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Introducing Regularization Constraints Into Combination of GRACE Monthly Terrestrial Water Storage Changes
by
Ran, Jiangjun
, Qian, Ke
, Ditmar, Pavel
, You, Wei
in
Constraints
/ Data processing
/ Estimates
/ GRACE (experiment)
/ Noise
/ Noise levels
/ Regularization
/ Spherical harmonics
/ Time series
/ Water storage
2026
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Introducing Regularization Constraints Into Combination of GRACE Monthly Terrestrial Water Storage Changes
by
Ran, Jiangjun
, Qian, Ke
, Ditmar, Pavel
, You, Wei
in
Constraints
/ Data processing
/ Estimates
/ GRACE (experiment)
/ Noise
/ Noise levels
/ Regularization
/ Spherical harmonics
/ Time series
/ Water storage
2026
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Do you wish to request the book?
Introducing Regularization Constraints Into Combination of GRACE Monthly Terrestrial Water Storage Changes
by
Ran, Jiangjun
, Qian, Ke
, Ditmar, Pavel
, You, Wei
in
Constraints
/ Data processing
/ Estimates
/ GRACE (experiment)
/ Noise
/ Noise levels
/ Regularization
/ Spherical harmonics
/ Time series
/ Water storage
2026
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Introducing Regularization Constraints Into Combination of GRACE Monthly Terrestrial Water Storage Changes
Journal Article
Introducing Regularization Constraints Into Combination of GRACE Monthly Terrestrial Water Storage Changes
2026
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Overview
Gravity Recovery and Climate Experiment (GRACE) observation data processed by various institutions yields somewhat different spherical harmonic solutions, which are further used to derive terrestrial water storage (TWS) changes. Combining TWS solutions from different institutions helps to refine the effective signal while removing noise. This study investigates regularization constraints in the context of TWS fusion to enhance the resulting estimates. The considered constraints are Tikhonov regularization of different orders, as well as minimization of month‐to‐month year‐to‐year double differences (MYDD), and triple differences (MYTD). Different accuracy and signal evaluation approaches are implemented for both individual and combined solutions. Compared to individual solutions and unregularized combinations, the regularized TWS combined solutions demonstrate lower noise levels. Among them, the second‐order Tikhonov regularization performs slightly better than other constraints, providing lower noise levels. This study offers a novel perspective for exploring GRACE‐based TWS combination methodologies.
Publisher
John Wiley & Sons, Inc
Subject
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