A Novel Approach for Sea Surface Wind Field Reconstruction Using Sparse Scatterometer Data Based on Physics‐Informed Neutral Network

Abstract Satellite‐based research on global sea surface winds is crucial for understanding and monitoring dynamic air‐sea interface processes, driving the need for reliable assessments and forecasts. Achieving these objectives requires accurate reconstruction of wind speeds and wind directions that assimilate real‐time observations, but current methods used for such reconstructions remain computationally expensive and challenging to solve highly nonlinear equations. By selecting multiple regions in the Pacific Ocean as case studies, we explore the fusion of sparse scatterometer observations from different satellites with physics‐informed neural networks (PINNs) to reconstruct large‐scale sea surface wind fields, effectively integrating observations and filling data gaps. The result demonstrates that PINNs can efficiently reconstruct full realistic wind fields from sparse data at a low computation cost. Moreover, we propose a novel loss function that can preserve fine‐scale details while capturing the key features of the wind field. Evaluation against reference data sets, including buoy measurements, ERA5, and cross‐calibrated multiplatform (CCMP) wind speed and direction, highlights the accuracy and robustness of the proposed method. Compared to the reanalysis data of ERA5 and CCMP, the wind field reconstructed by the PINN closely aligns with the observation. These findings underscore the potential of the PINN technique as an alternative, computationally efficient approach for operational sea surface wind reconstruction.