Revealing the Contemporary Kinematics of Antarctic Plate Using GPS and GRACE Data

2022 ◽  
pp. 343-359
Author(s):  
P. S. Sunil ◽  
Ajish P. Saji ◽  
K. Vijay Kumar ◽  
M. Ponraj ◽  
S. Amirtharaj ◽  
...  
Keyword(s):  
Grace Data

ASSESSMENT OF THE SPATIO-TEMPORAL VARIATIONS IN GROUND WATER STORAGE USING DOWN-SCALED GRACE DATA

2018 ◽  
Author(s):  
Hossein Sahour ◽  
◽  
Mohamed Sultan ◽  
Karem Abdelmohsen ◽  
Sita Karki ◽  
...  
Keyword(s):  
Ground Water ◽  
Water Storage ◽  
Temporal Variations ◽  
Grace Data ◽  
Spatio Temporal

Seasonal and trend analysis of TWS for the Indo-Gangetic plain using GRACE data

2019 ◽  
Vol 35 (12) ◽  
pp. 1343-1359
Author(s):  
Saurabh Srivastava ◽  
Onkar Dikshit
Keyword(s):  
Trend Analysis ◽  
Gangetic Plain ◽  
Grace Data ◽  
Indo Gangetic Plain

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

scholarly journals Magnetometer data from the GRACE satellite duo

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Nils Olsen
Keyword(s):  
Mean Squared Error ◽  
Incident Angle ◽  
Magnetic Data ◽  
Grace Data ◽  
Magnetometer Data ◽  
Geophysical Investigations ◽  
Grace Satellites ◽  
Grace Satellite ◽  
The Difference ◽  
Grace Mission

AbstractThis paper describes and discusses the preprocessing and calibration of the magnetic data taken by the navigational magnetometers onboard the two GRACE satellites, with focus on the almost 10 years period from January 2008 to the end of the GRACE mission in October 2017 for which 1-Hz magnetic data are available. A calibration of the magnetic data is performed by comparing the raw magnetometer sensor readings with model magnetic vector values as provided by the CHAOS-7 geomagnetic field model for the time and position of the GRACE data. The presented approach also accounts for magnetic disturbances produced by the satellite’s magnetorquer and for temperature effects, which are parametrized by the Sun incident angle. The root-mean-squared error of the difference between the calibrated data and CHAOS-7 model values is about 10 nT, which makes the GRACE magnetometer data relevant for geophysical investigations.

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 Predicting groundwater level changes using GRACE data

2013 ◽  
Vol 49 (9) ◽  
pp. 5900-5912 ◽  
Author(s):  
Alexander Y. Sun
Keyword(s):  
Groundwater Level ◽  
Grace Data ◽  
Groundwater Level Changes

scholarly journals GRACETOOLS—GRACE Gravity Field Recovery Tools

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 350 ◽  
Author(s):  
Neda Darbeheshti ◽  
Florian Wöske ◽  
Matthias Weigelt ◽  
Christopher Mccullough ◽  
Hu Wu
Keyword(s):  
Open Source ◽  
Gravity Field ◽  
Transition Matrix ◽  
State Transition ◽  
Satellite Observations ◽  
Gravity Field Recovery ◽  
Grace Data ◽  
Range Rate ◽  
Analysis Platform ◽  
Recovery Tools

This paper introduces GRACETOOLS, the first open source gravity field recovery tool using GRACE type satellite observations. Our aim is to initiate an open source GRACE data analysis platform, where the existing algorithms and codes for working with GRACE data are shared and improved. We describe the first release of GRACETOOLS that includes solving variational equations for gravity field recovery using GRACE range rate observations. All mathematical models are presented in a matrix format, with emphasis on state transition matrix, followed by details of the batch least squares algorithm. At the end, we demonstrate how GRACETOOLS works with simulated GRACE type observations. The first release of GRACETOOLS consist of all MATLAB M-files and is publicly available at Supplementary Materials.

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