Reconstructing Significant Wave Height Fields from Sparse Observations Using Virtual Observation Augmentation

Shuang Xu , Hailong Liu, Qi Shu

Real-time, high-precision significant wave height (SWH) fields are essential for ocean forecasting, disaster mitigation, and marine applications. However, extremely sparse observational coverage in vast ocean regions limits accurate reconstruction, as traditional spatial interpolation often produces overly smoothed fields that fail to capture fine-scale structures and sharp gradients. This study proposes the Time-to-Space Wave (T2S-W) Framework, a hybrid approach based on “filling space with time.” It advects historical time-series observations forward to the target time using group velocities to generate virtual observations that augment spatial coverage. A lightweight LightGBM model approximates the net effect of nonlinear source and sink processes, applying corrections for dissipation and energy growth along ray paths without real-time wind fields. A convolutional neural network (CNN)–Transformer architecture then performs high-precision continuous reconstruction. Results show strong accuracy and robustness under ~99.8% missing rates, achieving 0.20 m RMSE on test sets while preserving fine-scale structures across monsoon and typhoon conditions. Diagnostics reveal the framework estimates wind-sea/swell decoupling, shallow-water topographic dissipation, and propagation-direction-dependent spatial response patterns. This provides a novel pathway for high-fidelity reconstruction under sparse data and indicates that the potential extends to other advection-diffusion ocean fields like temperature, salinity, and tracers.