GRACE-FO RL06 Level-2 Gravity Fields and Mascon Solutions from CSR: Assessments and Future Plans

Save, Himanshu; Bettadpur, Srinivas; Nagel, Peter; Pie, Nadège; Poole, Steven R.; Tamisiea, M. E.; Kang, Z.

doi:10.5194/egusphere-egu22-11161

Cited by 21 publications

(26 citation statements)

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“…To validate our results, we compare our estimates of integrated water changes over specific regions with the RL06 solutions of the Center for Space Research, University of Texas at Austin (CSR; Save, 2020; Save et al., 2016) and the Goddard Space Flight Center (GSFC; Loomis et al., 2019). Differences between the three analysis approaches mean that one should not necessarily expect to find exact agreement between the solutions.…”

Section: Resultsmentioning

confidence: 97%

“…The Level‐1B grace data are available at https://podaac-tools.jpl.nasa.gov/drive/files/allData/grace/L1B. CSR and GSFC solutions are available respectively at http://www2.csr.utexas.edu/grace/RL06_mascons.html (Save et al., 2016; Save, 2020) and https://earth.gsfc.nasa.gov/geo/data/grace-mascons (Loomis et al., 2019). All data used for this study are freely available.…”

Section: Data Availability Statementmentioning

confidence: 99%

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ANU GRACE Data Analysis: Orbit Modeling, Regularization and Inter‐satellite Range Acceleration Observations

et al. 2022

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Small changes in the strength of the Earth's gravity field caused by the redistribution of mass on the Earth cause subtle changes in the range between the satellites. This is the fundamental observation that made the grace mission unique.A number of different mathematical approaches have been used to parameterize the Earth's gravity field and the convergence of the inversions of the data. Solving for coefficients of spherical harmonic models has been the most common approach used since the start of the grace mission (e.g., Lemoine et al., 2007;Tapley et al., 2004). A common characteristic of the estimated temporal gravity fields is a north-south striped error pattern, in part related to the high correlations between even and odd order coefficients (Swenson & Wahr, 2006

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Section: Resultsmentioning

confidence: 97%

Section: Data Availability Statementmentioning

confidence: 99%

ANU GRACE Data Analysis: Orbit Modeling, Regularization and Inter‐satellite Range Acceleration Observations

et al. 2022

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“…We also compare our joint inversion result with mascon solutions from the Center for Space Research at the University of Texas at Austin (Save, 2020 ; Save et al., 2016 ) and gridded TWS anomalies from the International Combination Service for Time‐variable Gravity Fields (Boergens et al., 2020 ). Unlike the JPL mascon solutions, the CSR mascon solutions do not use any external models as constraints.…”

Section: Datamentioning

confidence: 99%

“…Next, we combine GRACE and GNSS data in a joint inversion to solve for monthly ∆TWS using the JPL mascon data products. Finally, we compare the results from both inversions to GRACE mascon products from the Center for Space Research at the University of Texas at Austin (CSR; Save, 2020 ; Save et al., 2016 ), gridded TWS anomalies from the International Combination Service for Time‐variable Gravity Fields (COST‐G; Boergens et al., 2020 ), the WaterGap Global Hydrological Model (WGHM), and the Global Land Data Assimilation System, and the Catchment Land Surface Model (GLDAS‐2.1, CLSM). Both the GRACE mascon data and GNSS station locations are shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Joint Inversion of GNSS and GRACE for Terrestrial Water Storage Change in California

2022

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Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change (∆TWS) estimates for studying both annual ∆TWS as well as multi‐year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite‐based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate ∆TWS estimates with global coverage but have a low spatial resolution of ∼400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE ∆TWS to produce more accurate spatiotemporal maps of ∆TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to ∆TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long‐term and short‐term mass variations. This allows us to preserve trends, annual, interannual, and multi‐year changes in TWS that were previously challenging to capture by satellite‐based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of ∆TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE ∆TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone.

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“…The time span of product and data related to GRACE are from April 2002 to June 2017, while the data from GRACE-Follow On are from May 2018 to December 2019. CSR global mascon RL06 data can be applied directly without further adjustment of the scale factor, as it is not extensively affected by leakage and measurement errors [36,37]. For some missing short-term monthly data, the linear interpolation method was used.…”

Section: Grace Productsmentioning

confidence: 99%

Influence of Terrestrial Water Storage on Flood Potential Index in the Yangtze River Basin, China

et al. 2022

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In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS based on precipitation and temperature, evaluated the reconstructed TWS data based on Gravity Recovery and Climate Experiment (GRACE)-TWS data, and analyzed and calculated the flood potential index (FPI) in the Yangtze River Basin (YRB). The related influencing factors were analyzed based on the Global Land Data Assimilation System (GLDAS) data and Granger’s causality test. The main conclusions are as follows: (1) although the GRACE-TWS anomaly (GRACE-TWSA) in the YRB showed an increasing trend for the averaged TWSA over all grids in the whole basin (i.e., 0.31 cm/a, p < 0.05), the variable infiltration capacity-soil moisture anomalies (VIC-SMA) showed a decreasing trend (i.e., −0.048 cm/a, p > 0.05) during April 2002–December 2019; (2) a larger relative contribution of detrended precipitation to FPI was found in the Jialingjiang River Basin (JRB), Wujiang River Basin (WRB), Dongting Lake Rivers Basin (DLRB), YinBin-Yichang reaches (YB-YC), and Yichang-Hukou reaches (YC-HK), while the contribution of detrended TWS to FPI in the Poyang Lake Rivers Basin (PLRB) was larger than that in other basins; and (3) the original and detrended soil moisture (SM) and TWS in the YRB showed a significant positive correlation (p < 0.05), while the significant effect of SM on TWS caused a change in FPI in the YRB and its sub-basins. This study is of great significance for the correct understanding of the FPI and the accurate assessment of flood risk.

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GRACE-FO RL06 Level-2 Gravity Fields and Mascon Solutions from CSR: Assessments and Future Plans

Cited by 21 publications

References 0 publications

ANU GRACE Data Analysis: Orbit Modeling, Regularization and Inter‐satellite Range Acceleration Observations

ANU GRACE Data Analysis: Orbit Modeling, Regularization and Inter‐satellite Range Acceleration Observations

Joint Inversion of GNSS and GRACE for Terrestrial Water Storage Change in California

Influence of Terrestrial Water Storage on Flood Potential Index in the Yangtze River Basin, China

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