New publication by Gou et al. (2025)

Ocean bottom pressure (OBP) fluctuations indicate variations in the amount and spatio-temporal distribution of ocean mass, and are valuable to understanding ocean dynamics, including large-scale ocean circulation and mesoscale turbulence. Moreover, changes in OBP fields are also closely linked to essential geodetic parameters. Accurate OBP estimations with high spatio-temporal resolution are required for the success of the aforementioned applications. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission can provide OBP variation information, but only with a limited spatial resolution of around 3 degrees.

In this study, we applied a convolution-based neural network to fuse the GRACE(-FO) products and two eddy-permitting ocean reanalysis products with the help of additional features, resulting in 0.25-degrees OBP variation products. The downscaled product agrees with GRACE(-FO) solutions over large ocean basins and shows signs of outperforming them when evaluating short spatial scale variability. In particular, the downscaled OBP 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.

The downscaled products are available at external page https://doi.org/10.3929/ethz-b-000686843.

The full paper is available at external page https://doi.org/10.1007/s00190-025-01943-9 (open access).