Abstract Accurate ocean forecasting is crucial in different areas ranging from science to decision making. Recent advancements in data‐driven models have shown significant promise, particularly in weather forecasting community, but yet no data‐driven approaches have matched the accuracy and the scalability of traditional global ocean forecasting systems that rely on physics‐driven numerical models and can be very computationally expensive, depending on their spatial resolution or complexity. Here, we introduce GLONET, a global ocean neural network‐based forecasting system, developed by Mercator Ocean International. GLONET is trained on the global Mercator Ocean physical reanalysis GLORYS12 to integrate physics‐based principles through neural operators and networks, which dynamically capture local‐global interactions within a unified scalable framework, ensuring high small‐scale accuracy and efficient dynamics. GLONET's performance is assessed and benchmarked against two other forecasting systems: the global Mercator Ocean analysis and forecasting 1/12° high‐resolution physical system GLO12 and a recent neural‐based system also trained from GLORYS12. A series of comprehensive validation metrics is proposed, specifically tailored for neural network‐based ocean forecasting systems, which extend beyond traditional point‐wise error assessments that can introduce bias towards neural networks optimized primarily to minimize such metrics. The preliminary evaluation of GLONET shows promising results, for temperature, sea surface height, salinity, and ocean currents. GLONET's experimental daily forecast are accessible through the European Digital Twin Ocean platform EDITO.
Aouni et al. (Mon,) studied this question.