The GravIS Portal: User-friendly Global Mass Variations from GRACE and GRACE-FO

2021 ◽  
Author(s):  
Christoph Dahle ◽  
Eva Boergens ◽  
Henryk Dobslaw ◽  
Andreas Groh ◽  
Ingo Sasgen ◽  
...  
Keyword(s):  
Time Series ◽  
Information Service ◽  
Research Centre ◽  
Data Sets ◽  
Ocean Bottom ◽  
Climatic Regions ◽  
Uncertainty Estimates ◽  
Terrestrial Water ◽  
User Friendly ◽  
Global Mean

<p>The German Research Centre for Geosciences (GFZ) maintains the “Gravity Information Service” (GravIS, gravis.gfz-potsdam.de) portal in collaboration with the Alfred-Wegener-Institute (AWI) and Technische Universität Dresden. Main objective of this portal is the dissemination of data describing mass variations in the Earth system based on observations of the satellite gravimetry missions GRACE and GRACE-FO.</p><p>The provided data sets encompass products of terrestrial water storage (TWS) variations over the continents, ocean bottom pressure (OBP) variations from which global mean barystatic sea-level rise can be estimated, and mass changes of the ice sheets in Greenland and Antarctica. All data sets are provided as time series of regular grids for each area, as well as in the form of regional basin averages. Regarding the latter, for the continental TWS data the user can choose between classical river basins and a novel segmentation based on climatic regions. For the oceans, the segmentation into different regions is derived similarly but based on modelled OBP data. All time series are accompanied by realistic uncertainty estimates.</p><p>All data sets can be interactively displayed at the portal and are freely available for download. This contribution aims to show the features and possibilities of the GravIS portal to researchers without a dedicated geodetic background, working in the fields of hydrology, oceanography, and cryosphere.</p>

Status of GFZ's GRACE/GRACE-FO RL06 Level-2 and Level-3 Data Products

2020 ◽  
Author(s):  
Christoph Dahle ◽  
Michael Murböck ◽  
Frank Flechtner ◽  
Rolf König ◽  
Henryk Dobslaw ◽  
...  
Keyword(s):  
Information Service ◽  
Gravity Potential ◽  
Research Centre ◽  
Science Data ◽  
Data Products ◽  
Terrestrial Water ◽  
Level 3 ◽  
User Friendly ◽  
Grace Mission ◽  
Level 2

<p>The GRACE Follow-On (GRACE-FO) mission was successfully launched on May 22<sup>nd</sup>, 2018 and continues the 15-year data record of monthly global mass changes from the GRACE mission (2002-2017). The German Research Centre for Geosciences (GFZ) as part of the GRACE/GRACE-FO Science Data System (SDS) has recently reprocessed the complete GRACE mission data (RL06 in the SDS nomenclature). These RL06 processing standards serve as common baseline for the continuation with GRACE-FO data.</p><p>This presentation provides an overview of the current processing status and the validation of the GFZ GRACE/GRACE-FO RL06 gravity field products. Besides its Level-2 products (monthly sets of spherical harmonic coefficients representing the Earth's gravity potential), GFZ additionally generates user-friendly Level-3 products in collaboration with the Alfred-Wegener-Institut (AWI) and TU Dresden. These Level-3 data products comprise dedicated mass anomaly products of terrestrial water storage over non-glaciated regions, bottom pressure variations in the oceans and ice mass changes in Antarctica and Greenland, available via GFZ's Gravity Information Service (GravIS) portal (http://gravis.gfz-potsdam.de/).</p>

Intercomparisons of CSR GRACE-FO Solutions with External Data Sets

2020 ◽  
Author(s):  
Mark Tamisiea ◽  
Benjamin Krichman ◽  
Himanshu Save ◽  
Srinivas Bettadpur ◽  
Zhigui Kang ◽  
...  
Keyword(s):  
Deep Ocean ◽  
Data Sets ◽  
Ocean Bottom ◽  
Data Set ◽  
The North ◽  
External Data ◽  
Low Degree ◽  
Terrestrial Water ◽  
Land Data Assimilation

<p>To assess the quality of the CSR solutions, we compare results against external data sets that have contemporaneous availability.  These evaluations fall into three categories: changes in terrestrial water storage against data from the North American and Global Land Data Assimilation Systems, variations in ocean bottom pressure against data from the Deep Ocean Assessment of Tsunami Network, and estimates of the low degree and order Stokes coefficients compared against those inferred from satellite laser ranging observations (i.e. the CSR monthly 5x5 gravity harmonics from the MEaSUREs project).   As the mission provides a unique measurement of mass changes in the Earth system, evaluation of the new solutions against other data sets and models is challenging.  Thus, we primarily focus on relative agreement with these data set with the GRACE-FO solutions in relation to the historic agreement of the data sets with the GRACE solutions.</p>

scholarly journals The GFZ GRACE RL06 Monthly Gravity Field Time Series: Processing Details and Quality Assessment

2019 ◽  
Vol 11 (18) ◽  
pp. 2116 ◽  
Author(s):  
Christoph Dahle ◽  
Michael Murböck ◽  
Frank Flechtner ◽  
Henryk Dobslaw ◽  
Grzegorz Michalak ◽  
...  
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Transport Processes ◽  
Research Centre ◽  
Ocean Bottom ◽  
Science Data ◽  
Surface Mass ◽  
Surface Mass Transport ◽  
Grace Mission ◽  
Gravity Field Models

Time-variable gravity field models derived from observations of the Gravity Recovery and Climate Experiment (GRACE) mission, whose science operations phase ended in June 2017 after more than 15 years, enabled a multitude of studies of Earth’s surface mass transport processes and climate change. The German Research Centre for Geosciences (GFZ), routinely processing such monthly gravity fields as part of the GRACE Science Data System, has reprocessed the complete GRACE mission and released an improved GFZ GRACE RL06 monthly gravity field time series. This study provides an insight into the processing strategy of GFZ RL06 which has been considerably changed with respect to previous GFZ GRACE releases, and modifications relative to the precursor GFZ RL05a are described. The quality of the RL06 gravity field models is analyzed and discussed both in the spectral and spatial domain in comparison to the RL05a time series. All results indicate significant improvements of about 40% in terms of reduced noise. It is also shown that the GFZ RL06 time series is a step forward in terms of consistency, and that errors of the gravity field coefficients are more realistic. These findings are confirmed as well by independent validation of the monthly GRACE models, as done in this work by means of ocean bottom pressure in situ observations and orbit tests with the GOCE satellite. Thus, the GFZ GRACE RL06 time series allows for a better quantification of mass changes in the Earth system.

Validation of Earth rotation time series by comparison of their sub-daily to sub-monthly excitation signal with simulated geophysical fluid model excitations

2020 ◽  
Author(s):  
Robert Dill ◽  
Henryk Dobslaw ◽  
Maik Thomas ◽  
Hellmers Hendrik ◽  
Thaller Daniela ◽  
...  
Keyword(s):  
Time Series ◽  
Angular Momentum ◽  
Land Surface ◽  
Earth Rotation ◽  
Fluid Model ◽  
Ocean Bottom ◽  
Terrestrial Water ◽  
Band Pass ◽  
International Services ◽  
Geophysical Fluids

<p>Time-variations in the orientation of the solid Earth are largely governed by the exchange of angular momentum with the surface geophysical fluids of atmosphere, oceans, and the land surface. Modelled fields of atmospheric winds, atmospheric surface pressure, ocean currents, ocean bottom pressure, and terrestrial water storage allow calculating effective angular momentum (EAM) functions that can be compared to geodetic angular momentum functions (GAM) derived from observed Earth Orientation Parameters (EOP) via the Liouville equation. Especially in the high-frequency range, currently available global geophysical fluid models provide highly reliable information about angular momentum transfers that determine the orientation changes of the Earth.</p><p>In this contribution, we investigate the extent to which the modelled Earth rotation angular momentum functions processed at GFZ can be used to evaluate time series of EOP processed from different geodetic space techniques at periods between 2 and 60 days. We therefore compare the time series from various sources that are based on individual techniques (e.g., VLBI[TD1], GNSS, SLR, and DORIS) only, and also combined solutions that are processed at different institutions (e.g., JPL, GFZ, BKG[TD2], DGFI-TUM) or published by international services (e.g., IERS, IGS, IVS[TD3] ). By calculating differences from all possible pairs of EAM and GAM and by utilizing both band-pass filtering and spectral analysis techniques, we will elaborate the systematic differences between excitation functions from different sources that are expected to help identifying deficits in geodetic data processing and/or numerical modelling.</p>

Is There Convergence in National Alcohol Consumption Patterns? Evidence from a Compositional Time Series Approach

2018 ◽  
Vol 13 (1) ◽  
pp. 92-98 ◽  
Author(s):  
Terence C. Mills
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Alcohol Consumption ◽  
Statistical Approach ◽  
Consumption Patterns ◽  
Data Sets ◽  
Share Data ◽  
Time Series Approach ◽  
Global Mean ◽  
Jel Classifications

AbstractHolmes and Anderson (2017a) introduce two extensive data sets on world alcohol consumption and expenditure and with them investigate, among other things, the possible convergence of national alcohol consumption patterns using wine, beer, and spirit shares. Such share data define a composition, on which conventional statistical analysis using covariances and correlations is invalid. This note reanalyses the data using techniques appropriate for a composition and introduces a statistic that can validly track the variation in national shares around the global mean through time. This variability statistic shows that such convergence of national alcohol patterns has clearly taken place over the period 1961 to 2014 and thus confirms Holmes and Anderson's findings using a valid statistical approach. (JEL Classifications: C18, D12, L66)

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

scholarly journals Analysis of Ocean Bottom Pressure Anomalies and Seismic Activities in the MedRidge Zone

2021 ◽  
Vol 13 (7) ◽  
pp. 1242
Author(s):  
Hakan S. Kutoglu ◽  
Kazimierz Becek
Keyword(s):  
Time Series ◽  
Mass Change ◽  
Ocean Bottom ◽  
Bottom Pressure ◽  
Vertical Movements ◽  
Ocean Bottom Pressure ◽  
Continuous Mass ◽  
Mediterranean Ridge Accretionary Complex ◽  
Gravity Recovery ◽  
Periodic Components

The Mediterranean Ridge accretionary complex (MAC) is a product of the convergence of Africa–Europe–Aegean plates. As a result, the region exhibits a continuous mass change (horizontal/vertical movements) that generates earthquakes. Over the last 50 years, approximately 430 earthquakes with M ≥ 5, including 36 M ≥ 6 earthquakes, have been recorded in the region. This study aims to link the ocean bottom deformations manifested through ocean bottom pressure variations with the earthquakes’ time series. To this end, we investigated the time series of the ocean bottom pressure (OBP) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The OBP time series comprises a decreasing trend in addition to 1.02, 1.52, 4.27, and 10.66-year periodic components, which can be explained by atmosphere, oceans, and hydrosphere (AOH) processes, the Earth’s pole movement, solar activity, and core–mantle coupling. It can be inferred from the results that the OBP anomalies time series/mass change is linked to a rising trend and periods in the earthquakes’ energy time series. Based on this preliminary work, ocean-bottom pressure variation appears to be a promising lead for further research.

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