High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021
Abstract Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-ter...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02887-5 |
| _version_ | 1797356002101690368 |
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| author | Metehan Uz Orhan Akyılmaz C. K. Shum Kazım Gökhan Atman Sevda Olgun Özge Güneş |
| author_facet | Metehan Uz Orhan Akyılmaz C. K. Shum Kazım Gökhan Atman Sevda Olgun Özge Güneş |
| author_sort | Metehan Uz |
| collection | DOAJ |
| description | Abstract Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation. |
| first_indexed | 2024-03-08T14:19:24Z |
| format | Article |
| id | doaj.art-a5bd349aa2de4570b0f28504879a8870 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-08T14:19:24Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-a5bd349aa2de4570b0f28504879a88702024-01-14T12:11:25ZengNature PortfolioScientific Data2052-44632024-01-0111112010.1038/s41597-023-02887-5High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021Metehan Uz0Orhan Akyılmaz1C. K. Shum2Kazım Gökhan Atman3Sevda Olgun4Özge Güneş5Dept. of Geomatics Eng., Istanbul Technical UniversityDept. of Geomatics Eng., Istanbul Technical UniversityDivision of Geodetic Science, School of Earth Sciences, The Ohio State UniversitySchool of Mathematical Sciences, Queen Mary University of LondonDept. of Geomatics Eng., Kocaeli UniversityDept. of Geomatics Eng., Yıldız Technical UniversityAbstract Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation.https://doi.org/10.1038/s41597-023-02887-5 |
| spellingShingle | Metehan Uz Orhan Akyılmaz C. K. Shum Kazım Gökhan Atman Sevda Olgun Özge Güneş High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 Scientific Data |
| title | High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 |
| title_full | High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 |
| title_fullStr | High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 |
| title_full_unstemmed | High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 |
| title_short | High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 |
| title_sort | high resolution temporal gravity field data products monthly mass grids and spherical harmonics from 1994 to 2021 |
| url | https://doi.org/10.1038/s41597-023-02887-5 |
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