Abstract: Accurate reconstruction of three-dimensional ocean current fields is critical for understanding ocean dynamics and real-time conduction of modern oceanographic field campaigns, particularly in mesoscale eddy environments. We propose a physics-informed modeling and learning framework for multi-source data fusion to estimate the three-dimensional (3D) current structure of oceanic eddies by integrating satellite altimetry and temperature data, ocean reanalysis data, and in situ drifting buoy observations. The approach leverages geostrophic balance, derived from the Navier-Stokes equations, to guide a neural network trained on GLORYS reanalysis data in inferring subsurface currents from surface conditions. The surface conditions were estimated using a high-dimensional linear mixed model, which integrates systematically biased satellite altimetry and sparse drifting buoy data, allowing for spatially adaptive bias correction and yielding more accurate and spatially coherent surface velocity fields. This framework was deployed in a September 2024 field campaign targeting a cyclonic eddy in the Kuroshio Extension, guiding the real-time control of seven underwater gliders. Compared with existing data products, our method demonstrated substantially improved accuracy in cross-validation with drifting buoys and stronger consistency with ADCP observations. The resulting glider trajectories provided enhanced spatial coverage of the eddy interior, enabling the first successful high-resolution controlled network survey of a mesoscale eddy.

Bio：邱宇谋，博士毕业于爱荷华州立大学，后在爱荷华州立大学统计系任教。于2023年加入北京大学数学科学学院、统计科学中心。他的研究包括：高维数据分析、高维协方差矩阵和精度矩阵的统计推断、因果分析、缺失数据分析。同时，他也致力于统计方法在海洋科学、精准农业、流行病模型、法医学等领域的应用研究。