughout the modern era of ocean observation. PMID:25386910

Real time wave forecasting using wind time history and numerical model

NASA Astrophysics Data System (ADS)

Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Marine microbes in 4D-using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health.

PubMed

Buttigieg, Pier Luigi; Fadeev, Eduard; Bienhold, Christina; Hehemann, Laura; Offre, Pierre; Boetius, Antje

2018-02-21

Microbial observation is of high relevance in assessing marine phenomena of scientific and societal concern including ocean productivity, harmful algal blooms, and pathogen exposure. However, we have yet to realise its potential to coherently and comprehensively report on global ocean status. The ability of satellites to monitor the distribution of phytoplankton has transformed our appreciation of microbes as the foundation of key ecosystem services; however, more in-depth understanding of microbial dynamics is needed to fully assess natural and anthropogenically induced variation in ocean ecosystems. While this first synthesis shows that notable efforts exist, vast regions such as the ocean depths, the open ocean, the polar oceans, and most of the Southern Hemisphere lack consistent observation. To secure a coordinated future for a global microbial observing system, existing long-term efforts must be better networked to generate shared bioindicators of the Global Ocean's state and health. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Continuous pCO2 time series from Ocean Networks Canada cabled observatories at the northeast Pacific shelf edge and in the sub-tidal Arctic

NASA Astrophysics Data System (ADS)

Juniper, S. Kim; Sastri, Akash; Mihaly, Steven; Duke, Patrick; Else, Brent; Thomas, Helmuth; Miller, Lisa

2017-04-01

Marine pCO2 sensor technology has progressed to the point where months-long time series from remotely-deployed pCO2 sensors can be used to document seasonal and higher frequency variability in pCO2 and its relationship to oceanographic processes. Ocean Networks Canada recently deployed pCO2 sensors on two cabled platforms: a bottom-moored (400 m depth), vertical profiler at the edge of the northeast Pacific continental shelf off Vancouver Island, Canada, and a subtidal seafloor platform in the Canadian High Arctic (69˚ N) at Cambridge Bay, Nunavut. Both platforms streamed continuous data to a shore-based archive from Pro-Oceanus pCO2 sensors and other oceanographic instruments. The vertical profiler time series revealed substantial intrusions of corrosive (high CO2/low O2), saltier, colder water masses during the summertime upwelling season and during winter-time reversals of along-slope currents. Step-wise profiles during the downcast provided the most reliable pCO2 data, permitting the sensor to equilibrate to the broad range of pCO2 concentrations encountered over the 400 metre depth interval. The Arctic pCO2 sensor was deployed in August 2015. Reversing seasonal trends in pCO2 and dissolved oxygen values can be related to the changing balance of photosynthesis and respiration under sea ice, as influenced by irradiance. Correlation of pCO2 and dissolved oxygen sensor data and the collection of calibration samples have permitted evaluation of sensor performance in relation to operational conditions encountered in vertical profiling and lengthy exposure to subzero seawater.

GRACE RL03-v2 monthly time series of solutions from CNES/GRGS

NASA Astrophysics Data System (ADS)

Lemoine, Jean-Michel; Bourgogne, Stéphane; Bruinsma, Sean; Gégout, Pascal; Reinquin, Franck; Biancale, Richard

2015-04-01

Based on GRACE GPS and KBR Level-1B.v2 data, as well as on LAGEOS-1/2 SLR data, CNES/GRGS has published in 2014 the third full re-iteration of its GRACE gravity field solutions. This monthly time series of solutions, named RL03-v1, complete to spherical harmonics degree/order 80, has displayed interesting performances in terms of spatial resolution and signal amplitude compared to JPL/GFZ/CSR RL05. This is due to a careful selection of the background models (FES2014 ocean tides, ECMWF ERA-interim (atmosphere) and TUGO (non IB-ocean) "dealiasing" models every 3 hours) and to the choice of an original method for gravity field inversion : truncated SVD. Identically to the previous CNES/GRGS releases, no additional filtering of the solutions is necessary before using them. Some problems have however been identified in CNES/GRGS RL03-v1: - an erroneous mass signal located in two small circular rings close to the Earth's poles, leading to the recommendation not to use RL03-v1 above 82° latitudes North and South; - a weakness in the sectorials due to an excessive downweighting of the GRACE GPS observations. These two problems have been understood and addressed, leading to the computation of a corrected time series of solutions, RL03-v2. The corrective steps have been: - to strengthen the determination of the very low degrees by adding Starlette and Stella SLR data to the normal equations; - to increase the weight of the GRACE GPS observations; - to adopt a two steps approach for the computation of the solutions: first a Choleski inversion for the low degrees, followed by a truncated SVD solution. The identification of these problems will be discussed and the performance of the new time series evaluated.

Smoothing of climate time series revisited

NASA Astrophysics Data System (ADS)

Mann, Michael E.

2008-08-01

We present an easily implemented method for smoothing climate time series, generalizing upon an approach previously described by Mann (2004). The method adaptively weights the three lowest order time series boundary constraints to optimize the fit with the raw time series. We apply the method to the instrumental global mean temperature series from 1850-2007 and to various surrogate global mean temperature series from 1850-2100 derived from the CMIP3 multimodel intercomparison project. These applications demonstrate that the adaptive method systematically out-performs certain widely used default smoothing methods, and is more likely to yield accurate assessments of long-term warming trends.

A Review of Subsequence Time Series Clustering

PubMed Central

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A review of subsequence time series clustering.

PubMed

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

Estimation of time-series properties of gourd observed solar irradiance data using cloud properties derived from satellite observations

NASA Astrophysics Data System (ADS)

Watanabe, T.; Nohara, D.

2017-12-01

The shorter temporal scale variation in the downward solar irradiance at the ground level (DSI) is not understood well because researches in the shorter-scale variation in the DSI is based on the ground observation and ground observation stations are located coarsely. Use of dataset derived from satellite observation will overcome such defect. DSI data and MODIS cloud properties product are analyzed simultaneously. Three metrics: mean, standard deviation and sample entropy, are used to evaluate time-series properties of the DSI. Three metrics are computed from two-hours time-series centered at the observation time of MODIS over the ground observation stations. We apply the regression methods to design prediction models of each three metrics from cloud properties. The validation of the model accuracy show that mean and standard deviation are predicted with a higher degree of accuracy and that the accuracy of prediction of sample entropy, which represents the complexity of time-series, is not high. One of causes of lower prediction skill of sample entropy is the resolution of the MODIS cloud properties. Higher sample entropy is corresponding to the rapid fluctuation, which is caused by the small and unordered cloud. It seems that such clouds isn't retrieved well.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

A regressive methodology for estimating missing data in rainfall daily time series

NASA Astrophysics Data System (ADS)

Barca, E.; Passarella, G.

2009-04-01

The "presence" of gaps in environmental data time series represents a very common, but extremely critical problem, since it can produce biased results (Rubin, 1976). Missing data plagues almost all surveys. The problem is how to deal with missing data once it has been deemed impossible to recover the actual missing values. Apart from the amount of missing data, another issue which plays an important role in the choice of any recovery approach is the evaluation of "missingness" mechanisms. When data missing is conditioned by some other variable observed in the data set (Schafer, 1997) the mechanism is called MAR (Missing at Random). Otherwise, when the missingness mechanism depends on the actual value of the missing data, it is called NCAR (Not Missing at Random). This last is the most difficult condition to model. In the last decade interest arose in the estimation of missing data by using regression (single imputation). More recently multiple imputation has become also available, which returns a distribution of estimated values (Scheffer, 2002). In this paper an automatic methodology for estimating missing data is presented. In practice, given a gauging station affected by missing data (target station), the methodology checks the randomness of the missing data and classifies the "similarity" between the target station and the other gauging stations spread over the study area. Among different methods useful for defining the similarity degree, whose effectiveness strongly depends on the data distribution, the Spearman correlation coefficient was chosen. Once defined the similarity matrix, a suitable, nonparametric, univariate, and regressive method was applied in order to estimate missing data in the target station: the Theil method (Theil, 1950). Even though the methodology revealed to be rather reliable an improvement of the missing data estimation can be achieved by a generalization. A first possible improvement consists in extending the univariate technique to

An Autonomous Ozone Instrument for Atmospheric Measurements from Ocean Buoys

NASA Astrophysics Data System (ADS)

Hintsa, E. J.; Rawlins, W. T.; Sholkovitz, E. R.; Hosom, D. S.; Allsup, G. P.; Purcell, M. J.; Scott, D. R.; Mulhall, P.

2002-05-01

Tropospheric ozone is an oxidant, a greenhouse gas, and a pollutant. Because of its adverse health effects, there are numerous monitoring stations on land but none over the oceans. We have built an ozone instrument for deployment anywhere at sea from ocean buoys, to study ozone chemistry over the oceans, intercontinental transport of pollution, diurnal and seasonal cycles of ozone, and to make baseline and long-term time series measurements of ozone in remote locations. The instrument uses direct (Beer's Law) absorption of UV radiation in a dual-path cell, with ambient and ozone-free air alternately switched between the two paths, to measure ozone. Ozone can be measured at a rate of 1 Hz, with a precision of about 1 ppb at sea level. The air inlet and outlet have valves which close automatically under high wind conditions or rain to protect the ozone sensor. The instrument has been packaged for deployment at sea, and tested on a 3-meter discus buoy with other instruments in coastal waters in fall 2001. It can operate autonomously or be controlled via line-of-sight modem or a satellite link. We will present the details of the instrument, and laboratory and buoy test data from its first deployment, including a comparison with a nearby ozone monitoring station on land. We will also present an evaluation of the instrument's performance and describe plans for improvements. In summer 2002, the ozone measurement system will be operated at the Martha's Vineyard Coastal Observatory; in the future we anticipate deploying on the Bermuda Testbed Mooring, followed by use on the open ocean to measure long-range transport of ozone.

Space Station

NASA Image and Video Library

1952-01-01

This is a von Braun 1952 space station concept. In a 1952 series of articles written in Collier's, Dr. Wernher von Braun, then Technical Director of the Army Ordnance Guided Missiles Development Group at Redstone Arsenal, wrote of a large wheel-like space station in a 1,075-mile orbit. This station, made of flexible nylon, would be carried into space by a fully reusable three-stage launch vehicle. Once in space, the station's collapsible nylon body would be inflated much like an automobile tire. The 250-foot-wide wheel would rotate to provide artificial gravity, an important consideration at the time because little was known about the effects of prolonged zero-gravity on humans. Von Braun's wheel was slated for a number of important missions: a way station for space exploration, a meteorological observatory and a navigation aid. This concept was illustrated by artist Chesley Bonestell.

Time series with tailored nonlinearities

NASA Astrophysics Data System (ADS)

Räth, C.; Laut, I.

2015-10-01

It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

Trend analysis of air temperature and precipitation time series over Greece: 1955-2010

NASA Astrophysics Data System (ADS)

Marougianni, G.; Melas, D.; Kioutsioukis, I.; Feidas, H.; Zanis, P.; Anandranistakis, E.

2012-04-01

In this study, a database of air temperature and precipitation time series from the network of Hellenic National Meteorological Service has been developed in the framework of the project GEOCLIMA, co-financed by the European Union and Greek national funds through the Operational Program "Competitiveness and Entrepreneurship" of the Research Funding Program COOPERATION 2009. Initially, a quality test was applied to the raw data and then missing observations have been imputed with a regularized, spatial-temporal expectation - maximization algorithm to complete the climatic record. Next, a quantile - matching algorithm was applied in order to verify the homogeneity of the data. The processed time series were used for the calculation of temporal annual and seasonal trends of air temperature and precipitation. Monthly maximum and minimum surface air temperature and precipitation means at all available stations in Greece were analyzed for temporal trends and spatial variation patterns for the longest common time period of homogenous data (1955 - 2010), applying the Mann-Kendall test. The majority of the examined stations showed a significant increase in the summer maximum and minimum temperatures; this could be possibly physically linked to the Etesian winds, because of the less frequent expansion of the low over the southeastern Mediterranean. Summer minimum temperatures have been increasing at a faster rate than that of summer maximum temperatures, reflecting an asymmetric change of extreme temperature distributions. Total annual precipitation has been significantly decreased at the stations located in western Greece, as well as in the southeast, while the remaining areas exhibit a non-significant negative trend. This reduction is very likely linked to the positive phase of the NAO that resulted in an increase in the frequency and persistence of anticyclones over the Mediterranean.

A method of time transfer between remote stations via LRO

NASA Astrophysics Data System (ADS)

Hoffman, Evan; Sun, Xiaoli; Skillman, David R.; McGarry, Jan F.; Mao, Dandan

2014-05-01

Satellite Laser Ranging (SLR) is a standard geodetic technique that uses the round trip time of light from a ground station to a satellite to determine distance. When combined with a spacecraft detector and timing system, this technique can also be used to transfer time between ground stations, demonstrated by the Time Transfer by Laser Link (T2L2) project by the Centre National d'Etudes Spatiaes (CNES) and Observatorire de la Cote d'Azur (OCA) as well as the Laser Time Transfer (LTT) project by the Shanghai Astronomical Observatory. We describe an additional method of time transfer using simultaneous one-way laser ranging (LR) by two or more ground stations to the Lunar Reconnaissance Orbiter (LRO). A one way ranging is necessary, as two way ranging via retroreflectors for time transfer becomes impractical at lunar distances. The method will utilize the one-way LR currently being performed as a part of the LRO mission, allowing time transfer to be a by-product of the conventional usage of the data. Each ground station is referenced to a Master Clock using a multifrequency all-view GPS receiver at both the ground station and Master Clock locations.The Master Clock is located close enough to the ground station to make ionospheric differences in signal path negligible. Two or more stations range to LRO at the same time and their times of arrival are compared. Results from a ground-based experiment are shown, with sub-nanosecond precision shown to be achievable. Ultimately this measurement will provide a more precise and accurate relation of timing standards between stations, leading to a marked improvement in orbit determination.

Multi-geodetic characterization of the seasonal signal at the CERGA geodetic reference station, France

NASA Astrophysics Data System (ADS)

Mémin, Anthony; Viswanathan, Vishnu; Fienga, Agnes; Santamarìa-Gómez, Alvaro; Boy, Jean-Paul; Cavalié, Olivier; Deleflie, Florent; Exertier, Pierre; Bernard, Jean-Daniel; Hinderer, Jacques

2017-04-01

Crustal deformations due to surface-mass loading account for a significant part of the variability in geodetic time series. A perfect understanding of the loading signal observed by geodetic techniques should help in improving terrestrial reference frame (TRF) realizations. Yet, discrepancies between crustal motion estimates from models of surface-mass loading and observations are still too large so that no model is currently recommended by the IERS for reducing the observations. We investigate the discrepancy observed in the seasonal variations of the position at the CERGA station, South of France. We characterize the seasonal motions of the reference geodetic station CERGA from GNSS, SLR, LLR and InSAR. We investigate the consistency between the station motions deduced from these geodetic techniques and compare the observed station motion with that estimated using models of surface-mass change. In that regard, we compute atmospheric loading effects using surface pressure fields from ECMWF, assuming an ocean response according to the classical inverted barometer (IB) assumption, considered to be valid for periods typically exceeding a week. We also used general circulation ocean models (ECCO and GLORYS) forced by wind, heat and fresh water fluxes. The continental water storage is described using GLDAS/Noah and MERRA-land models. Using the surface-mass models, we estimate that the seasonal signal due to loading deformation at the CERGA station is about 8-9, 1-2 and 1-2 mm peak-to-peak in Up, North and East component, respectively. There is a very good correlation between GPS observations and non-tidal loading predicted deformation due to atmosphere, ocean and hydrology which is the main driver of seasonal signal at CERGA. Despite large error bars, LLR observations agree reasonably well with GPS and non-tidal loading predictions in Up component. Local deformation as observed by InSAR is very well correlated with GPS observations corrected for non-tidal loading

Clustering of financial time series

NASA Astrophysics Data System (ADS)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

NREL Charges Forward to Reduce Time at EV Stations | News | NREL

Science.gov Websites

station siting, travel patterns, grid resources, and business cases. At the same time, it is clear that Charges Forward to Reduce Time at EV Stations NREL Charges Forward to Reduce Time at EV Stations charging stations in Europe that will begin to approach the refueling time of gasoline vehicles. Photo

Global Ocean Circulation During Cretaceous Time

NASA Astrophysics Data System (ADS)

Haupt, B. J.; Seidov, D.

2001-12-01

Present--day global thermohaline ocean circulation (TOC) is usually associated with high--latitude deep-water formation due to surface cooling. In this understanding of the TOC driven by the deep--water production, the warm deep ocean during Mesozoic--Cenozoic time is a challenge. It may be questioned whether warm deep--ocean water, which is direct geologic evidence, does reflect warm polar surface--ocean regions. For the warm Cretaceous, it is difficult to maintain strong poleward heat transport in the case of reduced oceanic thermal contrasts. Usually, atmospheric feedbacks, in conjunction with the increase of atmospheric concentrations of greenhouse gases, are employed in order to explain the warm equable Cretaceous--Eocene climate. However, there is no feasible physical mechanism that could maintain warm subpolar surface oceans in both hemispheres, an assumption often used in atmospheric modeling. Our numerical experiments indicate that having a relatively cool but saltier high--latitude sea surface in at least one hemisphere is sufficient for driving a strong meridional overturning. Thus freshwater impacts in the high latitudes may be responsible for a vigorous conveyor capable of maintaining sufficient poleward oceanic heat transport needed to keep the polar oceans ice--free. These results imply that evaporation-precipitation patterns during warm climates are especially important climatic factors that can redistribute freshwater to create hemispheric asymmetry of sea surface conditions capable of generating a sufficiently strong TOC, otherwise impossible in warm climates.

Subsidence Evaluation of High-Speed Railway in Shenyang Based on Time-Series Insar

NASA Astrophysics Data System (ADS)

Zhang, Yun; Wei, Lianhuan; Li, Jiayu; Liu, Shanjun; Mao, Yachun; Wu, Lixin

2018-04-01

More and more high-speed railway are under construction in China. The slow settlement along high-speed railway tracks and newly-built stations would lead to inhomogeneous deformation of local area, and the accumulation may be a threat to the safe operation of high-speed rail system. In this paper, surface deformation of the newly-built high-speed railway station as well as the railway lines in Shenyang region will be retrieved by time series InSAR analysis using multi-orbit COSMO-SkyMed images. This paper focuses on the non-uniform subsidence caused by the changing of local environment along the railway. The accuracy of the settlement results can be verified by cross validation of the results obtained from two different orbits during the same period.

Coastal Atmosphere and Sea Time Series (CoASTS)

NASA Technical Reports Server (NTRS)

Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); Berthon, Jean-Francoise; Zibordi, Giuseppe; Doyle, John P.; Grossi, Stefania; vanderLinde, Dirk; Targa, Cristina; McClain, Charles R. (Technical Monitor)

2002-01-01

In this document, the first three years of a time series of bio-optical marine and atmospheric measurements are presented and analyzed. These measurements were performed from an oceanographic tower in the northern Adriatic Sea within the framework of the Coastal Atmosphere and Sea Time Series (CoASTS) project, an ocean color calibration and validation activity. The data set collected includes spectral measurements of the in-water apparent (diffuse attenuation coefficient, reflectance, Q-factor, etc.) and inherent (absorption and scattering coefficients) optical properties, as well as the concentrations of the main optical components (pigment and suspended matter concentrations). Clear seasonal patterns are exhibited by the marine quantities on which an appreciable short-term variability (on the order of a half day to one day) is superimposed. This short-term variability is well correlated with the changes in salinity at the surface resulting from the southward transport of freshwater coming from the northern rivers. Concentrations of chlorophyll alpha and total suspended matter span more than two orders of magnitude. The bio-optical characteristics of the measurement site pertain to both Case-I (about 64%) and Case-II (about 36%) waters, based on a relationship between the beam attenuation coefficient at 660nm and the chlorophyll alpha concentration. Empirical algorithms relating in-water remote sensing reflectance ratios and optical components or properties of interest (chlorophyll alpha, total suspended matter, and the diffuse attenuation coefficient) are presented.

Modified DTW for a quantitative estimation of the similarity between rainfall time series

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Barthès, Laurent; Mallet, Cécile; Chazottes, Aymeric

2017-04-01

interpretation of this inter-correlation is not straightforward. We propose here to use an improvement of the Euclidian distance which integrates the global complexity of the rainfall series. The Dynamic Time Wrapping (DTW) used in speech recognition allows matching two time series instantly different and provide the most probable time lag. However, the original formulation of the DTW suffers from some limitations. In particular, it is not adequate to the rain intermittency. In this study we present an adaptation of the DTW for the analysis of rainfall time series : we used time series from the "Météo France" rain gauge network observed between January 1st, 2007 and December 31st, 2015 on 25 stations located in the Île de France area. Then we analyze the results (eg. The distance, the relationship between the time lag detected by our methods and others measured parameters like speed and direction of the wind…) to show the ability of the proposed similarity to provide usefull information on the rain structure. The possibility of using this measure of similarity to define a quality indicator of a sensor integrated into an observation network is also envisaged.

Time-series photographs of the sea floor in western Massachusetts Bay: June 1997 to June 1998

USGS Publications Warehouse

Butman, Bradford; Alexander, P. Soupy; Bothner, Michael H.

2004-01-01

This report presents time-series photographs of the sea floor obtained from an instrumented tripod deployed at Site A in western Massachusetts Bay (42° 22.6' N., 70? 47.0' W., 30 m water depth, from June 1997 through June 1998. Site A is approximately 1 km south of an ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. Time-series photographs and oceanographic observations were initiated at Site A in December 1989 and are anticipated to continue to September 2005. This is the first in a series of reports planned to summarize and distribute these images in digital form. The objective of these reports is to enable easy and rapid viewing of the photographs and to provide a medium-resolution digital archive. The images, obtained every 4 hours, are presented as a movie (in .avi format, which may be viewed using an image viewer such as QuickTime or Windows Media Player) and as individual images (.tif format). The images provide time-series observations of changes of the sea floor and near-bottom water properties.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

NASA Astrophysics Data System (ADS)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Space Station evolution

NASA Technical Reports Server (NTRS)

Black, David C.

1987-01-01

The Space Station that will be launched and made operational in the early 1990s should be viewed as a beginning, a facility that will evolve with the passing of time to better meet the needs and requirements of a diverse set of users. Evolution takes several forms, ranging from simple growth through addition of infrastructure elements to upgrading of system capability through inclusion of advanced technologies. Much of the early considerations of Space Station evolution focused on physical growth. However, a series of recent workshops have revealed that the more likely mode of Space Station evolution will not be through growth but rather through a process known as 'branching'.

Earthquake forecasting studies using radon time series data in Taiwan

NASA Astrophysics Data System (ADS)

Walia, Vivek; Kumar, Arvind; Fu, Ching-Chou; Lin, Shih-Jung; Chou, Kuang-Wu; Wen, Kuo-Liang; Chen, Cheng-Hong

2017-04-01

For few decades, growing number of studies have shown usefulness of data in the field of seismogeochemistry interpreted as geochemical precursory signals for impending earthquakes and radon is idendified to be as one of the most reliable geochemical precursor. Radon is recognized as short-term precursor and is being monitored in many countries. This study is aimed at developing an effective earthquake forecasting system by inspecting long term radon time series data. The data is obtained from a network of radon monitoring stations eastblished along different faults of Taiwan. The continuous time series radon data for earthquake studies have been recorded and some significant variations associated with strong earthquakes have been observed. The data is also examined to evaluate earthquake precursory signals against environmental factors. An automated real-time database operating system has been developed recently to improve the data processing for earthquake precursory studies. In addition, the study is aimed at the appraisal and filtrations of these environmental parameters, in order to create a real-time database that helps our earthquake precursory study. In recent years, automatic operating real-time database has been developed using R, an open source programming language, to carry out statistical computation on the data. To integrate our data with our working procedure, we use the popular and famous open source web application solution, AMP (Apache, MySQL, and PHP), creating a website that could effectively show and help us manage the real-time database.

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

NASA Technical Reports Server (NTRS)

Adelfang, Stanley I.

2008-01-01

design case, the equations, the process and the simulated time series at multiple vehicle stations are presented.

Assessments of higher-order ionospheric effects on GPS coordinate time series: A case study of CMONOC with longer time series

NASA Astrophysics Data System (ADS)

Jiang, Weiping; Deng, Liansheng; Zhou, Xiaohui; Ma, Yifang

2014-05-01

Higher-order ionospheric (HIO) corrections are proposed to become a standard part for precise GPS data analysis. For this study, we deeply investigate the impacts of the HIO corrections on the coordinate time series by implementing re-processing of the GPS data from Crustal Movement Observation Network of China (CMONOC). Nearly 13 year data are used in our three processing runs: (a) run NO, without HOI corrections, (b) run IG, both second- and third-order corrections are modeled using the International Geomagnetic Reference Field 11 (IGRF11) to model the magnetic field, (c) run ID, the same with IG but dipole magnetic model are applied. Both spectral analysis and noise analysis are adopted to investigate these effects. Results show that for CMONOC stations, HIO corrections are found to have brought an overall improvement. After the corrections are applied, the noise amplitudes decrease, with the white noise amplitudes showing a more remarkable variation. Low-latitude sites are more affected. For different coordinate components, the impacts vary. The results of an analysis of stacked periodograms show that there is a good match between the seasonal amplitudes and the HOI corrections, and the observed variations in the coordinate time series are related to HOI effects. HOI delays partially explain the seasonal amplitudes in the coordinate time series, especially for the U component. The annual amplitudes for all components are decreased for over one-half of the selected CMONOC sites. Additionally, the semi-annual amplitudes for the sites are much more strongly affected by the corrections. However, when diplole model is used, the results are not as optimistic as IGRF model. Analysis of dipole model indicate that HIO delay lead to the increase of noise amplitudes, and that HIO delays with dipole model can generate false periodic signals. When dipole model are used in modeling HIO terms, larger residual and noise are brought in rather than the effective improvements.

Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

NASA Astrophysics Data System (ADS)

Jiang, Weiping; Ma, Jun; Li, Zhao; Zhou, Xiaohui; Zhou, Boye

2018-05-01

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

Time averaging, ageing and delay analysis of financial time series

NASA Astrophysics Data System (ADS)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Whale contribution to long time series of low-frequency oceanic ambient sound

NASA Astrophysics Data System (ADS)

Andrew, Rex K.; Howe, Bruce M.; Mercer, James A.

2002-05-01

It has long been known that baleen (mainly blue and fin) whale vocalizations are a component of oceanic ambient sound. Urick reports that the famous ``20-cycle pulses'' were observed even from the first Navy hydrophone installations in the early 1950's. As part of the Acoustic Thermometry Ocean Climate (ATOC) and the North Pacific Acoustic Laboratory (NPAL) programs, more than 6 years of nearly continuous ambient sound data have been collected from Sound Surveillance System (SOSUS) sites in the northeast Pacific. These records now show that the average level of the ambient sound has risen by as much as 10 dB since the 1960's. Although much of this increase is probably attributable to manmade sources, the whale call component is still prominent. The data also show that the whale signal is clearly seasonal: in coherent averages of year-long records, the whale call signal is the only feature that stands out, making strong and repeatable patterns as the whale population migrates past the hydrophone systems. This prominent and sometimes dominant component of ambient sound has perhaps not been fully appreciated in current ambient noise models. [Work supported by ONR.

SeaWiFS technical report series. Volume 17: Ocean color in the 21st century. A strategy for a 20-year time series

NASA Technical Reports Server (NTRS)

Abbott, Mark R.; Brown, Otis B.; Evans, Robert H.; Gordon, Howard R.; Carder, Kendall L.; Mueller-Karger, Frank E.; Esaias, Wayne E.; Hooker, Stanford B.; Firestone, Elaine R.

1994-01-01

Beginning with the upcoming launch of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), there should be almost continuous measurements of ocean color for nearly 20 years if all of the presently planned national and international missions are implemented. This data set will present a unique opportunity to understand the coupling of physical and biological processes in the world ocean. The presence of multiple ocean color sensors will allow the eventual development of an ocean color observing system that is both cost effective and scientifically based. This report discusses the issues involved and makes recommendations intended to ensure the maximum scientific return from this unique set of planned ocean color missions. An executive summary is included with this document which briefly discusses the primary issues and suggested actions to be considered.

Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen

2016-04-01

Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i

Using in-situ Glider Data to Improve the Interpretation of Time-Series Data in the San Pedro Channel

NASA Astrophysics Data System (ADS)

Teel, E.; Liu, X.; Seegers, B. N.; Ragan, M. A.; Jones, B. H.; Levine, N. M.

2016-02-01

Oceanic time-series have provided insight into biological, physical, and chemical processes and how these processes change over time. However, time-series data collected near coastal zones have not been used as broadly because of regional features that may prevent extrapolation of local results. Though these sites are inherently more affected by local processes, broadening the application of coastal data is crucial for improved modeling of processes such as total carbon drawdown and the development of oxygen minimum zones. Slocum gliders were deployed off the coast of Los Angeles from February to July of 2013 and 2014 providing high temporal and spatial resolution data of the San Pedro Channel (SPC), which includes the San Pedro Ocean Time Series (SPOT). The data were collapsed onto a standardized grid and primary and secondary characteristics of glider profiles were analyzed by principal component analysis to determine the processes impacting SPC and SPOT. The data fell into four categories: active upwelling, offshore intrusion, subsurface bloom, and surface bloom. Waters across the SPC were most similar to offshore water masses, even during the upwelling season when near-shore blooms are commonly observed. The SPOT site was found to be representative of the SPC 86% of the time, suggesting that the findings from SPOT are applicable for the entire SPC. Subsurface blooms were common in both years with co-located chlorophyll and particle maxima, and results suggested that these subsurface blooms contribute significantly to the local primary production. Satellite estimation of integrated chlorophyll was poor, possibly due to the prevalence of subsurface blooms and shallow optical depths during surface blooms. These results indicate that high resolution in-situ glider deployments can be used to determine the spatial domain of coastal time-series data, allowing for broader application of these datasets and greater integration into modeling efforts.

AQUAdexIM: highly efficient in-memory indexing and querying of astronomy time series images

NASA Astrophysics Data System (ADS)

Hong, Zhi; Yu, Ce; Wang, Jie; Xiao, Jian; Cui, Chenzhou; Sun, Jizhou

2016-12-01

Astronomy has always been, and will continue to be, a data-based science, and astronomers nowadays are faced with increasingly massive datasets, one key problem of which is to efficiently retrieve the desired cup of data from the ocean. AQUAdexIM, an innovative spatial indexing and querying method, performs highly efficient on-the-fly queries under users' request to search for Time Series Images from existing observation data on the server side and only return the desired FITS images to users, so users no longer need to download entire datasets to their local machines, which will only become more and more impractical as the data size keeps increasing. Moreover, AQUAdexIM manages to keep a very low storage space overhead and its specially designed in-memory index structure enables it to search for Time Series Images of a given area of the sky 10 times faster than using Redis, a state-of-the-art in-memory database.

Entropic Analysis of Electromyography Time Series

NASA Astrophysics Data System (ADS)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Measuring ocean coherence time with dual-baseline interferometry

NASA Technical Reports Server (NTRS)

Carande, Richard E.

1992-01-01

Using the Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) interferometer, measurements of the ocean coherence time at L and C band can be made at high spatial resolution. Fundamental to this measurement is the ability to image the ocean interferometrically at two different time-lags, or baselines. By modifying the operating procedure of the existing two antenna interferometer, a technique was developed make these measurements. L band coherence times are measured and presented.

Real-Time Ocean Data in the Classroom

ERIC Educational Resources Information Center

Murray, Laura; Gibson, Deidre; Ward, Angela

2008-01-01

To apply students' savvy internet skills in the science classroom--as well as capture their interest in science and investigation, and provide opportunities for authentic research--introduce them to real-time data from ocean-observing systems. Students can use data from these ocean-observing systems to discover the winds and waves from storms or…

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

A comparison of methods to estimate vertical land motion trends from GNSS and altimetry at tide gauge stations

NASA Astrophysics Data System (ADS)

Kleinherenbrink, Marcel; Riva, Riccardo; Frederikse, Thomas

2018-03-01

Tide gauge (TG) records are affected by vertical land motion (VLM), causing them to observe relative instead of geocentric sea level. VLM can be estimated from global navigation satellite system (GNSS) time series, but only a few TGs are equipped with a GNSS receiver. Hence, (multiple) neighboring GNSS stations can be used to estimate VLM at the TG. This study compares eight approaches to estimate VLM trends at 570 TG stations using GNSS by taking into account all GNSS trends with an uncertainty smaller than 1 mm yr-1 within 50 km. The range between the methods is comparable with the formal uncertainties of the GNSS trends. Taking the median of the surrounding GNSS trends shows the best agreement with differenced altimetry-tide gauge (ALT-TG) trends. An attempt is also made to improve VLM trends from ALT-TG time series. Only using highly correlated along-track altimetry and TG time series reduces the SD of ALT-TG time series by up to 10 %. As a result, there are spatially coherent changes in the trends, but the reduction in the root mean square (RMS) of differences between ALT-TG and GNSS trends is insignificant. However, setting correlation thresholds also acts like a filter to remove problematic TG time series. This results in sets of ALT-TG VLM trends at 344-663 TG locations, depending on the correlation threshold. Compared to other studies, we decrease the RMS of differences between GNSS and ALT-TG trends (from 1.47 to 1.22 mm yr-1), while we increase the number of locations (from 109 to 155), Depending on the methods the mean of differences between ALT-TG and GNSS trends vary between 0.1 and 0.2 mm yr-1. We reduce the mean of the differences by taking into account the effect of elastic deformation due to present-day mass redistribution. At varying ALT-TG correlation thresholds, we provide new sets of trends for 759 to 939 different TG stations. If both GNSS and ALT-TG trend estimates are available, we recommend using the GNSS trend estimates because residual

Homogenising time series: Beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2010-09-01

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that

Structural Time Series Model for El Niño Prediction

NASA Astrophysics Data System (ADS)

Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodo, Xavier

2015-04-01

ENSO is a dominant feature of climate variability on inter-annual time scales destabilizing weather patterns throughout the globe, and having far-reaching socio-economic consequences. It does not only lead to extensive rainfall and flooding in some regions of the world, and anomalous droughts in others, thus ruining local agriculture, but also substantially affects the marine ecosystems and the sustained exploitation of marine resources in particular coastal zones, especially the Pacific South American coast. As a result, forecasting of ENSO and especially of the warm phase of the oscillation (El Niño/EN) has long been a subject of intense research and improvement. Thus, the present study explores a novel method for the prediction of the Niño 3.4 index. In the state-of-the-art the advantageous statistical modeling approach of Structural Time Series Analysis has not been applied. Therefore, we have developed such a model using a State Space approach for the unobserved components of the time series. Its distinguishing feature is that observations consist of various components - level, seasonality, cycle, disturbance, and regression variables incorporated as explanatory covariates. These components are aimed at capturing the various modes of variability of the N3.4 time series. They are modeled separately, then combined in a single model for analysis and forecasting. Customary statistical ENSO prediction models essentially use SST, SLP and wind stress in the equatorial Pacific. We introduce new regression variables - subsurface ocean temperature in the western equatorial Pacific, motivated by recent (Ramesh and Murtugudde, 2012) and classical research (Jin, 1997), (Wyrtki, 1985), showing that subsurface processes and heat accumulation there are fundamental for initiation of an El Niño event; and a southern Pacific temperature-difference tracer, the Rossbell dipole, leading EN by about nine months (Ballester, 2011).

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Detection of early postseismic deformation from high-rate GNSS time series

NASA Astrophysics Data System (ADS)

Twardzik, C.; Vergnolle, M.; Avallone, A.; Sladen, A.

2017-12-01

Postseismic processes after an earthquake contribute to the redistribution of stresses in addition to that induced by the coseismic rupture. With the exception of very few studies (e.g., Miyazaki and Larson, 2008), most postseismic analyses only start one or two days following the mainshock. This leaves a critical part of postseismic phase unexplored, from a few minutes up to a few hours after the earthquake. In this study, we use kinematic precise point positioning (K-PPP) to analyze continuous GNSS data in order to obtain 30s position time series. These time series provide information on the surface displacements a soon as the dynamic response of the earthquake is over. Our first analysis focuses on the 2016 Pedernales, Ecuador, earthquake (Mw7.8). Using spectral analysis, we show that the typical logarithmic postseismic displacement trend can be detected as early as one to six hours after the earthquake depending on the station location and the level of noise. This analysis also allows to estimate the bias on the coseismic offsets usually based on daily pre- and post- earthquake positions. We use the early postseismic time series to test whether rate-and-state friction laws, traditionally used to explain postseismic processes days after the earthquake, still hold right after the mainshock. This study is being extended to two other subduction earthquakes: the 2010 Maule, Chile, earthquake (Mw8.8) and the 2015 Illapel, Chile, earthquake (Mw8.2).

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.

PubMed

Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng

2017-07-01

Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.

Real-time forecasting of the April 11, 2012 Sumatra tsunami

USGS Publications Warehouse

Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; ,

2012-01-01

The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

GNSS reflectometry aboard the International Space Station: phase-altimetry simulation to detect ocean topography anomalies

NASA Astrophysics Data System (ADS)

Semmling, Maximilian; Leister, Vera; Saynisch, Jan; Zus, Florian; Wickert, Jens

2016-04-01

An ocean altimetry experiment using Earth reflected GNSS signals has been proposed to the European Space Agency (ESA). It is part of the GNSS Reflectometry Radio Occultation Scatterometry (GEROS) mission that is planned aboard the International Space Station (ISS). Altimetric simulations are presented that examine the detection of ocean topography anomalies assuming GNSS phase delay observations. Such delay measurements are well established for positioning and are possible due to a sufficient synchronization of GNSS receiver and transmitter. For altimetric purpose delays of Earth reflected GNSS signals can be observed similar to radar altimeter signals. The advantage of GNSS is the synchronized separation of transmitter and receiver that allow a significantly increased number of observation per receiver due to more than 70 GNSS transmitters currently in orbit. The altimetric concept has already been applied successfully to flight data recorded over the Mediterranean Sea. The presented altimetric simulation considers anomalies in the Agulhas current region which are obtained from the Region Ocean Model System (ROMS). Suitable reflection events in an elevation range between 3° and 30° last about 10min with ground track's length >3000km. Typical along-track footprints (1s signal integration time) have a length of about 5km. The reflection's Fresnel zone limits the footprint of coherent observations to a major axis extention between 1 to 6km dependent on the elevation. The altimetric performance depends on the signal-to-noise ratio (SNR) of the reflection. Simulation results show that precision is better than 10cm for SNR of 30dB. Whereas, it is worse than 0.5m if SNR goes down to 10dB. Precision, in general, improves towards higher elevation angles. Critical biases are introduced by atmospheric and ionospheric refraction. Corresponding correction strategies are still under investigation.

Homogenising time series: beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Laser, GPS and absolute gravimetry vertical positioning time series comparison at the OCA observatory, France

NASA Astrophysics Data System (ADS)

Nicolas, J.; Nocquet, J.; van Camp, M.; Coulot, D.

2003-12-01

Time-dependent displacements of stations usually have magnitude close to the accuracy of each individual technique, and it still remains difficult to separate the true geophysical motion from possible artifacts inherent to each space geodetic technique. The Observatoire de la C“te d'Azur (OCA), located at Grasse, France benefits from the collocation of several geodetic instruments and techniques (3 laser ranging stations, and a permanent GPS) what allows us to do a direct comparison of the time series. Moreover, absolute gravimetry measurement campaigns have also been regularly performed since 1997, first by the "Ecole et Observatoire des Sciences de la Terre (EOST) of Strasbourg, France, and more recently by the Royal Observatory of Belgium. This study presents a comparison between the positioning time series of the vertical component derived from the SLR and GPS analysis with the gravimetric results from 1997 to 2003. The laser station coordinates are based on a LAGEOS -1 and -2 combined solution using reference 10-day arc orbits, the ITRF2000 reference frame, and the IERS96 conventions. Different GPS weekly global solutions provided from several IGS are combined and compared to the SLR results. The absolute gravimetry measurements are converted into vertical displacements with a classical gradient. The laser time series indicate a strong annual signal at the level of about 3-4 cm peak to peak amplitude on the vertical component. Absolute gravimetry data agrees with the SLR results. GPS positioning solutions also indicate a significant annual term, but with a magnitude of only 50% of the one shown by the SLR solution and by the gravimetry measurements. Similar annual terms are also observed on other SLR sites we processed, but usually with! lower and various amplitudes. These annual signals are also compared to vertical positioning variations corresponding to an atmospheric loading model. We present the level of agreement between the different techniques and we

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam

NASA Astrophysics Data System (ADS)

Eymen, Abdurrahman; Köylü, Ümran

2018-02-01

Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann-Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil-Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevşehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.

Quality and Consistency of the NASA Ocean Color Data Record

NASA Technical Reports Server (NTRS)

Franz, Bryan A.

2012-01-01

The NASA Ocean Biology Processing Group (OBPG) recently reprocessed the multimission ocean color time-series from SeaWiFS, MODIS-Aqua, and MODIS-Terra using common algorithms and improved instrument calibration knowledge. Here we present an analysis of the quality and consistency of the resulting ocean color retrievals, including spectral water-leaving reflectance, chlorophyll a concentration, and diffuse attenuation. Statistical analysis of satellite retrievals relative to in situ measurements will be presented for each sensor, as well as an assessment of consistency in the global time-series for the overlapping periods of the missions. Results will show that the satellite retrievals are in good agreement with in situ measurements, and that the sensor ocean color data records are highly consistent over the common mission lifespan for the global deep oceans, but with degraded agreement in higher productivity, higher complexity coastal regions.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation

NASA Astrophysics Data System (ADS)

Solazzo, E.; Galmarini, S.

2015-07-01

A more sensible use of monitoring data for the evaluation and development of regional-scale atmospheric models is proposed. The motivation stems from observing current practices in this realm where the quality of monitoring data is seldom questioned and model-to-data deviation is uniquely attributed to model deficiency. Efforts are spent to quantify the uncertainty intrinsic to the measurement process, but aspects connected to model evaluation and development have recently emerged that remain obscure, such as the spatial representativeness and the homogeneity of signals subjects of our investigation. By using time series of hourly records of ozone for a whole year (2006) collected by the European AirBase network the area of representativeness is firstly analysed showing, for similar class of stations (urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the noise of the signal, suggesting the mere station classification to be not a suitable candidate to help select the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed based on the spatial properties of the associativity of the spectral components of the ozone time series, in an attempt to determine the level of homogeneity. The spatial structure of the associativity among stations is informative of the spatial representativeness of that specific component and automatically tells about spatial anisotropy. Time series of ozone data from North American networks have also been analysed to support the methodology. We find that the low energy components (especially the intra-day signal) suffer from a too strong influence of country-level network set-up in Europe, and different networks in North America, showing spatial heterogeneity exactly at the administrative border that separates countries in Europe and at areas separating different networks in North America. For model evaluation purposes these elements should be treated

33 CFR 334.595 - Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape Canaveral Air Force Station, FL...

Code of Federal Regulations, 2014 CFR

2014-07-01

...; 45th Space Wing, Cape Canaveral Air Force Station, FL; restricted area. 334.595 Section 334.595.... The regulations in this section shall be enforced by the Commander, 45th Space Wing, Patrick Air Force... AND RESTRICTED AREA REGULATIONS § 334.595 Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape...

33 CFR 334.595 - Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape Canaveral Air Force Station, FL...

Code of Federal Regulations, 2013 CFR

2013-07-01

...; 45th Space Wing, Cape Canaveral Air Force Station, FL; restricted area. 334.595 Section 334.595.... The regulations in this section shall be enforced by the Commander, 45th Space Wing, Patrick Air Force... AND RESTRICTED AREA REGULATIONS § 334.595 Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape...

33 CFR 334.595 - Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape Canaveral Air Force Station, FL...

Code of Federal Regulations, 2012 CFR

2012-07-01

...; 45th Space Wing, Cape Canaveral Air Force Station, FL; restricted area. 334.595 Section 334.595.... The regulations in this section shall be enforced by the Commander, 45th Space Wing, Patrick Air Force... AND RESTRICTED AREA REGULATIONS § 334.595 Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape...

33 CFR 334.595 - Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape Canaveral Air Force Station, FL...

Code of Federal Regulations, 2010 CFR

2010-07-01

...; 45th Space Wing, Cape Canaveral Air Force Station, FL; restricted area. 334.595 Section 334.595.... The regulations in this section shall be enforced by the Commander, 45th Space Wing, Patrick Air Force... AND RESTRICTED AREA REGULATIONS § 334.595 Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape...

33 CFR 334.595 - Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape Canaveral Air Force Station, FL...

Code of Federal Regulations, 2011 CFR

2011-07-01

...; 45th Space Wing, Cape Canaveral Air Force Station, FL; restricted area. 334.595 Section 334.595.... The regulations in this section shall be enforced by the Commander, 45th Space Wing, Patrick Air Force... AND RESTRICTED AREA REGULATIONS § 334.595 Atlantic Ocean off Cape Canaveral; 45th Space Wing, Cape...

Forecasting Enrollments with Fuzzy Time Series.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series

NASA Astrophysics Data System (ADS)

Liang, X. S.

2017-12-01

Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Here we show that this important and challenging question (one of the major challenges in the science of big data), which is of interest in a wide variety of disciplines, has a positive answer. Particularly, for linear systems, the maximal likelihood estimator of the causality from a series X2 to another series X1, written T2→1, turns out to be concise in form: T2→1 = [C11 C12 C2,d1 — C112 C1,d1] / [C112 C22 — C11C122] where Cij (i,j=1,2) is the sample covariance between Xi and Xj, and Ci,dj the covariance between Xi and ΔXj/Δt, the difference approximation of dXj/dt using the Euler forward scheme. An immediate corollary is that causation implies correlation, but not vice versa, resolving the long-standing debate over causation versus correlation. The above formula has been validated with touchstone series purportedly generated with one-way causality that evades the classical approaches such as Granger causality test and transfer entropy analysis. It has also been applied successfully to the investigation of many real problems. Through a simple analysis with the stock series of IBM and GE, an unusually strong one-way causality is identified from the former to the latter in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a "Giant" for the computer market. Another example presented here regards the cause-effect relation between the two climate modes, El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean. In the third example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming

CHEMICAL TIME-SERIES SAMPLING

EPA Science Inventory

The rationale for chemical time-series sampling has its roots in the same fundamental relationships as govern well hydraulics. Samples of ground water are collected as a function of increasing time of pumpage. The most efficient pattern of collection consists of logarithmically s...

Usability of ocean-bottom seismograms for broadband waveform tomography

NASA Astrophysics Data System (ADS)

Eibl, Eva P. S.; Sigloch, Karin

2013-04-01

Recordings made by broadband seismometers on the ocean-bottom are generally noisier than recordings of land stations using the same sensor type. The primary reason is that oceanic recordings are more affected by microseismic noise, which originates in the oceans. A similar drawback applies to data from stations on oceanic islands. The frequency band between 0.05 Hz and 0.2 Hz is most affected by microseismic noise -- unfortunately a large overlap with the band that is most useful in highly-resolving body-wave tomography when using land stations. On the other hand, waveform inversion methods, unlike traditional ray theory, do not necessarily depend on the availability of clean, pulse-like broadband signals across the entire frequency range. For example in finite-frequency tomography, the method of our choice, modelling procedures permit the exclusion of unusable frequency bands on a case-by-case basis. Hence we investigate to what extent seismograms from the ocean-bottom and from island stations can be used for broadband waveform inversion of teleseismic P-waves, as compared to continental land stations. We have re-analyzed data from one of the largest onshore-offshore, broadband, long-term seismological experiment to date: the Hawaiian PLUME project (Wolfe et al. 2009, Laske 2009). The data quality was studied in eight overlapping frequency bands (dominant periods between 30.0 s and 2.7 s), for year-long records from 62 ocean-bottom stations (January 2005 - June 2007), complemented by seismograms from 74 regional island stations and 236 continental stations from four different networks on the Pacific-rim, recorded in the same time frame. P-wave seismograms from 103 earthquakes of moment magnitude 6.2 and above, recorded at epicentral distances of 32° to 85° to Hawaii were assessed in this study. The quality of the recorded data was evaluated by calculating the cross-correlation coefficient between the first 1.5 dominant periods of real and predicted waveforms, in

Space-time measurements of oceanic sea states

NASA Astrophysics Data System (ADS)

Fedele, Francesco; Benetazzo, Alvise; Gallego, Guillermo; Shih, Ping-Chang; Yezzi, Anthony; Barbariol, Francesco; Ardhuin, Fabrice

2013-10-01

Stereo video techniques are effective for estimating the space-time wave dynamics over an area of the ocean. Indeed, a stereo camera view allows retrieval of both spatial and temporal data whose statistical content is richer than that of time series data retrieved from point wave probes. We present an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea and near the southern seashore of the Crimean peninsula, in the Black Sea. We use classical epipolar techniques to reconstruct the sea surface from the stereo pairs sequentially in time, viz. a sequence of spatial snapshots. We also present a variational approach that exploits the entire data image set providing a global space-time imaging of the sea surface, viz. simultaneous reconstruction of several spatial snapshots of the surface in order to guarantee continuity of the sea surface both in space and time. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directional spectra and wave statistics at a point in time that agrees well with probabilistic models. In particular, WASS stereo imaging is able to capture typical features of the wave surface, especially the crest-to-trough asymmetry due to second order nonlinearities, and the observed shape of large waves are fairly described by theoretical models based on the theory of quasi-determinism (Boccotti, 2000). Further, we investigate space-time extremes of the observed stationary sea states, viz. the largest surface wave heights expected over a given area during the sea state duration. The WASS analysis provides the first experimental proof that a space-time extreme is generally larger than that observed in time via point measurements, in agreement with the predictions based on stochastic theories for global maxima of Gaussian fields.

Stratus Ocean Reference Station (20 deg. S, 85 deg. W) : Mooring Recovery and Deployment Cruise, R/V Ronald H. Brown Cruise 05-05, September 26, 2005-October 21, 2005

DTIC Science & Technology

2006-02-01

WHOI- 2006 -06 Woods Hole Oceanographic Institution 1930 Stratus Ocean Reference Station (20°S, 85°W) Mooring Recovery and Deployment Cruise RN Ronald...Environmental Technology Laboratory, 3University of Colorado, CIRES, 4University of Miami, 5University of Concepcion February 2006 Technical Report...Institution Woods Hole, MA 02543 UOP Technical Report 2006 -01 WHOI- 2006 -06 UOP- 2006 -01 Stratus Ocean Reference Station (20*S, 85*W) Mooring Recovery and

Changing times at the Rocky Mountain Forest & Range Experiment Station: Station history from 1976 to 1997

Treesearch

R. H. Hamre

2005-01-01

Changing Times includes a review of early Station history, touches on changing societal perspectives and how things are now done differently, how the Station has changed physically and organizationally, technology transfer, a sampling of major characters, how some Station research has been applied, and a timeline of significant and/or interesting events. It includes...

A permanent seismic station beneath the Ocean Bottom

NASA Astrophysics Data System (ADS)

Harris, David; Cessaro, Robert K.; Duennebier, Fred K.; Byrne, David A.

1987-03-01

The Hawaii Institute of Geophysics began development of the Ocean Subbottom Seisometer (OSS) system in 1978, and OSS systems were installed in four locations between 1979 and 1982. The OSS system is a permanent, deep ocean borehole seismic recording system composed of a borehole sensor package (tool), an electromechanical cable, recorder package, and recovery system. Installed near the bottom of a borehole (drilled by the D/V Glomar Challenger), the tool contains three orthogonal, 4.5-Hz geophones, two orthogonal tilt meters; and a temperature sensor. Signals from these sensors are multiplexed, digitized (with a floating point technique), and telemetered through approximately 10 km of electromechanical cable to a recorder package located near the ocean bottom. Electrical power for the tool is supplied from the recorder package. The digital seismic signals are demultiplexed, converted back to analog form, processed through an automatic gain control (AGC) circuit, and recorded along with a time code on magnetic tape cassettes in the recorder package. Data may be recorded continuously for up to two months in the self-contained recorder package. Data may also be recorded in real time (digital formal) during the installation and subsequent recorder package servicing. The recorder package is connected to a submerged recovery buoy by a length of bouyant polypropylene rope. The anchor on the recovery buoy is released by activating either of the acoustical command releases. The polypropylene rope may also be seized with a grappling hook to effect recovery. The recorder package may be repeatedly serviced as long as the tool remains functional A wide range of data has been recovered from the OSS system. Recovered analog records include signals from natural seismic sources such as earthquakes (teleseismic and local), man-made seismic sources such as refraction seismic shooting (explosives and air cannons), and nuclear tests. Lengthy continuous recording has permitted analysis

Decadal variability of the Tropical Atlantic Ocean Surface Temperature in shipboard measurements and in a Global Ocean-Atmosphere model

NASA Technical Reports Server (NTRS)

Mehta, Vikram M.; Delworth, Thomas

1995-01-01

Sea surface temperature (SST) variability was investigated in a 200-yr integration of a global model of the coupled oceanic and atmospheric general circulations developed at the Geophysical Fluid Dynamics Laboratory (GFDL). The second 100 yr of SST in the coupled model's tropical Atlantic region were analyzed with a variety of techniques. Analyses of SST time series, averaged over approximately the same subregions as the Global Ocean Surface Temperature Atlas (GOSTA) time series, showed that the GFDL SST anomalies also undergo pronounced quasi-oscillatory decadal and multidecadal variability but at somewhat shorter timescales than the GOSTA SST anomalies. Further analyses of the horizontal structures of the decadal timescale variability in the GFDL coupled model showed the existence of two types of variability in general agreement with results of the GOSTA SST time series analyses. One type, characterized by timescales between 8 and 11 yr, has high spatial coherence within each hemisphere but not between the two hemispheres of the tropical Atlantic. A second type, characterized by timescales between 12 and 20 yr, has high spatial coherence between the two hemispheres. The second type of variability is considerably weaker than the first. As in the GOSTA time series, the multidecadal variability in the GFDL SST time series has approximately opposite phases between the tropical North and South Atlantic Oceans. Empirical orthogonal function analyses of the tropical Atlantic SST anomalies revealed a north-south bipolar pattern as the dominant pattern of decadal variability. It is suggested that the bipolar pattern can be interpreted as decadal variability of the interhemispheric gradient of SST anomalies. The decadal and multidecadal timescale variability of the tropical Atlantic SST, both in the actual and in the GFDL model, stands out significantly above the background 'red noise' and is coherent within each of the time series, suggesting that specific sets of

Learning time series for intelligent monitoring

NASA Technical Reports Server (NTRS)

Manganaris, Stefanos; Fisher, Doug

1994-01-01

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features using Bayesian model induction and the minimum message length approach to assign priors. In the performance task, we classify a time series in one of the learned classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. We report results from experiments in a monitoring domain of interest to NASA.

Changing characteristics of streamflow in the Midwest and its relation to oceanic-atmospheric oscillations

NASA Astrophysics Data System (ADS)

Thakur, B.; Pathak, P.; Kalra, A.; Ahmad, S.

2016-12-01

The identification of primary drivers of streamflow may prove beneficial in forecasting streamflow in the Midwestern U.S. In the past researches, streamflow in the region have been strongly correlated with El Niño-Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO). The present study takes in to account the pre-defined Pacific and Atlantic Ocean regions (e.g., ENSO, PDO, AMO) along with new regions with an intent to identify new significantly correlated regions. This study assesses the interrelationship between sea surface temperatures (SST) anomalies in the Pacific and Atlantic Ocean and seasonal streamflow in the Midwestern U.S. Average Pacific and Atlantic Ocean SST anomalies, were calculated for 2 different 3 month series: September-November and December-February so as to create a lead time varying from 3 to 9 months. Streamflow were averaged for three seasons: spring (April-June), spring-summer (April-August) and summer (June-August). The correlation between streamflow and SST is analyzed using singular value decomposition for a period of 1960-2013. The result of the study showed several regions-other than the known Pacific and Atlantic Ocean regions- that were significantly correlated with streamflow stations. Higher correlation between the climate indices and streamflow were observed as the lead time decreased. The identification of the associations between SST and streamflow and significant SST regions in the Pacific and Atlantic Ocean may enhance the skill of streamflow predictability and water management in the region.

Short-term variations of Icelandic ice cap mass inferred from cGPS coordinate time series

NASA Astrophysics Data System (ADS)

Compton, Kathleen; Bennett, Richard A.; Hreinsdóttir, Sigrún; van Dam, Tonie; Bordoni, Andrea; Barletta, Valentina; Spada, Giorgio

2017-06-01

As the global climate changes, understanding short-term variations in water storage is increasingly important. Continuously operating Global Positioning System (cGPS) stations in Iceland record annual periodic motion—the elastic response to winter accumulation and spring melt seasons—with peak-to-peak vertical amplitudes over 20 mm for those sites in the Central Highlands. Here for the first time for Iceland, we demonstrate the utility of these cGPS-measured displacements for estimating seasonal and shorter-term ice cap mass changes. We calculate unit responses to each of the five largest ice caps in central Iceland at each of the 62 cGPS locations using an elastic half-space model and estimate ice mass variations from the cGPS time series using a simple least squares inversion scheme. We utilize all three components of motion, taking advantage of the seasonal motion recorded in the horizontal. We remove secular velocities and accelerations and explore the impact that seasonal motions due to atmospheric, hydrologic, and nontidal ocean loading have on our inversion results. Our results match available summer and winter mass balance measurements well, and we reproduce the seasonal stake-based observations of loading and melting within the 1σ confidence bounds of the inversion. We identify nonperiodic ice mass changes associated with interannual variability in precipitation and other processes such as increased melting due to reduced ice surface albedo or decreased melting due to ice cap insulation in response to tephra deposition following volcanic eruptions, processes that are not resolved with once or twice-yearly stake measurements.

The Crossover Time as an Evaluation of Ocean Models Against Persistence

NASA Astrophysics Data System (ADS)

Phillipson, L. M.; Toumi, R.

2018-01-01

A new ocean evaluation metric, the crossover time, is defined as the time it takes for a numerical model to equal the performance of persistence. As an example, the average crossover time calculated using the Lagrangian separation distance (the distance between simulated trajectories and observed drifters) for the global MERCATOR ocean model analysis is found to be about 6 days. Conversely, the model forecast has an average crossover time longer than 6 days, suggesting limited skill in Lagrangian predictability by the current generation of global ocean models. The crossover time of the velocity error is less than 3 days, which is similar to the average decorrelation time of the observed drifters. The crossover time is a useful measure to quantify future ocean model improvements.

Directionality volatility in electroencephalogram time series

NASA Astrophysics Data System (ADS)

Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

2016-06-01

We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

Ecosystem behavior at Bermuda Station [open quotes]S[close quotes] and ocean weather station [open quotes]India[close quotes]: A general circulation model and observational analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasham, M.J.R.; Sarmiento, J.L.; Slater, R.D.

1993-06-01

One important theme of modern biological oceanography has been the attempt to develop models of how the marine ecosystem responds to variations in the physical forcing functions such as solar radiation and the wind field. The authors have addressed the problem by embedding simple ecosystem models into a seasonally forced three-dimensional general circulation model of the North Atlantic ocean. In this paper first, some of the underlying biological assumptions of the ecosystem model are presented, followed by an analysis of how well the model predicts the seasonal cycle of the biological variables at Bermuda Station s' and Ocean Weather Stationmore » India. The model gives a good overall fit to the observations but does not faithfully model the whole seasonal ecosystem model. 57 refs., 25 figs., 5 tabs.« less

Precisions Measurement for the Grasp of Welding Deformation amount of Time Series for Large-Scale Industrial Products

NASA Astrophysics Data System (ADS)

Abe, R.; Hamada, K.; Hirata, N.; Tamura, R.; Nishi, N.

2015-05-01

As well as the BIM of quality management in the construction industry, demand for quality management of the manufacturing process of the member is higher in shipbuilding field. The time series of three-dimensional deformation of the each process, and are accurately be grasped strongly demanded. In this study, we focused on the shipbuilding field, will be examined three-dimensional measurement method. The shipyard, since a large equipment and components are intricately arranged in a limited space, the installation of the measuring equipment and the target is limited. There is also the element to be measured is moved in each process, the establishment of the reference point for time series comparison is necessary to devise. In this paper will be discussed method for measuring the welding deformation in time series by using a total station. In particular, by using a plurality of measurement data obtained from this approach and evaluated the amount of deformation of each process.

Comparison of seasonal variability of Aquarius sea surface salinity time series with in situ observations in the Karimata Strait, Indonesia

NASA Astrophysics Data System (ADS)

Susanto, R. D.; Setiawan, A.; Zheng, Q.; Sulistyo, B.; Adi, T. R.; Agustiadi, T.; Trenggono, M.; Triyono, T.; Kuswardani, A.

2016-12-01

The seasonal variability of a full lifetime of Aquarius sea surface salinity time series from August 25, 2011 to June 7, 2015 is compared to salinity time series obtained from in situ observations in the Karimata Strait. The Karimata Strait plays dual roles in water exchange between the Pacific and the Indian Ocean. The salinity in the Karimata Strait is strongly affected by seasonal monsoon winds. During the boreal winter monsoon, northwesterly winds draws low salinity water from the South China Sea into the Java Sea and at the same time, the Java Sea receives an influx of the Indian Ocean water via the Sunda Strait. The Java Sea water will reduce the main Indonesian throughflow in the Makassar Strait. Conditions are reversed during the summer monsoon. Low salinity water from the South China Sea also controls the vertical structure of water properties in the upper layer of the Makassar Strait and the Lombok Strait. As a part of the South China Sea and Indonesian Seas Transport/Exchange (SITE) program, trawl resistance bottom mounted CTD was deployed in the Karimata Strait in mid-2010 to mid-2016 at water depth of 40 m. CTD casts during the mooring recoveries and deployments are used to compare the bottom salinity data. This in situ salinity time series is compared with various Aquarius NASA salinity products (the level 2, level 3 ascending and descending tracks and the seven-days rolling averaged) to check the consistency, correlation and statistical analysis. The preliminary results show that the seasonal variability of Aquarius salinity time series has larger amplitude variability compared to that of in situ data.

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

NASA Astrophysics Data System (ADS)

Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

2012-01-01

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval

Decadal trends in Indian Ocean ambient sound.

PubMed

Miksis-Olds, Jennifer L; Bradley, David L; Niu, Xiaoyue Maggie

2013-11-01

The increase of ocean noise documented in the North Pacific has sparked concern on whether the observed increases are a global or regional phenomenon. This work provides evidence of low frequency sound increases in the Indian Ocean. A decade (2002-2012) of recordings made off the island of Diego Garcia, UK in the Indian Ocean was parsed into time series according to frequency band and sound level. Quarterly sound level comparisons between the first and last years were also performed. The combination of time series and temporal comparison analyses over multiple measurement parameters produced results beyond those obtainable from a single parameter analysis. The ocean sound floor has increased over the past decade in the Indian Ocean. Increases were most prominent in recordings made south of Diego Garcia in the 85-105 Hz band. The highest sound level trends differed between the two sides of the island; the highest sound levels decreased in the north and increased in the south. Rate, direction, and magnitude of changes among the multiple parameters supported interpretation of source functions driving the trends. The observed sound floor increases are consistent with concurrent increases in shipping, wind speed, wave height, and blue whale abundance in the Indian Ocean.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin.

PubMed

Nkiaka, E; Nawaz, N R; Lovett, J C

2016-07-01

Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.

The Integrated Ocean Observing System Data Assembly Center

NASA Astrophysics Data System (ADS)

Bouchard, R. H.; Henderson, D.; Burnett, W.; Hervey, R. V.; Crout, R.

2008-05-01

The Integrated Ocean Observing System (IOOS) is the U.S. contribution to the Global Ocean Observing System and the Global Earth Observing System of Systems (GEOSS). As the Integrated Ocean Observing System Data Assembly Center (IOOS DAC), the National Oceanic and Atmospheric Administration`s (NOAA) National Data Buoy Center (NDBC) collects data from ocean observing systems and performs quality control on the data. Once the IOOS DAC performs the quality control, it distributes them in real-time: (1) in World Meteorological Organization alphanumeric data formats via the Global Telecommunications System (GTS) that provides instant availability to national and international users (2) in text files via its website (http://www.ndbc.noaa.gov) that provide easy access and use, and (3) in netCDF format via its OPeNDAP/DODS Server (http://dods.ndbc.noaa.gov) that provides higher resolution data than available in WMO alphanumeric or text file formats. The IOOS DAC routinely checks and distributes data from about 200 NDBC stations that include meteorological and oceanographic observations from buoys and coastal stations, water-level estimations from tsunameters (DART), and climate monitoring from buoys (Tropical Atmosphere Ocean array (TAO)). The IOOS DAC operates continuously - 24 hours per day, 7 days per week. In addition to data from NDBC`s platforms, the IOOS DAC applies its scientific expertise and data management and communications capabilities to facilitate partnerships for the exchange and application of data and to coordinate and leverage regional assets and resources from about 350 IOOS Partner stations. The IOOS DAC through its quality control process provides feedback to its partners on the quality of their observation that can lead to improved quality of the observations. The NDBC-IOOS Data Partnerships span the Western Hemisphere with data collection from the Beaufort Sea to the Peru Current, from the International Date Line to the central Atlantic Ocean, and

Identifying and Correcting Timing Errors at Seismic Stations in and around Iran

DOE PAGES

Syracuse, Ellen Marie; Phillips, William Scott; Maceira, Monica; ...

2017-09-06

A fundamental component of seismic research is the use of phase arrival times, which are central to event location, Earth model development, and phase identification, as well as derived products. Hence, the accuracy of arrival times is crucial. However, errors in the timing of seismic waveforms and the arrival times based on them may go unidentified by the end user, particularly when seismic data are shared between different organizations. Here, we present a method used to analyze travel-time residuals for stations in and around Iran to identify time periods that are likely to contain station timing problems. For the 14more » stations with the strongest evidence of timing errors lasting one month or longer, timing corrections are proposed to address the problematic time periods. Finally, two additional stations are identified with incorrect locations in the International Registry of Seismograph Stations, and one is found to have erroneously reported arrival times in 2011.« less

Scale-dependent intrinsic entropies of complex time series.

PubMed

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Ocean Sense: Student-Led, Real-Time Research at the Bottom of the Ocean - Without Leaving the Classroom

NASA Astrophysics Data System (ADS)

Pelz, M.; Hoeberechts, M.; McLean, M. A.; Riddell, D. J.; Ewing, N.; Brown, J. C.

2016-12-01

This presentation outlines the authentic research experiences created by Ocean Networks Canada's Ocean Sense program, a transformative education program that connects students and teachers with place-based, real-time data via the Internet. This program, developed in collaboration with community educators, features student-centric activities, clearly outlined learning outcomes, assessment tools and curriculum aligned content. Ocean Networks Canada (ONC), an initiative of the University of Victoria, develops, operates, and maintains cabled ocean observatory systems. Technologies developed on the world-leading NEPTUNE and VENUS observatories have been adapted for small coastal installations called "community observatories," which enable community members to directly monitor conditions in the local ocean environment. Data from these observatories are fundamental to lessons and activities in the Ocean Sense program. Marketed as Ocean Sense: Local observations, global connections, the program introduces middle and high school students to research methods in biology, oceanography and ocean engineering. It includes a variety of resources and opportunities to excite students and spark curiosity about the ocean environment. The program encourages students to connect their local observations to global ocean processes and the observations of students in other geographic regions. Connection to place and local relevance of the program is enhanced through an emphasis on Indigenous and place-based knowledge. The program promotes of cross-cultural learning with the inclusion of Indigenous knowledge of the ocean. Ocean Sense provides students with an authentic research experience by connecting them to real-time data, often within their own communities. Using the freely accessible data portal, students can curate the data they need from a range of instruments and time periods. Further, students are not restricted to their local community; if their question requires a greater range of

An Energy-Based Similarity Measure for Time Series

NASA Astrophysics Data System (ADS)

Boudraa, Abdel-Ouahab; Cexus, Jean-Christophe; Groussat, Mathieu; Brunagel, Pierre

2007-12-01

A new similarity measure, called SimilB, for time series analysis, based on the cross-[InlineEquation not available: see fulltext.]-energy operator (2004), is introduced. [InlineEquation not available: see fulltext.] is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED) or the Pearson correlation coefficient (CC), SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of [InlineEquation not available: see fulltext.] are presented. Particularly, we show that [InlineEquation not available: see fulltext.] as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Arctic-HYCOS: a Large Sample observing system for estimating freshwater fluxes in the drainage basin of the Arctic Ocean

NASA Astrophysics Data System (ADS)

Pietroniro, Al; Korhonen, Johanna; Looser, Ulrich; Hardardóttir, Jórunn; Johnsrud, Morten; Vuglinsky, Valery; Gustafsson, David; Lins, Harry F.; Conaway, Jeffrey S.; Lammers, Richard; Stewart, Bruce; Abrate, Tommaso; Pilon, Paul; Sighomnou, Daniel; Arheimer, Berit

2015-04-01

The Arctic region is an important regulating component of the global climate system, and is also experiencing a considerable change during recent decades. More than 10% of world's river-runoff flows to the Arctic Ocean and there is evidence of changes in its fresh-water balance. However, about 30% of the Arctic basin is still ungauged, with differing monitoring practices and data availability from the countries in the region. A consistent system for monitoring and sharing of hydrological information throughout the Arctic region is thus of highest interest for further studies and monitoring of the freshwater flux to the Arctic Ocean. The purpose of the Arctic-HYCOS project is to allow for collection and sharing of hydrological data. Preliminary 616 stations were identified with long-term daily discharge data available, and around 250 of these already provide online available data in near real time. This large sample will be used in the following scientific analysis: 1) to evaluate freshwater flux to the Arctic Ocean and Seas, 2) to monitor changes and enhance understanding of the hydrological regime and 3) to estimate flows in ungauged regions and develop models for enhanced hydrological prediction in the Arctic region. The project is intended as a component of the WMO (World Meteorological Organization) WHYCOS (World Hydrological Cycle Observing System) initiative, covering the area of the expansive transnational Arctic basin with participation from Canada, Denmark, Finland, Iceland, Norway, Russian Federation, Sweden and United States of America. The overall objective is to regularly collect, manage and share high quality data from a defined basic network of hydrological stations in the Arctic basin. The project focus on collecting data on discharge and possibly sediment transport and temperature. Data should be provisional in near-real time if available, whereas time-series of historical data should be provided once quality assurance has been completed. The

Detecting chaos in irregularly sampled time series.

PubMed

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Phase walk analysis of leptokurtic time series.

PubMed

Schreiber, Korbinian; Modest, Heike I; Räth, Christoph

2018-06-01

The Fourier phase information play a key role for the quantified description of nonlinear data. We present a novel tool for time series analysis that identifies nonlinearities by sensitively detecting correlations among the Fourier phases. The method, being called phase walk analysis, is based on well established measures from random walk analysis, which are now applied to the unwrapped Fourier phases of time series. We provide an analytical description of its functionality and demonstrate its capabilities on systematically controlled leptokurtic noise. Hereby, we investigate the properties of leptokurtic time series and their influence on the Fourier phases of time series. The phase walk analysis is applied to measured and simulated intermittent time series, whose probability density distribution is approximated by power laws. We use the day-to-day returns of the Dow-Jones industrial average, a synthetic time series with tailored nonlinearities mimicing the power law behavior of the Dow-Jones and the acceleration of the wind at an Atlantic offshore site. Testing for nonlinearities by means of surrogates shows that the new method yields strong significances for nonlinear behavior. Due to the drastically decreased computing time as compared to embedding space methods, the number of surrogate realizations can be increased by orders of magnitude. Thereby, the probability distribution of the test statistics can very accurately be derived and parameterized, which allows for much more precise tests on nonlinearities.

FATS: Feature Analysis for Time Series

NASA Astrophysics Data System (ADS)

Nun, Isadora; Protopapas, Pavlos; Sim, Brandon; Zhu, Ming; Dave, Rahul; Castro, Nicolas; Pichara, Karim

2017-11-01

FATS facilitates and standardizes feature extraction for time series data; it quickly and efficiently calculates a compilation of many existing light curve features. Users can characterize or analyze an astronomical photometric database, though this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.

Time-dependent limited penetrable visibility graph analysis of nonstationary time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Yang, Yu-Xuan; Dang, Wei-Dong

2017-06-01

Recent years have witnessed the development of visibility graph theory, which allows us to analyze a time series from the perspective of complex network. We in this paper develop a novel time-dependent limited penetrable visibility graph (TDLPVG). Two examples using nonstationary time series from RR intervals and gas-liquid flows are provided to demonstrate the effectiveness of our approach. The results of the first example suggest that our TDLPVG method allows characterizing the time-varying behaviors and classifying heart states of healthy, congestive heart failure and atrial fibrillation from RR interval time series. For the second example, we infer TDLPVGs from gas-liquid flow signals and interestingly find that the deviation of node degree of TDLPVGs enables to effectively uncover the time-varying dynamical flow behaviors of gas-liquid slug and bubble flow patterns. All these results render our TDLPVG method particularly powerful for characterizing the time-varying features underlying realistic complex systems from time series.

GEWEX-RFA Land-Ocean Mask

Atmospheric Science Data Center

2017-05-25

... for the Radiative Flux Assessment. It can be used as a filter for creating global, regional, or zonal time series for land or ocean. ... where coastal pixels have at least 10% of both land and water. Download file . ...

Global Real-Time Ocean Forecast System

Science.gov Websites

services. Marine Modeling and Analysis Branch Logo Click here to go to the MMAB home page Global Real-Time 17 Oct 2017 at 0Z, the Global RTOFS model has been upgraded to version 1.1.2. Changes include: The ). The global operational Real-Time Ocean Forecast System (Global RTOFS) at the National Centers for

Efficient Algorithms for Segmentation of Item-Set Time Series

NASA Astrophysics Data System (ADS)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Revealing the timing of ocean stratification using remotely sensed ocean fronts

NASA Astrophysics Data System (ADS)

Miller, Peter I.; Loveday, Benjamin R.

2017-10-01

Stratification is of critical importance to the circulation, mixing and productivity of the ocean, and is expected to be modified by climate change. Stratification is also understood to affect the surface aggregation of pelagic fish and hence the foraging behaviour and distribution of their predators such as seabirds and cetaceans. Hence it would be prudent to monitor the stratification of the global ocean, though this is currently only possible using in situ sampling, profiling buoys or underwater autonomous vehicles. Earth observation (EO) sensors cannot directly detect stratification, but can observe surface features related to the presence of stratification, for example shelf-sea fronts that separate tidally-mixed water from seasonally stratified water. This paper describes a novel algorithm that accumulates evidence for stratification from a sequence of oceanic front maps, and discusses preliminary results in comparison with in situ data and simulations from 3D hydrodynamic models. In certain regions, this method can reveal the timing of the seasonal onset and breakdown of stratification.

Phytoplankton biomass and microbial abundances during the spring upwelling season in the coastal area off Concepción, central-southern Chile: Variability around a time series station

NASA Astrophysics Data System (ADS)

Morales, Carmen E.; Anabalón, Valeria

2012-01-01

In the coastal system off Concepción, time series observations at a fixed station (St. 18) have shown strong seasonal changes in the oceanographic environment of the upper layer (<35 m depth), accompanied by large increases in phytoplankton biomass during the spring-summer upwelling season. These blooms, dominated by microplanktonic diatoms, have usually overshadowed the relevance of the smaller microbial components during upwelling. This study focuses on the variability of oceanographic conditions and their association with the structure of the planktonic community (size fractionated chlorophyll-a and microbial abundances) in the upper layer during the upwelling season, examining the extent to which St. 18 is representative of the coastal system off Concepción during springtime. For this purpose, data from three consecutive springs (2004, 2005, 2006) were compared, which included cruises for all years (8 stations around St. 18) as well as monthly sampling at St. 18. Most of the spatial (submesoscale) variability in chlorophyll-a and the microbial components was not significant, but data dispersion around mean values was high. Water column structure (temperature and salinity) in the upper layer explained a significant fraction (25-65%) of the spatial variability in most of the planktonic components; their responses to oceanographic variability were linear in some cases and non-linear in others. For the most part, St. 18 appears to adequately represent mean oceanographic conditions and the structure of planktonic communities in the coastal waters off Concepción during springtime, however spatial variability needs to be taken into account in the interpretations of temporal changes at this fixed station as well as in assessments of carbon flow within, and exportation processes from, this upwelling system.

ZWD time series analysis derived from NRT data processing. A regional study of PW in Greece.

NASA Astrophysics Data System (ADS)

Pikridas, Christos; Balidakis, Kyriakos; Katsougiannopoulos, Symeon

2015-04-01

ZWD (Zenith Wet/non-hydrostatic Delay) estimates are routinely derived Near Real Time from the new established Analysis Center in the Department of Geodesy and Surveying of Aristotle University of Thessaloniki (DGS/AUT-AC), in the framework of E-GVAP (EUMETNET GNSS water vapour project) since October 2014. This process takes place on an hourly basis and yields, among else, station coordinates and tropospheric parameter estimates for a network of 90+ permanent GNSS (Global Navigation Satellite System) stations. These are distributed at the wider part of Hellenic region. In this study, temporal and spatial variability of ZWD estimates were examined, as well as their relation with coordinate series extracted from both float and fixed solution of the initial phase ambiguities. For this investigation, Bernese GNSS Software v5.2 was used for the acquisition of the 6 month dataset from the aforementioned network. For time series analysis we employed techniques such as the Generalized Lomb-Scargle periodogram and Burg's maximum entropy method due to inefficiencies of the Discrete Fourier Transform application in the test dataset. Through the analysis, interesting results for further geophysical interpretation were drawn. In addition, the spatial and temporal distributions of Precipitable Water vapour (PW) obtained from both ZWD estimates and ERA-Interim reanalysis grids were investigated.

Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series.

PubMed

Khwarahm, Nabaz R; Dash, Jadunandan; Skjøth, C A; Newnham, R M; Adams-Groom, B; Head, K; Caulton, Eric; Atkinson, Peter M

2017-02-01

Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to

Causes of the sharp increase in the time series of surface solar radiation in China between 1990 and 1993

NASA Astrophysics Data System (ADS)

Wang, Yawen; Wild, Martin

2017-02-01

During 1990-1993, a nation-wide replacement of the instruments measuring surface solar radiation (SSR) and a restructuring of SSR stations took place in China. Meanwhile, a sudden upward jump was noted in published composite time series of observed SSR records in this period. This study clarifies that about 1/3 of the magnitude of the SSR jump in China was accidentally caused by the abandonment/establishment of 51 stations (˜39% of total) during the period of 1990-1993. The remaining 2/3 of the SSR jump was only caused by 22 stations detected by the methods of the accumulated deviation curve and the Mann-Whitney U test. Out of these 22 stations, about 1/4 of the SSR jump were caused by 6 stations due to natural factors, as similar variations were recorded by sunshine duration. The other 3/4 were caused by the remaining 16 stations as a result of artificial factors such as instrument replacement, changes in the classification or location of stations, or potential operational errors.

Real-time generation of infrared ocean scene based on GPU

NASA Astrophysics Data System (ADS)

Jiang, Zhaoyi; Wang, Xun; Lin, Yun; Jin, Jianqiu

2007-12-01

Infrared (IR) image synthesis for ocean scene has become more and more important nowadays, especially for remote sensing and military application. Although a number of works present ready-to-use simulations, those techniques cover only a few possible ways of water interacting with the environment. And the detail calculation of ocean temperature is rarely considered by previous investigators. With the advance of programmable features of graphic card, many algorithms previously limited to offline processing have become feasible for real-time usage. In this paper, we propose an efficient algorithm for real-time rendering of infrared ocean scene using the newest features of programmable graphics processors (GPU). It differs from previous works in three aspects: adaptive GPU-based ocean surface tessellation, sophisticated balance equation of thermal balance for ocean surface, and GPU-based rendering for infrared ocean scene. Finally some results of infrared image are shown, which are in good accordance with real images.

Sensor-Generated Time Series Events: A Definition Language

PubMed Central

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

2012-01-01

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

Remote Sensing Time Series Product Tool

NASA Technical Reports Server (NTRS)

Predos, Don; Ryan, Robert E.; Ross, Kenton W.

2006-01-01

The TSPT (Time Series Product Tool) software was custom-designed for NASA to rapidly create and display single-band and band-combination time series, such as NDVI (Normalized Difference Vegetation Index) images, for wide-area crop surveillance and for other time-critical applications. The TSPT, developed in MATLAB, allows users to create and display various MODIS (Moderate Resolution Imaging Spectroradiometer) or simulated VIIRS (Visible/Infrared Imager Radiometer Suite) products as single images, as time series plots at a selected location, or as temporally processed image videos. Manually creating these types of products is extremely labor intensive; however, the TSPT development tool makes the process simplified and efficient. MODIS is ideal for monitoring large crop areas because of its wide swath (2330 km), its relatively small ground sample distance (250 m), and its high temporal revisit time (twice daily). Furthermore, because MODIS imagery is acquired daily, rapid changes in vegetative health can potentially be detected. The new TSPT technology provides users with the ability to temporally process high-revisit-rate satellite imagery, such as that acquired from MODIS and from its successor, the VIIRS. The TSPT features the important capability of fusing data from both MODIS instruments onboard the Terra and Aqua satellites, which drastically improves cloud statistics. With the TSPT, MODIS metadata is used to find and optionally remove bad and suspect data. Noise removal and temporal processing techniques allow users to create low-noise time series plots and image videos and to select settings and thresholds that tailor particular output products. The TSPT GUI (graphical user interface) provides an interactive environment for crafting what-if scenarios by enabling a user to repeat product generation using different settings and thresholds. The TSPT Application Programming Interface provides more fine-tuned control of product generation, allowing experienced

Time series dataset of fish assemblages near thermal discharges at nuclear power plants in northern Taiwan.

PubMed

Chen, Hungyen; Chen, Ching-Yi; Shao, Kwang-Tsao

2018-05-08

Long-term time series datasets with consistent sampling methods are rather rare, especially the ones of non-target coastal fishes. Here we described a long-term time series dataset of fish collected by trammel net fish sampling and observed by an underwater diving visual census near the thermal discharges at two nuclear power plants on the northern coast of Taiwan. Both experimental and control stations of these two investigations were monitored four times per year in the surrounding seas at both plants from 2000 to 2017. The underwater visual census mainly monitored reef fish assemblages and trammel net samples monitored pelagic or demersal fishes above the muddy/sandy bottom. In total, 508 samples containing 203,863 individuals from 347 taxa were recorded in both investigations at both plants. These data can be used by ecologists and fishery biologists interested in the elucidation of the temporal patterns of species abundance and composition.

Extending nonlinear analysis to short ecological time series.

PubMed

Hsieh, Chih-hao; Anderson, Christian; Sugihara, George

2008-01-01

Nonlinearity is important and ubiquitous in ecology. Though detectable in principle, nonlinear behavior is often difficult to characterize, analyze, and incorporate mechanistically into models of ecosystem function. One obvious reason is that quantitative nonlinear analysis tools are data intensive (require long time series), and time series in ecology are generally short. Here we demonstrate a useful method that circumvents data limitation and reduces sampling error by combining ecologically similar multispecies time series into one long time series. With this technique, individual ecological time series containing as few as 20 data points can be mined for such important information as (1) significantly improved forecast ability, (2) the presence and location of nonlinearity, and (3) the effective dimensionality (the number of relevant variables) of an ecological system.

Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna

NASA Astrophysics Data System (ADS)

Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea; Montalto, Placido; Patanè, Domenico; Privitera, Eugenio

2016-07-01

From January 2011 to December 2015, Mt. Etna was mainly characterized by a cyclic eruptive behavior with more than 40 lava fountains from New South-East Crater. Using the RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area, an automatic recognition of the different states of volcanic activity (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN) has been applied for monitoring purposes. Since values of the RMS time series calculated on the seismic signal are generated from a stochastic process, we can try to model the system generating its sampled values, assumed to be a Markov process, using Hidden Markov Models (HMMs). HMMs analysis seeks to recover the sequence of hidden states from the observations. In our framework, observations are characters generated by the Symbolic Aggregate approXimation (SAX) technique, which maps RMS time series values with symbols of a pre-defined alphabet. The main advantages of the proposed framework, based on HMMs and SAX, with respect to other automatic systems applied on seismic signals at Mt. Etna, are the use of multiple stations and static thresholds to well characterize the volcano states. Its application on a wide seismic dataset of Etna volcano shows the possibility to guess the volcano states. The experimental results show that, in most of the cases, we detected lava fountains in advance.

Financial Time-series Analysis: a Brief Overview

NASA Astrophysics Data System (ADS)

Chakraborti, A.; Patriarca, M.; Santhanam, M. S.

Prices of commodities or assets produce what is called time-series. Different kinds of financial time-series have been recorded and studied for decades. Nowadays, all transactions on a financial market are recorded, leading to a huge amount of data available, either for free in the Internet or commercially. Financial time-series analysis is of great interest to practitioners as well as to theoreticians, for making inferences and predictions. Furthermore, the stochastic uncertainties inherent in financial time-series and the theory needed to deal with them make the subject especially interesting not only to economists, but also to statisticians and physicists [1]. While it would be a formidable task to make an exhaustive review on the topic, with this review we try to give a flavor of some of its aspects.

Physics, Nonlinear Time Series Analysis, Data Assimilation and Hyperfast Modeling of Nonlinear Ocean Waves

DTIC Science & Technology

2010-09-30

Hyperfast Modeling of Nonlinear Ocean Waves A. R. Osborne Dipartimento di Fisica Generale, Università di Torino Via Pietro Giuria 1, 10125...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Universit?i Torino,Dipartimento di Fisica Generale,Via Pietro Giuria 1,10125 Torino, Italy, 8. PERFORMING

Comparison of ocean mass content change from direct and inversion based approaches

NASA Astrophysics Data System (ADS)

Uebbing, Bernd; Kusche, Jürgen; Rietbroek, Roelof

2017-04-01

The GRACE satellite mission provides an indispensable tool for measuring oceanic mass variations. Such time series are essential to separate global mean sea level rise in thermosteric and mass driven contributions, and thus to constrain ocean heat content and (deep) ocean warming when viewed together with altimetry and Argo data. However, published estimates over the GRACE era differ, not only depending on the time window considered. Here, we will look into sources of such differences with direct and inverse approaches. Deriving ocean mass time series requires several processing steps; choosing a GRACE (and altimetry and Argo) product, data coverage, masks and filters to be applied in either spatial or spectral domain, corrections related to spatial leakage, GIA and geocenter motion need to be accounted for. In this study, we quantify the effects of individual processing choices and assumptions of the direct and inversion based approaches to derive ocean mass content change. Furthermore, we compile the different estimates from existing literature and sources, to highlight the differences.

Estimation of coupling between time-delay systems from time series

NASA Astrophysics Data System (ADS)

Prokhorov, M. D.; Ponomarenko, V. I.

2005-07-01

We propose a method for estimation of coupling between the systems governed by scalar time-delay differential equations of the Mackey-Glass type from the observed time series data. The method allows one to detect the presence of certain types of linear coupling between two time-delay systems, to define the type, strength, and direction of coupling, and to recover the model equations of coupled time-delay systems from chaotic time series corrupted by noise. We verify our method using both numerical and experimental data.

Wind speed time series reconstruction using a hybrid neural genetic approach

NASA Astrophysics Data System (ADS)

Rodriguez, H.; Flores, J. J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F.

2017-11-01

Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.

Reconstruction of ensembles of coupled time-delay systems from time series.

PubMed

Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

2014-06-01

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

Time series smoother for effect detection.

PubMed

You, Cheng; Lin, Dennis K J; Young, S Stanley

2018-01-01

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined.

Time series smoother for effect detection

PubMed Central

Lin, Dennis K. J.; Young, S. Stanley

2018-01-01

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined. PMID:29684033

Time Series Model Identification and Prediction Variance Horizon.

DTIC Science & Technology

1980-06-01

stationary time series Y(t). -6- In terms of p(v), the definition of the three time series memory types is: No Memory Short Memory Long Memory X IP (v)I 0 0...X lp(v)l < - I IP (v) = v=1 v=l v=l Within short memory time series there are three types whose classification in terms of correlation functions is...1974) "Some Recent Advances in Time Series Modeling", TEEE Transactions on Automatic ControZ, VoZ . AC-19, No. 6, December, 723-730. Parzen, E. (1976) "An

Time series modeling in traffic safety research.

PubMed

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

Common View Time Transfer Using Worldwide GPS and DMA Monitor Stations

NASA Technical Reports Server (NTRS)

Reid, Wilson G.; McCaskill, Thomas B.; Oaks, Orville J.; Buisson, James A.; Warren, Hugh E.

1996-01-01

Analysis of the on-orbit Navstar clocks and the Global Positioning System (GPS) monitor station reference clocks is performed by the Naval Research Laboratory using both broadcast and postprocessed precise ephemerides. The precise ephemerides are produced by the Defense Mapping Agency (DMA) for each of the GPS space vehicles from pseudo-range measurements collected at five GPS and at five DMA monitor stations spaced around the world. Recently, DMA established an additional site co-located with the US Naval Observatory precise time site. The time reference for the new DMA site is the DoD Master Clock. Now, for the first time, it is possible to transfer time every 15 minutes via common view from the DoD Master Clock to the 11 GPS and DMA monitor stations. The estimated precision of a single common-view time transfer measurement taken over a 15-minute interval was between 1.4 and 2.7 nanoseconds. Using the measurements from all Navstar space vehicles in common view during the 15-minute interval, typically 3-7 space vehicles, improved the estimate of the precision to between 0.65 and 1.13 nanoseconds. The mean phase error obtained from closure of the time transfer around the world using the 11 monitor stations and the 25 space vehicle clocks over a period of 4 months had a magnitude of 31 picoseconds. Analysis of the low noise time transfer from the DoD Master Clock to each of the monitor stations yields not only the bias in the time of the reference clock, but also focuses attention on structure in the behaviour of the reference clock not previously seen. Furthermore, the time transfer provides a a uniformly sampled database of 15-minute measurements that make possible, for the first time, the direct and exhaustive computation of the frequency stability of the monitor station reference clocks. To lend perspective to the analysis, a summary is given of the discontinuities in phase and frequency that occurred in the reference clock at the Master Control Station during

Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

NASA Astrophysics Data System (ADS)

Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

2014-05-01

Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help

Harvesting the Ocean: 1. The Ocean.

ERIC Educational Resources Information Center

Caton, Albert, Ed.; And Others

This booklet is the first in a series of three interdisciplinary units which focus specifically on the Pacific Ocean and its surrounding countries. The booklet, designed for lower secondary students, provides an introduction to the ocean environment such that students can understand the physical factors underlying issues raised by the other two…

Space-for-time substitution in predicting the state of picoplankton and nanoplankton in a changing Arctic Ocean

NASA Astrophysics Data System (ADS)

Li, William K. W.; Carmack, Eddy C.; McLaughlin, Fiona A.; Nelson, R. John; Williams, William J.

2013-10-01

The Arctic Ocean is changing rapidly but there are no long-term time series observations on the state of the phytoplankton community that could allow a link to be made from physical/chemical pressures to the impact on marine ecosystems. Here, we test the idea that space-for-time (SFT) substitution might predict temporal change in the Canada Basin premised on differences in the present state of phytoplankton in other geographic zones, specifically the ratio in the abundance of picophytoplankton to nanophytoplankton (Pico:Nano). We compared the change in Pico:Nano observed in the Canada Basin from 2004 to 2012 to the different average states of this ratio in 26 other ocean ecological regions. Our results show that as upper ocean nitrate concentration changed in the Canada Basin from year to year, the concomitant change in Pico:Nano was statistically commensurate with the difference that this ratio exhibits between Longhurst ecological provinces in relation to nitrate concentration. Lower average concentration of nitrate in the upper water column is associated with a higher value of Pico:Nano, a result consistent with resource control of phytoplankton size structure in the ocean. We suggest that SFT substitution allows an explanation of temporal progression from spatial pattern as a test of mechanism, but such statistical prediction is not necessarily a projection of future states.

Particulate matter time-series and Köppen-Geiger climate classes in North America and Europe

NASA Astrophysics Data System (ADS)

Pražnikar, Jure

2017-02-01

Four years of time-series data on the particulate matter (PM) concentrations from 801 monitoring stations located in Europe and 234 stations in North America were analyzed. Using k-means clustering with distance correlation as a measure for similarity, 5 distinct PM clusters in Europe and 9 clusters across the United States of America (USA) were found. This study shows that meteorology has an important role in controlling PM concentrations, as comparison between Köppen-Geiger climate zones and identified PM clusters revealed very good spatial overlapping. Moreover, the Köppen-Geiger boundaries in Europe show a high similarity to the boundaries as defined by PM clusters. The western USA is much more diverse regarding climate zones; this characteristic was confirmed by cluster analysis, as 6 clusters were identified in the west, and only 3 were identified on the eastern side of the USA. The lowest similarity between PM time-series in Europe was observed between the Iberian Peninsula and the north Europe clusters. These two regions also show considerable differences, as the cold semi-arid climate has a long and hot summer period, while the cool continental climate has a short summertime and long and cold winters. Additionally, intra-continental examination of European clusters showed meteorologically driven phenomena in autumn 2011 encompassing a large European region from Bulgaria in the south, Germany in central Europe and Finland in the north with high PM concentrations in November and a decline in December 2011. Inter-continental comparison between Europe and the USA clusters revealed a remarkable difference between the PM time-series located in humid continental zone. It seems that because of higher shortwave downwelling radiation (≈210 W m-2) over the USA's continental zone, and consequently more intense production of secondary aerosols, a summer peak in PM concentration was observed. On the other hand, Europe's humid continental climate region experiences

Seasonal evolution of the upper-ocean adjacent to the South Orkney Islands, Southern Ocean: Results from a “lazy biological mooring”

NASA Astrophysics Data System (ADS)

Meredith, Michael P.; Nicholls, Keith W.; Renfrew, Ian A.; Boehme, Lars; Biuw, Martin; Fedak, Mike

2011-07-01

A serendipitous >8-month time series of hydrographic properties was obtained from the vicinity of the South Orkney Islands, Southern Ocean, by tagging a southern elephant seal ( Mirounga leonina) on Signy Island with a Conductivity-Temperature-Depth/Satellite-Relay Data Logger (CTD-SRDL) in March 2007. Such a time series (including data from the austral autumn and winter) would have been extremely difficult to obtain via other means, and it illustrates with unprecedented temporal resolution the seasonal progression of upper-ocean water mass properties and stratification at this location. Sea ice production values of around 0.15-0.4 m month -1 for April to July were inferred from the progression of salinity, with significant levels still in September (around 0.2 m month -1). However, these values presume that advective processes have negligible effect on the salinity changes observed locally; this presumption is seen to be inappropriate in this case, and it is argued that the ice production rates inferred are better considered as "smeared averages" for the region of the northwestern Weddell Sea upstream from the South Orkneys. The impact of such advective effects is illustrated by contrasting the observed hydrographic series with the output of a one-dimensional model of the upper-ocean forced with local fluxes. It is found that the difference in magnitude between local (modelled) and regional (inferred) ice production is significant, with estimates differing by around a factor of two. A halo of markedly low sea ice concentration around the South Orkneys during the austral winter offers at least a partial explanation for this, since it enabled stronger atmosphere/ocean fluxes to persist and hence stronger ice production to prevail locally compared with the upstream region. The year of data collection was an El Niño year, and it is well-established that this phenomenon can impact strongly on the surface ocean and ice field in this sector of the Southern Ocean, thus

A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

NASA Astrophysics Data System (ADS)

Skeie, R. B.; Berntsen, T.; Aldrin, M.; Holden, M.; Myhre, G.

2012-04-01

A key question in climate science is to quantify the sensitivity of the climate system to perturbation in the radiative forcing (RF). This sensitivity is often represented by the equilibrium climate sensitivity, but this quantity is poorly constrained with significant probabilities for high values. In this work the equilibrium climate sensitivity (ECS) is estimated based on observed near-surface temperature change from the instrumental record, changes in ocean heat content and detailed RF time series. RF time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanisms are estimated and the cloud lifetime effect and the semi-direct effect, which are not RF mechanisms in a strict sense, are included in the analysis. The RF time series are linked to the observations of ocean heat content and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS from the data. The posterior mean of the ECS is 1.9˚C with 90% credible interval (C.I.) ranging from 1.2 to 2.9˚C, which is tighter than previously published estimates. Observational data up to and including year 2010 are used in this study. This is at least ten additional years compared to the majority of previously published studies that have used the instrumental record in attempts to constrain the ECS. We show that the additional 10 years of data, and especially 10 years of additional ocean heat content data, have significantly narrowed the probability density function of the ECS. If only data up to and including year 2000 are used in the analysis, the 90% C.I. is 1.4 to 10.6˚C with a pronounced heavy tail in line with previous estimates of ECS constrained by observations in the 20th century. Also the transient climate response (TCR) is estimated in this study. Using observational data up to and including year 2010 gives a 90% C.I. of 1.0 to 2.1˚C, while the 90% C.I. is significantly broader ranging from 1.1 to 3

Climate, carbon cycling, and deep-ocean ecosystems.

PubMed

Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S

2009-11-17

Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

Common mode error in Antarctic GPS coordinate time series on its effect on bedrock-uplift estimates

NASA Astrophysics Data System (ADS)

Liu, Bin; King, Matt; Dai, Wujiao

2018-05-01

Spatially-correlated common mode error always exists in regional, or-larger, GPS networks. We applied independent component analysis (ICA) to GPS vertical coordinate time series in Antarctica from 2010 to 2014 and made a comparison with the principal component analysis (PCA). Using PCA/ICA, the time series can be decomposed into a set of temporal components and their spatial responses. We assume the components with common spatial responses are common mode error (CME). An average reduction of ˜40% about the RMS values was achieved in both PCA and ICA filtering. However, the common mode components obtained from the two approaches have different spatial and temporal features. ICA time series present interesting correlations with modeled atmospheric and non-tidal ocean loading displacements. A white noise (WN) plus power law noise (PL) model was adopted in the GPS velocity estimation using maximum likelihood estimation (MLE) analysis, with ˜55% reduction of the velocity uncertainties after filtering using ICA. Meanwhile, spatiotemporal filtering reduces the amplitude of PL and periodic terms in the GPS time series. Finally, we compare the GPS uplift velocities, after correction for elastic effects, with recent models of glacial isostatic adjustment (GIA). The agreements of the GPS observed velocities and four GIA models are generally improved after the spatiotemporal filtering, with a mean reduction of ˜0.9 mm/yr of the WRMS values, possibly allowing for more confident separation of various GIA model predictions.

Nonlinear parametric model for Granger causality of time series

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-06-01

The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

Pearson correlation estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

Many applications in the geosciences call for the joint and objective analysis of irregular time series. For automated processing, robust measures of linear and nonlinear association are needed. Up to now, the standard approach would have been to reconstruct the time series on a regular grid, using linear or spline interpolation. Interpolation, however, comes with systematic side-effects, as it increases the auto-correlation in the time series. We have searched for the best method to estimate Pearson correlation for irregular time series, i.e. the one with the lowest estimation bias and variance. We adapted a kernel-based approach, using Gaussian weights. Pearson correlation is calculated, in principle, as a mean over products of previously centralized observations. In the regularly sampled case, observations in both time series were observed at the same time and thus the allocation of measurement values into pairs of products is straightforward. In the irregularly sampled case, however, measurements were not necessarily observed at the same time. Now, the key idea of the kernel-based method is to calculate weighted means of products, with the weight depending on the time separation between the observations. If the lagged correlation function is desired, the weights depend on the absolute difference between observation time separation and the estimation lag. To assess the applicability of the approach we used extensive simulations to determine the extent of interpolation side-effects with increasing irregularity of time series. We compared different approaches, based on (linear) interpolation, the Lomb-Scargle Fourier Transform, the sinc kernel and the Gaussian kernel. We investigated the role of kernel bandwidth and signal-to-noise ratio in the simulations. We found that the Gaussian kernel approach offers significant advantages and low Root-Mean Square Errors for regular, slightly irregular and very irregular time series. We therefore conclude that it is a good

Coastal ocean acidification and increasing total alkalinity in the northwestern Mediterranean Sea

NASA Astrophysics Data System (ADS)

Kapsenberg, Lydia; Alliouane, Samir; Gazeau, Frédéric; Mousseau, Laure; Gattuso, Jean-Pierre

2017-05-01

Coastal time series of ocean carbonate chemistry are critical for understanding how global anthropogenic change manifests in near-shore ecosystems. Yet, they are few and have low temporal resolution. At the time series station Point B in the northwestern Mediterranean Sea, seawater was sampled weekly from 2007 through 2015, at 1 and 50 m, and analyzed for total dissolved inorganic carbon (CT) and total alkalinity (AT). Parameters of the carbonate system such as pH (pHT, total hydrogen ion scale) were calculated and a deconvolution analysis was performed to identify drivers of change. The rate of surface ocean acidification was -0.0028 ± 0.0003 units pHT yr-1. This rate is larger than previously identified open-ocean trends due to rapid warming that occurred over the study period (0.072 ± 0.022 °C yr-1). The total pHT change over the study period was of similar magnitude as the diel pHT variability at this site. The acidification trend can be attributed to atmospheric carbon dioxide (CO2) forcing (59 %, 2.08 ± 0.01 ppm CO2 yr-1) and warming (41 %). Similar trends were observed at 50 m but rates were generally slower. At 1 m depth, the increase in atmospheric CO2 accounted for approximately 40 % of the observed increase in CT (2.97 ± 0.20 µmol kg-1 yr-1). The remaining increase in CT may have been driven by the same unidentified process that caused an increase in AT (2.08 ± 0.19 µmol kg-1 yr-1). Based on the analysis of monthly trends, synchronous increases in CT and AT were fastest in the spring-summer transition. The driving process of the interannual increase in AT has a seasonal and shallow component, which may indicate riverine or groundwater influence. This study exemplifies the importance of understanding changes in coastal carbonate chemistry through the lens of biogeochemical cycling at the land-sea interface. This is the first coastal acidification time series providing multiyear data at high temporal resolution. The data confirm rapid warming in

Characterizing time series via complexity-entropy curves

NASA Astrophysics Data System (ADS)

Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

2017-06-01

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

Near-Real-Time Sismo-acoustic Submarine Station for offshore monitoring

NASA Astrophysics Data System (ADS)

D'Anna, Giuseppe; D'Alessandro, Antonino; Fertitta, Gioacchino; Fraticelli, Nicola; Calore, Daniele

2016-04-01

From the early 1980's, Italian seismicity is monitored by the National Seismic Network (NSN). The network has been considerably enhanced by INGV since 2005 by 24-bit digital stations equipped with broad-band sensors. The NSN is nowadays constituted by about 300 on-land seismic station able to detect and locate also small magnitude earthquake in the whole Italian peninsula. However, the lack of offshore seismic stations does not allow the accurate estimation of hypocentral and focal parameters of small magnitude earthquakes occurring in offshore areas. As in the Mediterranean area there is an intense offshore seismic activity, an extension of the seismic monitoring to the sea would be beneficial. There are two types of stations that could be used to extend the network towards the sea: the first type is connected to the coast though a cable, the second type is isolated (or stand alone) and works autonomously. Both solutions have serious limitations: the first one, for several technical and economic problems, linked to the indispensable transmission/alimentation cable, cannot be installed far from the coast; the second one, allows access to the recorded data, only after they are recovered from the seabed. It is clear that these technical solutions are not suitable for the real time monitoring of the offshore seismicity or for the realization of a tsunami warning system. For this reason, in early 2010, the OBSLab of Gibilmanna begins the design of a submarine station able to overcome the limitations of the two systems above. The station isbuilt under the project EMSO-MedIT. The two stations built have already been tested in dock and ready for installation. One of this station will be installed, in few time, in the southern Tyrrhenian Sea, near the epicentre of the Palermo 2002 main shock. The sea bottom station will be equipped with 2 very broadband 3C seismometers, a broad band hydrophone, a differential and an absolute pressure gauge. The station includes a submarine

Forbidden patterns in financial time series

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano

2008-03-01

The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, such as Lyapunov exponent or Kolmogorov entropy, thus allowing to separate deterministic (usually chaotic) from random series; however, it requires fewer values of the series to be calculated, and it is suitable for using with small datasets. In this paper, the appearance of forbidden patterns is studied in different economical indicators such as stock indices (Dow Jones Industrial Average and Nasdaq Composite), NYSE stocks (IBM and Boeing), and others (ten year Bond interest rate), to find evidence of deterministic behavior in their evolutions. Moreover, the rate of appearance of the forbidden patterns is calculated, and some considerations about the underlying dynamics are suggested.

Analysis of Nonstationary Time Series for Biological Rhythms Research.

PubMed

Leise, Tanya L

2017-06-01

This article is part of a Journal of Biological Rhythms series exploring analysis and statistics topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article on time series analysis for biological rhythms, we describe some methods for assessing the rhythmic properties of time series, including tests of whether a time series is indeed rhythmic. Because biological rhythms can exhibit significant fluctuations in their period, phase, and amplitude, their analysis may require methods appropriate for nonstationary time series, such as wavelet transforms, which can measure how these rhythmic parameters change over time. We illustrate these methods using simulated and real time series.

Reducing The Station-to Station Variability of Umkehr Ozone Trends Using SAGE Measurements

NASA Technical Reports Server (NTRS)

Newchurch, Mike; Allen, Mark; Cunnold, Derek; Herman, Ben; Mateer, Carl

2000-01-01

This proposed research sought to use SAGE I and II ozone and aerosol measurements to reduce the variability in ozone trends, principally, but not exclusively, in layer 8 (40 km) derived from multiple Umkehr stations. Building on our experience with both SAGE and Umkehr data, we proposed to commence at the very beginning of the Umkehr process (measured radiance ratios) and proceed through the fitting and inversion processes in conjunction with radiative transfer calculations to establish a consistent, reliable time series of Umkehr ozone profiles at a number of stations. We expected to be able to reconcile the present discrepancies between SAGE and Umkehr trends in the upper stratosphere and, in particular, to reduce the variability in trend estimates among mid-latitude Umkehr stations.

Complex network approach to fractional time series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Manshour, Pouya

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacencymore » matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.« less

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

BRAD BRDY and BRD1 GPS Station RINEX Files 09-17-2015

DOE Data Explorer

Corne Kreemer

2015-09-17

CSV files with links to RINEX data for stations BRAD and BRDY for all days after those reported previous (i.e., since 21-JAN-2015) Links to websites that show the position time-series of both stations.

Seasonal and interannual variability in deep ocean particle fluxes at the Oceanic Flux Program (OFP)/Bermuda Atlantic Time Series (BATS) site in the western Sargasso Sea near Bermuda

NASA Astrophysics Data System (ADS)

Conte, Maureen H.; Ralph, Nate; Ross, Edith H.

Since 1978, the Oceanic Flux Program (OFP) time-series sediment traps have measured particle fluxes in the deep Sargasso Sea near Bermuda. There is currently a 20+yr flux record at 3200-m depth, a 12+yr flux at 1500-m depth, and a 9+yr record at 500-m depth. Strong seasonality is observed in mass flux at all depths, with a flux maximum in February-March and a smaller maximum in December-January. There is also significant interannual variability in the flux, especially with respect to the presence/absence of the December-January flux maximum and in the duration of the high flux period in the spring. The flux records at the three depths are surprisingly coherent, with no statistically significant temporal lag between 500 and 3200-m fluxes at our biweekly sample resolution. Bulk compositional data indicate an extremely rapid decrease in the flux of organic constituents with depth between 500 and 1500-m, and a smaller decrease with depth between 1500 and 3200-m depth. In contrast, carbonate flux is uniform or increases slightly between 500 and 1500-m, possibly reflecting deep secondary calcification by foraminifera. The lithogenic flux increases by over 50% between 500 and 3200-m depth, indicating strong deep water scavenging/repackaging of suspended lithogenic material. Concurrent with the rapid changes in flux composition, there is a marked reduction in the heterogeneity of the sinking particle pool with depth, especially within the mesopelagic zone. By 3200-m depth, the bulk composition of the sinking particle pool is strikingly uniform, both seasonally and over variations in mass flux of more than an order of magnitude. These OFP results provide strong indirect evidence for the intensity of reprocessing of the particle pool by resident zooplankton within mesopelagic and bathypelagic waters. The rapid loss of organic components, the marked reduction in the heterogeneity of the bulk composition of the flux, and the increase in terrigenous fluxes with depth are most

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance.

PubMed

Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip

2016-06-28

The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PubMed Central

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PubMed

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Stochastic nature of series of waiting times.

PubMed

Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H; Salehi, E; Behjat, E; Qorbani, M; Nezhad, M Khazaei; Zirak, M; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M Reza Rahimi

2013-06-01

Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the "waiting times" series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

On demand processing of climate station sensor data

NASA Astrophysics Data System (ADS)

Wöllauer, Stephan; Forteva, Spaska; Nauss, Thomas

2015-04-01

Large sets of climate stations with several sensors produce big amounts of finegrained time series data. To gain value of this data, further processing and aggregation is needed. We present a flexible system to process the raw data on demand. Several aspects need to be considered to process the raw data in a way that scientists can use the processed data conveniently for their specific research interests. First of all, it is not feasible to pre-process the data in advance because of the great variety of ways it can be processed. Therefore, in this approach only the raw measurement data is archived in a database. When a scientist requires some time series, the system processes the required raw data according to the user-defined request. Based on the type of measurement sensor, some data validation is needed, because the climate station sensors may produce erroneous data. Currently, three validation methods are integrated in the on demand processing system and are optionally selectable. The most basic validation method checks if measurement values are within a predefined range of possible values. For example, it may be assumed that an air temperature sensor measures values within a range of -40 °C to +60 °C. Values outside of this range are considered as a measurement error by this validation method and consequently rejected. An other validation method checks for outliers in the stream of measurement values by defining a maximum change rate between subsequent measurement values. The third validation method compares measurement data to the average values of neighboring stations and rejects measurement values with a high variance. These quality checks are optional, because especially extreme climatic values may be valid but rejected by some quality check method. An other important task is the preparation of measurement data in terms of time. The observed stations measure values in intervals of minutes to hours. Often scientists need a coarser temporal resolution

Statistical Features of Deep-ocean Tsunamis Based on 30 Years of Bottom Pressure Observations in the Northeast Pacific

NASA Astrophysics Data System (ADS)

Fine, I.; Thomson, R.; Chadwick, W. W., Jr.; Davis, E. E.; Fox, C. G.

2016-12-01

We have used a set of high-resolution bottom pressure recorder (BPR) time series collected at Axial Seamount on the Juan de Fuca Ridge beginning in 1986 to examine tsunami waves of seismological origin in the northeast Pacific. These data are a combination of autonomous, internally-recording battery-powered instruments and cabled instruments on the OOI Cabled Array. Of the total of 120 tsunami events catalogued for the coasts of Japan, Alaska, western North America and Hawaii, we found evidence for 38 events in the Axial Seamount BPR records. Many of these tsunamis were not observed along the adjacent west coast of the USA and Canada because of the much higher noise level of coastal locations and the lack of digital tide gauge data prior to 2000. We have also identified several tsunamis of apparent seismological origin that were observed at coastal stations but not at the deep ocean site. Careful analysis of these observations suggests that they were likely of meteorological origin. Analysis of the pressure measurements from Axial Seamount, along with BPR measurements from a nearby ODP CORK (Ocean Drilling Program Circulation Obviation Retrofit Kit) borehole and DART (Deep-ocean Assessment and Reporting of Tsunamis) locations, reveals features of deep-ocean tsunamis that are markedly different from features observed at coastal locations. Results also show that the energy of deep-ocean tsunamis can differ significantly among the three sets of stations despite their close spatial spacing and that this difference is strongly dependent on the direction of the incoming tsunami waves. These deep-ocean observations provide the most comprehensive statistics possible for tsunamis in the Pacific Ocean over the past 30 years. New insight into the distribution of tsunami amplitudes and wave energy derived from the deep-ocean sites should prove useful for long-term tsunami prediction and mitigation for coastal communities along the west coast of the USA and Canada.

Single-station 6C beamforming

NASA Astrophysics Data System (ADS)

Nakata, N.; Hadziioannou, C.; Igel, H.

2017-12-01

Six-component measurements of seismic ground motion provide a unique opportunity to identify and decompose seismic wavefields into different wave types and incoming azimuths, as well as estimate structural information (e.g., phase velocity). By using the relationship between the transverse component and vertical rotational motion for Love waves, we can find the incident azimuth of the wave and the phase velocity. Therefore, when we scan the entire range of azimuth and slownesses, we can process the seismic waves in a similar way to conventional beamforming processing, without using a station array. To further improve the beam resolution, we use the distribution of amplitude ratio between translational and rotational motions at each time sample. With this beamforming, we decompose multiple incoming waves by azimuth and phase velocity using only one station. We demonstrate this technique using the data observed at Wettzell (vertical rotational motion and 3C translational motions). The beamforming results are encouraging to extract phase velocity at the location of the station, apply to oceanic microseism, and to identify complicated SH wave arrivals. We also discuss single-station beamforming using other components (vertical translational and horizontal rotational components). For future work, we need to understand the resolution limit of this technique, suitable length of time windows, and sensitivity to weak motion.

Demonstrating the Alaska Ocean Observing System in Prince William Sound

NASA Astrophysics Data System (ADS)

Schoch, G. Carl; McCammon, Molly

2013-07-01

The Alaska Ocean Observing System and the Oil Spill Recovery Institute developed a demonstration project over a 5 year period in Prince William Sound. The primary goal was to develop a quasi-operational system that delivers weather and ocean information in near real time to diverse user communities. This observing system now consists of atmospheric and oceanic sensors, and a new generation of computer models to numerically simulate and forecast weather, waves, and ocean circulation. A state of the art data management system provides access to these products from one internet portal at http://www.aoos.org. The project culminated in a 2009 field experiment that evaluated the observing system and performance of the model forecasts. Observations from terrestrial weather stations and weather buoys validated atmospheric circulation forecasts. Observations from wave gages on weather buoys validated forecasts of significant wave heights and periods. There was an emphasis on validation of surface currents forecasted by the ocean circulation model for oil spill response and search and rescue applications. During the 18 day field experiment a radar array mapped surface currents and drifting buoys were deployed. Hydrographic profiles at fixed stations, and by autonomous vehicles along transects, were made to acquire measurements through the water column. Terrestrial weather stations were the most reliable and least costly to operate, and in situ ocean sensors were more costly and considerably less reliable. The radar surface current mappers were the least reliable and most costly but provided the assimilation and validation data that most improved ocean circulation forecasts. We describe the setting of Prince William Sound and the various observational platforms and forecast models of the observing system, and discuss recommendations for future development.

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

PubMed Central

Dakos, Vasilis; Carpenter, Stephen R.; Brock, William A.; Ellison, Aaron M.; Guttal, Vishwesha; Ives, Anthony R.; Kéfi, Sonia; Livina, Valerie; Seekell, David A.; van Nes, Egbert H.; Scheffer, Marten

2012-01-01

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data. PMID:22815897

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

A Real-time 1/16° Global Ocean Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Shriver, J. F.; Rhodes, R. C.; Hurlburt, H. E.; Wallcraft, A. J.; Metzger, E. J.; Smedstad, O. M.; Kara, A. B.

2001-05-01

A 1/16° eddy-resolving global ocean prediction system that uses the NRL Layered Ocean Model (NLOM) has been transitioned to the Naval Oceanographic Office (NAVO), Stennis Space Center, MS. The system gives a real time view of the ocean down to the 50-100 mile scale of ocean eddies and the meandering of ocean currents and fronts, a view with unprecedented resolution and clarity, and demonstrated forecast skill for a month or more for many ocean features. It has been running in real time at NAVO since 19 Oct 2000 with assimilation of real-time altimeter sea surface height (SSH) data (currently ERS-2, GFO and TOPEX/POSEIDON) and sea surface temperature (SST). The model is updated daily and 4-day forecasts are made daily. 30-day forecasts are made once a week. Nowcasts and forecasts using this model are viewable on the web, including SSH, SST and 30-day forecast verification statistics for many zoom regions. The NRL web address is http://www7320.nrlssc.navy.mil/global_nlom/index.html. The NAVO web address is: http://www.navo.navy.mil. Click on "Operational Products", then "Product Search Form", then "Product Type View", then select "Model Navy Layered Ocean Model" and a region and click on "Submit Query". This system is used at NAVO for ocean front and eddy analyses and predictions and to provide accurate sea surface height for use in computing synthetic temperature and salinity profiles, among other applications.

Delay differential analysis of time series.

PubMed

Lainscsek, Claudia; Sejnowski, Terrence J

2015-03-01

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time

Flow networks for Ocean currents

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkenthin, Nora; Marwan, Norbert; Kurths, Jürgen

2014-05-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e., by its high computational complexity, we here introduce a new, discrete construction of flow-networks, which is then applied to static and dynamic velocity fields. Analyzing the flow-networks of prototypical flows we find that our approach can highlight the zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. We also apply the method to time series data of the Equatorial Pacific Ocean Current and the Gulf Stream ocean current for the changing velocity fields, which could not been done before, and analyse the properties of the dynamical system. Flow-networks can be powerful tools to theoretically understand the step from system's dynamics to network's topology that can be analyzed using network measures and is used for shading light on different climatic phenomena.

Stochastic nature of series of waiting times

NASA Astrophysics Data System (ADS)

Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

2013-06-01

Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

Review of current GPS methodologies for producing accurate time series and their error sources

NASA Astrophysics Data System (ADS)

He, Xiaoxing; Montillet, Jean-Philippe; Fernandes, Rui; Bos, Machiel; Yu, Kegen; Hua, Xianghong; Jiang, Weiping

2017-05-01

The Global Positioning System (GPS) is an important tool to observe and model geodynamic processes such as plate tectonics and post-glacial rebound. In the last three decades, GPS has seen tremendous advances in the precision of the measurements, which allow researchers to study geophysical signals through a careful analysis of daily time series of GPS receiver coordinates. However, the GPS observations contain errors and the time series can be described as the sum of a real signal and noise. The signal itself can again be divided into station displacements due to geophysical causes and to disturbing factors. Examples of the latter are errors in the realization and stability of the reference frame and corrections due to ionospheric and tropospheric delays and GPS satellite orbit errors. There is an increasing demand on detecting millimeter to sub-millimeter level ground displacement signals in order to further understand regional scale geodetic phenomena hence requiring further improvements in the sensitivity of the GPS solutions. This paper provides a review spanning over 25 years of advances in processing strategies, error mitigation methods and noise modeling for the processing and analysis of GPS daily position time series. The processing of the observations is described step-by-step and mainly with three different strategies in order to explain the weaknesses and strengths of the existing methodologies. In particular, we focus on the choice of the stochastic model in the GPS time series, which directly affects the estimation of the functional model including, for example, tectonic rates, seasonal signals and co-seismic offsets. Moreover, the geodetic community continues to develop computational methods to fully automatize all phases from analysis of GPS time series. This idea is greatly motivated by the large number of GPS receivers installed around the world for diverse applications ranging from surveying small deformations of civil engineering structures (e