High‐Resolution GRACE Monthly Spherical Harmonic Solutions

Chen, Qiujie; Shen, Yunzhong; Kusche, Jürgen; Chen, Wu; Chen, Tianyi; Zhang, Xingfu

doi:10.1029/2019jb018892

Cited by 25 publications

(15 citation statements)

References 75 publications

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“…However, these filters all cause the attenuation of real geophysical signals (Tapley et al, 2019) and leakage errors that reach 20 Gt/yr in AIS (Chen et al, 2015;Mu et al, 2017). To mitigate leakage error and increase the spatial resolution, Chen et al (2021) developed the global regularized SHC solutions up to degree and order (d/o) 180 that is comparable with the three global mascon solutions, e.g., CSR (Center for Space Research, the University of Texas) mascon (Save et al, 2016), JPL (Jet Propulsive Laboratory) mascon (Watkins et al, 2015) and GSFC (Goddard Space Flight Center) mascon (Loomis et al, 2019b) solutions. JPL and GSFC mascon solutions are both developed from the GRACE Level-1B observations while CSR mascon solution is generated from the SHCs up to degree and order 120.…”

Section: Introductionmentioning

confidence: 99%

“…Geometry of mascon modeling Swenson and Wahr, 2006),. then estimate the signal variances of all mascons over AIS with the filtered model, same as that inChen et al (2021) andSave et al (2016). However, considering the signal leakage from coastal ice sheets to the buffer zone of AIS (coastal ocean within 600 km, about twice GRACE natural resolution), as suggested bySave et al (2016) the σ i (also called RMS) over the buffer zone are all set to 4 cm for the mascons of 1 °× 1 °grids.…”

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confidence: 99%

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One-degree resolution mascon solution over Antarctic derived from GRACE Level-2 data

et al. 2023

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The mass loss of the Antarctic Ice Sheet (AIS) is an important contributor to global sea-level rise in response to the warming ocean and atmospheric temperatures as well as the changes in current systems and precipitation patterns. In this study, a regional mascon method is developed to squeeze more mass change signals, in which the pseudo-observations of the geopotential are generated from the unfiltered GRACE Level-2 data whereas the regularization matrix is constructed with the prior information derived from filtered GRACE Level-2 data. A series of mascon solutions with 1°×1° equal-area resolution over AIS is derived from the updated Tongji-Grace2018 model spanning April 2002 to December 2016. Compared to the filtering results from P4M6 decorrelation and 100 km Gaussian filtering, our mascon solutions can effectively suppress the strips, improve the spatial resolution over AIS, and get a stronger signal with an improvement of 116.86% in the Antarctic Peninsula Ice Sheet (APIS), and more coincide with the features of glaciers and ice streams, such as the most striking ice mass loss in Totten, Getz, Thwaites and Pine Island, and the ice mass gain in Kamb Ice Stream. During the period from 2002 to 2016, the mass change rates from our mascon solution are −103.6 ± 5.6 Gt/yr, 63.0 ± 4.3 Gt/yr, −143.3 ± 4.9 Gt/yr and −23.29 ± 1.2 Gt/yr in AIS, East AIS, West AIS, and APIS, respectively. The mass change signals at the basin scale are with even more distinguishing features, with the highest mass gain rates of 18.03 ± 1.88 Gt/yr and 14.55 ± 0.60 Gt/yr at Basin 7 and Basin 18, and the highest mass loss rates of −58.57 ± 2.48 Gt/yr and −44.12 ± 2.27 Gt/yr at Basin 21 and Basin 22. Relative to the cumulated surface mass balance from the regional atmospheric climate model, the correlation coefficients of our mascon solutions are 0.91, 0.94, and 0.96 in East AIS, West AIS, and APIS.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

One-degree resolution mascon solution over Antarctic derived from GRACE Level-2 data

et al. 2023

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“…For example, Bruinsma et al (2010) established regularization matrices through the Kaula rule and estimated a series of 10-day gravity field solutions based on Tikhonov regularization; Save et al (2012) used the error characteristics of unconstrained CSR RL04 monthly solutions to design constraint matrices and solved the regularized solutions using the Tikhonov regularization with L-curve method; Q. Chen et al (2021) transformed the spatial constraints from GRACE-based ZHONG ET AL.…”

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confidence: 99%

“…Essentially, the unconstrained mascon solutions are equivalent to the unconstrained SH solutions (Rowlands et al, 2010;Watkins et al, 2015), and the improvements in mascon solutions are primarily attributed to the regularization constraints applied in mascon modeling (Q. Chen et al, 2021;Ran et al, 2018Ran et al, , 2021. Therefore, the SH solutions estimated with regularization constraints are also equivalent to the mascon solutions, and the correlated noise and spatial leakage can be reduced through regularized least-squares estimation.…”

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confidence: 99%

“…Furthermore, to avoid over-smoothing caused by the L-curve and MSE methods as noted in previous studies (Q. Chen et al, 2021;Kusche & Klees, 2002;Xu, 1998), the optimal regularization parameters for the monthly solutions were adaptively determined from the GRACE data itself using variance component estimation (Koch & Kusche, 2002). Finally, a series of constrained monthly gravity field solutions named WHU-GRACE-GPD01s (complete to degree and order 96) from August 2002 to July 2016 were recovered and compared with the official SH solutions (e.g., GFZ, JPL, and CSR RL06) and mascon solutions (e.g., CSR RL06M) at global and regional scales.…”

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confidence: 99%

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WHU‐GRACE‐GPD01s: A Series of Constrained Monthly Gravity Field Solutions Derived From GRACE‐Based Geopotential Differences

Zhong

Chen

et al. 2023

Earth and Space Science

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Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)

Soltani,

Azari

2023

Irrigation and Drainage

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In this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB) was obtained by using the GRACE satellites. The whole study area was covered by 10 GRACE/GFO pixels, and the TWSA value was calculated for these 10 pixels. Examining the changing trend showed that the value of TWSA in the LUB has a decreasing trend and fluctuates from approximately −200 to +200 compared to the average value. The TWSA was modelled using the group method of data handling (GMDH) by considering six different parameters obtained from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). Finally, the best models with two to six input variables were selected. Among the models with different inputs, the GMDH Model 2, with three inputs, had the best performance compared to the other models. After choosing the best inputs in calculating the TWSA value by the GMDH, the TWSA value was also modelled by using the Adaptive Neuro‐Fuzzy Inference System (ANFIS), the Extreme Learning Machine (ELM) and the Artificial Neural Network (ANN). The results showed that GMDH not only outperformed other models but also provided some simple equations to apply in practical tasks.

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High‐Resolution GRACE Monthly Spherical Harmonic Solutions

Cited by 25 publications

References 75 publications

One-degree resolution mascon solution over Antarctic derived from GRACE Level-2 data

One-degree resolution mascon solution over Antarctic derived from GRACE Level-2 data

WHU‐GRACE‐GPD01s: A Series of Constrained Monthly Gravity Field Solutions Derived From GRACE‐Based Geopotential Differences

Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)

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