Mon Oct 13, F. Flechtner
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Gou, Junyang, Börger, Lara, Schindelegger, Michael, and Soja, Benedikt, 2025. Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion: Downscaling GRACE-derived ocean bottom pressure anomalies.... Journal of Geodesy, 99(2):19, doi:10.1007/s00190-025-01943-9.
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@ARTICLE{2025JGeod..99...19G,
author = {{Gou}, Junyang and {B{\"o}rger}, Lara and {Schindelegger}, Michael and {Soja}, Benedikt},
title = "{Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion: Downscaling GRACE-derived ocean bottom pressure anomalies...}",
journal = {Journal of Geodesy},
keywords = {Downscaling, Ocean bottom pressure, GRACE(-FO), Ocean dynamics, Deep learning, Engineering, Geomatic Engineering, Earth Sciences, Oceanography, Physics - Geophysics},
year = 2025,
month = feb,
volume = {99},
number = {2},
eid = {19},
pages = {19},
abstract = "{The gravimetry measurements from the Gravity Recovery and Climate
Experiment (GRACE) and its follow-on (GRACE-FO) mission provide
an essential way to monitor changes in ocean bottom pressure
(<inline-formula id=``IEq1''><mml:math id=``IEq1\_Math''><mml:ms
ub><mml:mi>p</mml:mi><mml:mi>b</mml:mi></mml:msub></mml:math></i
nline-formula>), which is a critical variable in understanding
ocean circulation. However, the coarse spatial resolution of the
GRACE(-FO) fields blurs important spatial details, such as
<inline-formula id=``IEq2''><mml:math id=``IEq2\_Math''><mml:msu
b><mml:mi>p</mml:mi><mml:mi>b</mml:mi></mml:msub></mml:math></in
line-formula> gradients. In this study, we employ a self-
supervised deep learning algorithm to downscale global monthly
<inline-formula id=``IEq3''><mml:math id=``IEq3\_Math''><mml:msu
b><mml:mi>p</mml:mi><mml:mi>b</mml:mi></mml:msub></mml:math></in
line-formula> anomalies derived from GRACE(-FO) observations to
an equal-angle 0.25 <inline-formula id=``IEq4''><mml:math id=``I
Eq4\_Math''><mml:mmultiscripts><mml:mrow></mml:mrow><mml:mrow></
mml:mrow><mml:mo>{\ensuremath{\circ}}</mml:mo></mml:mmultiscript
s></mml:math></inline-formula> grid in the absence of high-
resolution ground truth. The optimization process is realized by
constraining the outputs to follow the large-scale mass
conservation contained in the gravity field estimates while
learning the spatial details from two ocean reanalysis products.
The downscaled product agrees with GRACE(-FO) solutions over
large ocean basins at the millimeter level in terms of
equivalent water height and shows signs of outperforming them
when evaluating short spatial scale variability. In particular,
the downscaled <inline-formula id=``IEq5''><mml:math id=``IEq5\_
Math''><mml:msub><mml:mi>p</mml:mi><mml:mi>b</mml:mi></mml:msub>
</mml:math></inline-formula> product has more realistic signal
content near the coast and exhibits better agreement with tide
gauge measurements at around 80\% of 465 globally distributed
stations. Our method presents a novel way of combining the
advantages of satellite measurements and ocean models at the
product level, with potential downstream applications for
studies of the large-scale ocean circulation, coastal sea level
variability, and changes in global geodetic parameters.}",
doi = {10.1007/s00190-025-01943-9},
archivePrefix = {arXiv},
eprint = {2404.05818},
primaryClass = {physics.geo-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025JGeod..99...19G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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GRACE-FO
Mon Oct 13, F. Flechtner