2022
DOI: 10.3390/rs14133114
|View full text |Cite
|
Sign up to set email alerts
|

Adaptive DDK Filter for GRACE Time-Variable Gravity Field with a Novel Anisotropic Filtering Strength Metric

Abstract: Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time-variable released by several data centers. The signal covariance matrix (SCM) involved is monthly time-variable also. Specifically, it is parameterized into two parameters, namely the regularization coefficient … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 29 publications
0
1
0
Order By: Relevance
“…The widespread empirical spectral filtering methods, such as Gaussian-type filter [15], [16], [17], [18] Decorrelation-type filter [19], [20], [21], Decomposition-type filter [22], [23], [13] and Root Mean Square (RMS) filter [24], are susceptible to under-filtering or over-filtering due to neglecting the precision information of GRACE gravity field models [25], [26]. In contrast, more rigorous statistical filtering methods, which incorporate signal and error covariance matrices of GRACE SHCs, such as Wiener filter [27], DDK filter [28], [29] and ANS filter [30], demonstrate significantly enhanced filtering effects. Apart from global spectral filtering, the regional pointmass modeling technique is developed to enhance the spatial resolution of regional mass changes [31], [32], [33], [34], utilizing the pseudo measurements (e.g., gravity disturbances, geoid heights, and gravity anomalies) at satellite altitudes computed from GRACE SHCs to estimate the spatial pointmass variations on the Earth's surface within a regularization framework.…”
Section: Introductionmentioning
confidence: 99%
“…The widespread empirical spectral filtering methods, such as Gaussian-type filter [15], [16], [17], [18] Decorrelation-type filter [19], [20], [21], Decomposition-type filter [22], [23], [13] and Root Mean Square (RMS) filter [24], are susceptible to under-filtering or over-filtering due to neglecting the precision information of GRACE gravity field models [25], [26]. In contrast, more rigorous statistical filtering methods, which incorporate signal and error covariance matrices of GRACE SHCs, such as Wiener filter [27], DDK filter [28], [29] and ANS filter [30], demonstrate significantly enhanced filtering effects. Apart from global spectral filtering, the regional pointmass modeling technique is developed to enhance the spatial resolution of regional mass changes [31], [32], [33], [34], utilizing the pseudo measurements (e.g., gravity disturbances, geoid heights, and gravity anomalies) at satellite altitudes computed from GRACE SHCs to estimate the spatial pointmass variations on the Earth's surface within a regularization framework.…”
Section: Introductionmentioning
confidence: 99%