Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021

Starting from April 2002, Gravity Recovery and Climate Experiment (GRACE) and, its successor mission GRACE-FO (FollowOn) have provided irreplaceable data for monitoring mass variations within the hydrosphere, cryosphere, and oceans, and with unprecedented accuracy and resolution for over two decades...

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Main Authors: Atman, Kazım Gökhan, Olgun, Sevda, Güneş, Özge, Uz, Metehan, Akyılmaz, Orhan, Shum, C.K.
Format: Conference Object
Language:English
Published: 2023
Subjects:
Online Access:https://avesis.yildiz.edu.tr/publication/details/f558041f-991a-4c31-853f-4e9e3f3ac534/oai
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spelling ftyildiztuniv:f558041f-991a-4c31-853f-4e9e3f3ac534 2023-09-26T15:18:09+02:00 Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021 Atman, Kazım Gökhan Olgun, Sevda Güneş, Özge Uz, Metehan Akyılmaz, Orhan Shum, C.K. 2023-07-15T00:00:00Z https://avesis.yildiz.edu.tr/publication/details/f558041f-991a-4c31-853f-4e9e3f3ac534/oai eng eng f558041f-991a-4c31-853f-4e9e3f3ac534 https://avesis.yildiz.edu.tr/publication/details/f558041f-991a-4c31-853f-4e9e3f3ac534/oai info:eu-repo/semantics/closedAccess info:eu-repo/semantics/conferenceObject 2023 ftyildiztuniv 2023-08-27T21:20:01Z Starting from April 2002, Gravity Recovery and Climate Experiment (GRACE) and, its successor mission GRACE-FO (FollowOn) have provided irreplaceable data for monitoring mass variations within the hydrosphere, cryosphere, and oceans, and with unprecedented accuracy and resolution for over two decades. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatiotemporal changes in climate-induced geophysical phenomena, including terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure. In this study, total water storage anomalies (TWSA) are simulated/reconstructed globally at 1.0°x1.0° spatial and monthly temporal resolutions from January 1994 to December 2020 with an in-house developed hybrid Deep Learning (DL) architecture using GRACE/-FO mascon and SLR gravimetry, ECMWF Reanalysis-5 (ERA5) data. We validated our simulated mass change data products both over land and ocean, not only through mathematical performance metrics (internal validations) such as RMSE or NSE along with comparisons to previous studies, but also external validations with non-GRACE datasets such as El-Nino and La-Nina patterns, barystatic global mean sea level change, degree (d) 2 order (o) 1 spherical harmonic coefficients (C21, S21) retrieved from Earth orientation parameters, Greenland Ice sheet mass balance and in-situ Ocean Bottom Pressure measurements were carried out. The overall validations show that the proposed DL paradigm can efficiently simulate high-resolution monthly global gravity field both in GRACE/GRACE-FO and pre-GRACE era. The resulting simulated data products are available as monthly mass change grids as well as spherical harmonic models up to d/o 180. Conference Object glacier Greenland Ice Sheet Yıldız Technical University Research Information System Greenland
institution Open Polar
collection Yıldız Technical University Research Information System
op_collection_id ftyildiztuniv
language English
description Starting from April 2002, Gravity Recovery and Climate Experiment (GRACE) and, its successor mission GRACE-FO (FollowOn) have provided irreplaceable data for monitoring mass variations within the hydrosphere, cryosphere, and oceans, and with unprecedented accuracy and resolution for over two decades. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatiotemporal changes in climate-induced geophysical phenomena, including terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure. In this study, total water storage anomalies (TWSA) are simulated/reconstructed globally at 1.0°x1.0° spatial and monthly temporal resolutions from January 1994 to December 2020 with an in-house developed hybrid Deep Learning (DL) architecture using GRACE/-FO mascon and SLR gravimetry, ECMWF Reanalysis-5 (ERA5) data. We validated our simulated mass change data products both over land and ocean, not only through mathematical performance metrics (internal validations) such as RMSE or NSE along with comparisons to previous studies, but also external validations with non-GRACE datasets such as El-Nino and La-Nina patterns, barystatic global mean sea level change, degree (d) 2 order (o) 1 spherical harmonic coefficients (C21, S21) retrieved from Earth orientation parameters, Greenland Ice sheet mass balance and in-situ Ocean Bottom Pressure measurements were carried out. The overall validations show that the proposed DL paradigm can efficiently simulate high-resolution monthly global gravity field both in GRACE/GRACE-FO and pre-GRACE era. The resulting simulated data products are available as monthly mass change grids as well as spherical harmonic models up to d/o 180.
format Conference Object
author Atman, Kazım Gökhan
Olgun, Sevda
Güneş, Özge
Uz, Metehan
Akyılmaz, Orhan
Shum, C.K.
spellingShingle Atman, Kazım Gökhan
Olgun, Sevda
Güneş, Özge
Uz, Metehan
Akyılmaz, Orhan
Shum, C.K.
Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021
author_facet Atman, Kazım Gökhan
Olgun, Sevda
Güneş, Özge
Uz, Metehan
Akyılmaz, Orhan
Shum, C.K.
author_sort Atman, Kazım Gökhan
title Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021
title_short Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021
title_full Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021
title_fullStr Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021
title_full_unstemmed Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021
title_sort deep learning-aided high-resolution temporal gravity field simulations: monthly global mass grids and spherical harmonics from 1994 to 2021
publishDate 2023
url https://avesis.yildiz.edu.tr/publication/details/f558041f-991a-4c31-853f-4e9e3f3ac534/oai
geographic Greenland
geographic_facet Greenland
genre glacier
Greenland
Ice Sheet
genre_facet glacier
Greenland
Ice Sheet
op_relation f558041f-991a-4c31-853f-4e9e3f3ac534
https://avesis.yildiz.edu.tr/publication/details/f558041f-991a-4c31-853f-4e9e3f3ac534/oai
op_rights info:eu-repo/semantics/closedAccess
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