Neural ocean forecasting from sparse satellite-derived observations: a case-study for SSH dynamics and altimetry data

TL;DR

This paper introduces a deep learning framework using U-Net and 4DVarNet architectures to forecast sea surface dynamics from sparse satellite altimetry data, outperforming traditional models over a 7-day horizon.

Contribution

It adapts state-of-the-art image segmentation and spatiotemporal interpolation models for ocean forecasting with sparse data, demonstrating improved accuracy and operational potential.

Findings

Neural forecasts outperform baseline models across all lead times.

Significant improvements in high variability regions.

Framework developed within OceanBench for reproducibility.

Abstract

We present an end-to-end deep learning framework for short-term forecasting of global sea surface dynamics based on sparse satellite altimetry data. Building on two state-of-the-art architectures: U-Net and 4DVarNet, originally developed for image segmentation and spatiotemporal interpolation respectively, we adapt the models to forecast the sea level anomaly and sea surface currents over a 7-day horizon using sequences of sparse nadir altimeters observations. The model is trained on data from the GLORYS12 operational ocean reanalysis, with synthetic nadir sampling patterns applied to simulate realistic observational coverage. The forecasting task is formulated as a sequence-to-sequence mapping, with the input comprising partial sea level anomaly (SLA) snapshots and the target being the corresponding future full-field SLA maps. We evaluate model performance using (i) normalized root mean squared error (nRMSE), (ii) averaged effective resolution, (iii) percentage of correctly predicted velocities magnitudes and angles, and benchmark results against the operational Mercator Ocean forecast product. Results show that end-to-end neural forecasts outperform the baseline across all lead times, with particularly notable improvements in high variability regions. Our framework is developed within the OceanBench benchmarking initiative, promoting reproducibility and standardized evaluation in ocean machine learning. These results demonstrate the feasibility and potential of end-to-end neural forecasting models for operational oceanography, even in data-sparse conditions.