scholarly journals Surface mass redistribution inversion from global GPS deformation and Gravity Recovery and Climate Experiment (GRACE) gravity data

2005 ◽  
Vol 110 (B9) ◽  
Author(s):  
J. Kusche ◽  
E. J. O. Schrama
Keyword(s):  
Gravity Data ◽  
Surface Mass ◽  
Mass Redistribution ◽  
Gravity Recovery ◽  
Surface Mass Redistribution

scholarly journals Constrained Linear Deconvolution of GRACE Anomalies to Correct Spatial Leakage

2020 ◽  
Vol 12 (11) ◽  
pp. 1798
Author(s):  
Ki-Weon Seo ◽  
Seokhoon Oh ◽  
Jooyoung Eom ◽  
Jianli Chen ◽  
Clark R. Wilson
Keyword(s):  
Climate Forcing ◽  
Mass Change ◽  
Data Types ◽  
Surface Mass ◽  
Grace Data ◽  
Mass Redistribution ◽  
Grace Satellites ◽  
Earth’S Climate ◽  
Gravity Recovery ◽  
Mass Loads

Time-varying gravity observed by the Gravity Recovery and Climate Experiment (GRACE) satellites measures surface water and ice mass redistribution driven by weather and climate forcing and has emerged as one of the most important data types in measuring changes in Earth’s climate. However, spatial leakage of GRACE signals, especially in coastal areas, has been a recognized limitation in quantitatively assessing mass change. It is evident that larger terrestrial signals in coastal regions spread into the oceans and vice versa and various remedies have been developed to address this problem. An especially successful one has been Forward Modeling but it requires knowledge of geographical locations of mass change to be fully effective. In this study, we develop a new method to suppress leakage effects using a linear least squares operator applied to GRACE spherical harmonic data. The method is effectively a constrained deconvolution of smoothing inherent in GRACE data. It assumes that oceanic mass changes near the coast are negligible compared to terrestrial changes, with additional spatial regularization constraints. Some calibration of constraint weighting is required. We apply the method to estimate surface mass loads over Australia using both synthetic and real GRACE data. Leakage into the oceans is effectively suppressed and when compared with mascon solutions there is better performance over interior basins.

scholarly journals The Assessment of Hydrologic- and Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO Data Assimilation

2021 ◽  
Vol 13 (2) ◽  
pp. 235
Author(s):  
Natthachet Tangdamrongsub ◽  
Michal Šprlák
Keyword(s):  
Data Assimilation ◽  
Gravity Data ◽  
Regional Scale ◽  
Vertical Motion ◽  
Vertical Displacement ◽  
Spatial Coverage ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Gravity Recovery ◽  
Central South

The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.

scholarly journals Accuracy Analysis of Gravity Field Changes from Grace RL06 and RL05 Data Compared to in Situ Gravimetric Measurements in the Context of Choosing Optimal Filtering Type

2020 ◽  
Vol 55 (3) ◽  
pp. 100-117
Author(s):  
Viktor Szabó ◽  
Dorota Marjańska
Keyword(s):  
Gravity Field ◽  
Ocean Circulation ◽  
Gravity Data ◽  
The Other ◽  
Subsurface Water ◽  
Satellite Gravity ◽  
Absolute Gravimetry ◽  
The Earth ◽  
Gravity Measurements ◽  
Gravity Recovery

AbstractGlobal satellite gravity measurements provide unique information regarding gravity field distribution and its variability on the Earth. The main cause of gravity changes is the mass transportation within the Earth, appearing as, e.g. dynamic fluctuations in hydrology, glaciology, oceanology, meteorology and the lithosphere. This phenomenon has become more comprehensible thanks to the dedicated gravimetric missions such as Gravity Recovery and Climate Experiment (GRACE), Challenging Minisatellite Payload (CHAMP) and Gravity Field and Steady-State Ocean Circulation Explorer (GOCE). From among these missions, GRACE seems to be the most dominating source of gravity data, sharing a unique set of observations from over 15 years. The results of this experiment are often of interest to geodesists and geophysicists due to its high compatibility with the other methods of gravity measurements, especially absolute gravimetry. Direct validation of gravity field solutions is crucial as it can provide conclusions concerning forecasts of subsurface water changes. The aim of this work is to present the issue of selection of filtration parameters for monthly gravity field solutions in RL06 and RL05 releases and then to compare them to a time series of absolute gravimetric data conducted in quasi-monthly measurements in Astro-Geodetic Observatory in Józefosław (Poland). The other purpose of this study is to estimate the accuracy of GRACE temporal solutions in comparison with absolute terrestrial gravimetry data and making an attempt to indicate the significance of differences between solutions using various types of filtration (DDK, Gaussian) from selected research centres.

scholarly journals Accelerating changes in ice mass within Greenland, and the ice sheet’s sensitivity to atmospheric forcing

2019 ◽  
Vol 116 (6) ◽  
pp. 1934-1939 ◽  
Author(s):  
Michael Bevis ◽  
Christopher Harig ◽  
Shfaqat A. Khan ◽  
Abel Brown ◽  
Frederik J. Simons ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Climate Model ◽  
Regional Climate ◽  
Atmospheric Forcing ◽  
Surface Mass ◽  
The North ◽  
Global Positioning ◽  
The North Atlantic Oscillation ◽  
Gravity Recovery ◽  
Gps Observations

From early 2003 to mid-2013, the total mass of ice in Greenland declined at a progressively increasing rate. In mid-2013, an abrupt reversal occurred, and very little net ice loss occurred in the next 12–18 months. Gravity Recovery and Climate Experiment (GRACE) and global positioning system (GPS) observations reveal that the spatial patterns of the sustained acceleration and the abrupt deceleration in mass loss are similar. The strongest accelerations tracked the phase of the North Atlantic Oscillation (NAO). The negative phase of the NAO enhances summertime warming and insolation while reducing snowfall, especially in west Greenland, driving surface mass balance (SMB) more negative, as illustrated using the regional climate model MAR. The spatial pattern of accelerating mass changes reflects the geography of NAO-driven shifts in atmospheric forcing and the ice sheet’s sensitivity to that forcing. We infer that southwest Greenland will become a major future contributor to sea level rise.

scholarly journals Quantifying sediment mass redistribution from joint time-lapse gravimetry and photogrammetry surveys

2019 ◽  
Author(s):  
Maxime Mouyen ◽  
Philippe Steer ◽  
Kuo-Jen Chang ◽  
Nicolas Le Moigne ◽  
Cheinway Hwang ◽  
...  
Keyword(s):  
Gravity Data ◽  
River Sediments ◽  
Time Lapse ◽  
New Combination ◽  
Gravity Changes ◽  
Sediment Mass ◽  
Mass Redistribution ◽  
Sediment Redistribution ◽  
Average Loss ◽  
Slow Landslide

Abstract. The accurate quantification of sediment mass redistribution is central to the study of surface processes, yet it remains a challenging task. Here we test a new combination of terrestrial gravity and drone photogrammetry methods to quantify sediment redistribution over a 1-km2 area. Gravity and photogrammetry are complementary methods. Indeed, gravity changes are sensitive to mass changes and to their location. Thus, by using photogrammetry data to constrain this location, the sediment mass can be properly estimated from the gravity data. We carried out 3 joint gravity-photogrammetry surveys, once a year in 2015, 2016 and 2017 over a 1-km2 area in southern Taiwan featuring both a wide meander of the Laonong River and a slow landslide. We first removed the gravity changes from non-sediment effects, such as tides, groundwater, surface displacements and air pressure variations. Then, we inverted the density of the sediment, with an attempt to distinguish the density of the landslide from the density of the river sediments. We eventually estimate an average loss of 4.7 ± 0.4 × 109 kg of sediment from 2015 to 2017, mostly due to the slow landslide. Although the gravity devices used in this study are expensive and need week-long surveys, new instrumentation progresses shall enable dense and continuous measurements at lower cost, making this method relevant to improve the estimation of erosion, sediment transfer and deposition in landscapes.

scholarly journals Multiple-data-based monthly geopotential model set LDCmgm90

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Wei Chen ◽  
Jiesi Luo ◽  
Jim Ray ◽  
Nan Yu ◽  
Jian Cheng Li
Keyword(s):  
Gravity Data ◽  
Gravity Models ◽  
Geopotential Model ◽  
Satellite Mission ◽  
Stripe Pattern ◽  
Angular Momenta ◽  
Multiple Data ◽  
Model Set ◽  
Gravity Recovery

Abstract While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Difference Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Difference Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.

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