Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis
Dozens of missing epochs in the monthly gravity product of the satellite mission Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission greatly inhibit the complete analysis and full utilization of the data. Despite previous attempts to handle this problem, a general al...
Main Authors: | , |
---|---|
Format: | Dataset |
Language: | unknown |
Published: |
DaRUS
2021
|
Subjects: | |
Online Access: | https://dx.doi.org/10.18419/darus-807 https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-807 |
id |
ftdatacite:10.18419/darus-807 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
description |
Dozens of missing epochs in the monthly gravity product of the satellite mission Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission greatly inhibit the complete analysis and full utilization of the data. Despite previous attempts to handle this problem, a general all-purpose gap-filling solution is still lacking. Here we propose a non-parametric, data-adaptive and easy-to-implement approach - composed of the Singular Spectrum Analysis (SSA) gap-filling technique, cross-validation, and spectral testing for significant components - to produce reasonable gap-filling results in the form of spherical harmonic coefficients (SHCs). We demonstrate that this approach is adept at inferring missing data from long-term and oscillatory changes extracted from available observations. A comparison in the spectral domain reveals that the gap-filling result resembles the product of GRACE missions below spherical harmonic degree 30 very well. As the degree increases above 30, the amplitude per degree of the gap-filling result decreases more rapidly than that of GRACE/GRACE-FO SHCs, showing effective suppression of noise. As a result, our approach can reduce noise in the oceans without sacrificing resolutions on land. The gap filling dataset is stored in the “SSA_filing/" folder. Each file represents a monthly result in the form of spherical harmonics. The data format follows the convention of the site ftp://isdcftp.gfz-potsdam.de/grace/. Low degree corrections (degree-1, C20, C30) have been made. The code to generate the dataset is located in the “code_share/“ folder, with an example for C30. The model-based Greenland mass balance result for data validation (results given in the paper) is provided in the "Greenland_SMB-D.txt” file. |
format |
Dataset |
author |
Yi, Shuang Sneeuw, Nico |
spellingShingle |
Yi, Shuang Sneeuw, Nico Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis |
author_facet |
Yi, Shuang Sneeuw, Nico |
author_sort |
Yi, Shuang |
title |
Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis |
title_short |
Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis |
title_full |
Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis |
title_fullStr |
Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis |
title_full_unstemmed |
Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis |
title_sort |
data for: filling the data gaps within grace missions using singular spectrum analysis |
publisher |
DaRUS |
publishDate |
2021 |
url |
https://dx.doi.org/10.18419/darus-807 https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-807 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_relation |
https://dx.doi.org/10.18419/darus-807/1 https://dx.doi.org/10.18419/darus-807/2 https://dx.doi.org/10.18419/darus-807/3 https://dx.doi.org/10.18419/darus-807/4 https://dx.doi.org/10.18419/darus-807/5 https://dx.doi.org/10.18419/darus-807/6 https://dx.doi.org/10.18419/darus-807/7 https://dx.doi.org/10.18419/darus-807/8 https://dx.doi.org/10.18419/darus-807/9 https://dx.doi.org/10.18419/darus-807/10 https://dx.doi.org/10.18419/darus-807/11 https://dx.doi.org/10.18419/darus-807/12 https://dx.doi.org/10.18419/darus-807/13 https://dx.doi.org/10.18419/darus-807/14 https://dx.doi.org/10.18419/darus-807/15 https://dx.doi.org/10.18419/darus-807/16 https://dx.doi.org/10.18419/darus-807/17 https://dx.doi.org/10.18419/darus-807/18 https://dx.doi.org/10.18419/darus-807/19 https://dx.doi.org/10.18419/darus-807/20 https://dx.doi.org/10.18419/darus-807/21 https://dx.doi.org/10.18419/darus-807/22 https://dx.doi.org/10.18419/darus-807/23 https://dx.doi.org/10.18419/darus-807/24 https://dx.doi.org/10.18419/darus-807/25 https://dx.doi.org/10.18419/darus-807/26 https://dx.doi.org/10.18419/darus-807/27 https://dx.doi.org/10.18419/darus-807/28 https://dx.doi.org/10.18419/darus-807/29 https://dx.doi.org/10.18419/darus-807/30 https://dx.doi.org/10.18419/darus-807/31 https://dx.doi.org/10.18419/darus-807/32 https://dx.doi.org/10.18419/darus-807/33 https://dx.doi.org/10.18419/darus-807/34 https://dx.doi.org/10.18419/darus-807/35 https://dx.doi.org/10.18419/darus-807/36 https://dx.doi.org/10.18419/darus-807/37 https://dx.doi.org/10.18419/darus-807/38 https://dx.doi.org/10.18419/darus-807/39 https://dx.doi.org/10.18419/darus-807/40 https://dx.doi.org/10.18419/darus-807/41 https://dx.doi.org/10.18419/darus-807/42 https://dx.doi.org/10.18419/darus-807/43 |
op_doi |
https://doi.org/10.18419/darus-807 https://doi.org/10.18419/darus-807/1 https://doi.org/10.18419/darus-807/2 https://doi.org/10.18419/darus-807/3 https://doi.org/10.18419/darus-807/4 https://doi.org/10.18419/darus-807/5 https://doi.org/10.1841 |
_version_ |
1766019725515554816 |
spelling |
ftdatacite:10.18419/darus-807 2023-05-15T16:30:01+02:00 Data for: Filling the data gaps within GRACE missions using Singular Spectrum Analysis Yi, Shuang Sneeuw, Nico 2021 https://dx.doi.org/10.18419/darus-807 https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-807 unknown DaRUS https://dx.doi.org/10.18419/darus-807/1 https://dx.doi.org/10.18419/darus-807/2 https://dx.doi.org/10.18419/darus-807/3 https://dx.doi.org/10.18419/darus-807/4 https://dx.doi.org/10.18419/darus-807/5 https://dx.doi.org/10.18419/darus-807/6 https://dx.doi.org/10.18419/darus-807/7 https://dx.doi.org/10.18419/darus-807/8 https://dx.doi.org/10.18419/darus-807/9 https://dx.doi.org/10.18419/darus-807/10 https://dx.doi.org/10.18419/darus-807/11 https://dx.doi.org/10.18419/darus-807/12 https://dx.doi.org/10.18419/darus-807/13 https://dx.doi.org/10.18419/darus-807/14 https://dx.doi.org/10.18419/darus-807/15 https://dx.doi.org/10.18419/darus-807/16 https://dx.doi.org/10.18419/darus-807/17 https://dx.doi.org/10.18419/darus-807/18 https://dx.doi.org/10.18419/darus-807/19 https://dx.doi.org/10.18419/darus-807/20 https://dx.doi.org/10.18419/darus-807/21 https://dx.doi.org/10.18419/darus-807/22 https://dx.doi.org/10.18419/darus-807/23 https://dx.doi.org/10.18419/darus-807/24 https://dx.doi.org/10.18419/darus-807/25 https://dx.doi.org/10.18419/darus-807/26 https://dx.doi.org/10.18419/darus-807/27 https://dx.doi.org/10.18419/darus-807/28 https://dx.doi.org/10.18419/darus-807/29 https://dx.doi.org/10.18419/darus-807/30 https://dx.doi.org/10.18419/darus-807/31 https://dx.doi.org/10.18419/darus-807/32 https://dx.doi.org/10.18419/darus-807/33 https://dx.doi.org/10.18419/darus-807/34 https://dx.doi.org/10.18419/darus-807/35 https://dx.doi.org/10.18419/darus-807/36 https://dx.doi.org/10.18419/darus-807/37 https://dx.doi.org/10.18419/darus-807/38 https://dx.doi.org/10.18419/darus-807/39 https://dx.doi.org/10.18419/darus-807/40 https://dx.doi.org/10.18419/darus-807/41 https://dx.doi.org/10.18419/darus-807/42 https://dx.doi.org/10.18419/darus-807/43 dataset Dataset 2021 ftdatacite https://doi.org/10.18419/darus-807 https://doi.org/10.18419/darus-807/1 https://doi.org/10.18419/darus-807/2 https://doi.org/10.18419/darus-807/3 https://doi.org/10.18419/darus-807/4 https://doi.org/10.18419/darus-807/5 https://doi.org/10.1841 2021-11-05T12:55:41Z Dozens of missing epochs in the monthly gravity product of the satellite mission Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission greatly inhibit the complete analysis and full utilization of the data. Despite previous attempts to handle this problem, a general all-purpose gap-filling solution is still lacking. Here we propose a non-parametric, data-adaptive and easy-to-implement approach - composed of the Singular Spectrum Analysis (SSA) gap-filling technique, cross-validation, and spectral testing for significant components - to produce reasonable gap-filling results in the form of spherical harmonic coefficients (SHCs). We demonstrate that this approach is adept at inferring missing data from long-term and oscillatory changes extracted from available observations. A comparison in the spectral domain reveals that the gap-filling result resembles the product of GRACE missions below spherical harmonic degree 30 very well. As the degree increases above 30, the amplitude per degree of the gap-filling result decreases more rapidly than that of GRACE/GRACE-FO SHCs, showing effective suppression of noise. As a result, our approach can reduce noise in the oceans without sacrificing resolutions on land. The gap filling dataset is stored in the “SSA_filing/" folder. Each file represents a monthly result in the form of spherical harmonics. The data format follows the convention of the site ftp://isdcftp.gfz-potsdam.de/grace/. Low degree corrections (degree-1, C20, C30) have been made. The code to generate the dataset is located in the “code_share/“ folder, with an example for C30. The model-based Greenland mass balance result for data validation (results given in the paper) is provided in the "Greenland_SMB-D.txt” file. Dataset Greenland DataCite Metadata Store (German National Library of Science and Technology) Greenland |