Reconstructed SWHs Based on a Deep Learning Method and the Revealed Long-Term SWH Variance Characteristics During 1993–2024

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Abstract
Long-term high-resolution spatial gridded altimeter-derived significant wave height (SWH) data with daily temporal resolution are fundamental to revealing the detailed processes through which weather systems influence the ocean. However, elucidation of those processes is hampered by the sparse coverage and narrow width of the swath of altimeter-derived SWHs. The core problem is how best to extract the spatial structure and then fill missing values around the daily swaths. Although recent developments in deep learning methods have improved the extraction of spatial features, progress regarding the reconstruction of gridded altimeter daily SWHs remains limited. This study reconstructed daily 0.25° × 0.25° gridded SWHs from 1993 to 2024 using a partial convolutional U-Net model with attention and residual blocks. Comparison between the daily reconstructed SWHs and ERA5 SWHs revealed that the reconstructed SWHs improved the accuracy for SWHs of >2.5 m, which are usually underestimated in the ERA5 data. The greatest differences were in China’s offshore waters, especially in the region of the Taiwan Strait and in waters influenced by the Huanghai Warm Current. This study highlights the importance of altimeter swath-derived SWHs in reconstructed gridded SWH datasets, particularly in complex straits and under high sea state conditions.
Funding Information
  • National Natural Science Foundation of China (42275164)
  • Civil Aviation Safety Operation Support Capability Construction Project (GrantsKG2025005)
  • Key Laboratory of Space Ocean Remote Sensing and Application (2023CFO018)
  • MNR and National Natural Science Foundation of China (41005057)
  • High Performance Computing Center of Nanjing University of Information Science & Technology

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