Observation‐Driven Correction of Numerical Weather Prediction for Marine Winds

Abstract Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable. We present an observation‐informed correction approach for global numerical weather prediction (NWP) of marine winds. Rather than forecasting winds directly, we learn local correction patterns by assimilating the latest in situ observations to adjust the Global Forecast System (GFS) output. We propose Observation‐informed Real‐time Correction with Attention (ORCA), a transformer‐based deep learning architecture that (a) handles irregular and time‐varying observation sets through masking and set‐based attention mechanisms, (b) conditions predictions on recent observation–forecast pairs via cross‐attention, and (c) employs cyclical time embeddings and coordinate‐aware location representations to enable single‐pass inference at arbitrary spatial coordinates. We evaluate ORCA over the Atlantic Ocean using observations from the International Comprehensive Ocean‐Atmosphere Data Set as reference. ORCA reduces GFS 10‐m wind error at all lead times