High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Metehan Uz; Orhan Akyılmaz; C. K. Shum; Kazım Gökhan Atman; Sevda Olgun; Özge Güneş

doi:10.1038/s41597-023-02887-5

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Metehan Uz, Orhan Akyılmaz^*, C. K. Shum, Kazım Gökhan Atman, Sevda Olgun, Özge Güneş

^*Corresponding author for this work

Department of Geomatics Engineering

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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.

Original language	English
Article number	71
Journal	Scientific data
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41597-023-02887-5
Publication status	Published - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Funding

This work is partially supported by Scientific and Technological Research Council of Turkey - TÜBİTAK (Contract number 119Y176), by the United States Agency for International Development (USAID)/Indian Partnerships Program (Cooperative Agreement: 72038621CA00002), by National Geospatial-Intelligence Agency (NGA) GEO-ESCON Program (No. HM157522D0009, Task 8.8), by NSF Partnerships for Innovation Program (2044704) and by NASA Earth Surface Interior Focused Area Program (80NSSC20K0494). Dr. Anno Löcher from University of Bonn is gratefully acknowledged for providing monthly gravity field solutions from SLR data. Special thanks to the editors and the two anonymous reviewers who significantly improved the quality of the manuscript with constructive comments. This work is partially supported by Scientific and Technological Research Council of Turkey - TÜBİTAK (Contract number 119Y176), by the United States Agency for International Development (USAID)/Indian Partnerships Program (Cooperative Agreement: 72038621CA00002), by National Geospatial-Intelligence Agency (NGA) GEO-ESCON Program (No. HM157522D0009, Task 8.8), by NSF Partnerships for Innovation Program (2044704) and by NASA Earth Surface Interior Focused Area Program (80NSSC20K0494). Dr. Anno Löcher from University of Bonn is gratefully acknowledged for providing monthly gravity field solutions from SLR data. Special thanks to the editors and the two anonymous reviewers who significantly improved the quality of the manuscript with constructive comments.

Funders	Funder number
NASA Earth Surface Interior Focused Area Program	80NSSC20K0494
National Science Foundation	2044704
United States Agency for International Development	72038621CA00002
National Geospatial-Intelligence Agency	HM157522D0009
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	119Y176
Rheinische Friedrich-Wilhelms-Universität Bonn

Access to Document

10.1038/s41597-023-02887-5

Cite this

@article{378326847b344bbdb63c72e29e991d7c,

title = "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-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.",

author = "Metehan Uz and Orhan Akyılmaz and Shum, {C. K.} and Atman, {Kazım G{\"o}khan} and Sevda Olgun and {\"O}zge G{\"u}ne{\c s}",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s).",

year = "2024",

month = dec,

doi = "10.1038/s41597-023-02887-5",

language = "English",

volume = "11",

journal = "Scientific data",

issn = "2052-4463",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - High-resolution temporal gravity field data products

T2 - Monthly mass grids and spherical harmonics from 1994 to 2021

AU - Uz, Metehan

AU - Akyılmaz, Orhan

AU - Shum, C. K.

AU - Atman, Kazım Gökhan

AU - Olgun, Sevda

AU - Güneş, Özge

PY - 2024/12

Y1 - 2024/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85182243691&partnerID=8YFLogxK

U2 - 10.1038/s41597-023-02887-5

DO - 10.1038/s41597-023-02887-5

M3 - Article

C2 - 38218975

AN - SCOPUS:85182243691

SN - 2052-4463

VL - 11

JO - Scientific data

JF - Scientific data

IS - 1

M1 - 71

ER -

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021

Abstract

Bibliographical note

Funding

Access to Document

Other files and links

Fingerprint

Cite this