Learning operational altimetry mapping from ocean models

In oceanography, altimetry products are used to measure the height of the ocean surface, and ocean modeling is used to understand and predict the behavior of the ocean. There are two main types of gridded altimetry products: operational sea level products, such as DUACS, which are used for forecasting and reconstruction, and ocean model reanalyses, such as Glorys 12, which are used to forecast seasonal trends and assess physical characteristics. However, advances in ocean modeling do not always directly benefit operational forecast or reconstruction products.

In this study, we investigate the potential for deep learning methods, which have been successfully applied in simulated setups, to leverage ocean modeling efforts for improving operational altimetry products. Specifically, we ask under what conditions the knowledge learned from ocean simulations can be applied to real-world operational altimetry mapping. We consider the impact of simulation grid resolution, observation data reanalysis, and physical processes modeled on the performance of a deep learning model.

Our results show that the deep learning model outperforms current operational methods on a regional domain around the Gulfstream, with a 50km improvement in resolved scale. This improvement has the potential to enhance the accuracy of operational altimetry products, which are used for a range of important applications, such as climate monitoring and understanding mesoscale ocean dynamics.