Physically Guided GAN‐Based Super Resolution of Buoy Observations for Internal Solitary Waves

Abstract Accurate monitoring of internal solitary waves (ISWs) is crucial for understanding their influence on ocean mixing and energy transfer. Satellite observations offer a powerful tool for large‐scale ISW monitoring, but they mainly detect surface signatures and thus require nearby in situ measurements for validation and amplitude estimation. However, buoy data with sparse sampling intervals cannot resolve the narrow waveforms of ISWs, resulting in systematic underestimation of amplitude parameters and reducing their usefulness for remote sensing‐based inversion models. To overcome this issue, we introduce IWSR, a physics‐guided generative adversarial network designed to reconstruct 1‐min ISW signals from 10‐min buoy data. The model uses a thermocline‐weighted loss function to focus on ISW‐relevant features and is trained with high‐frequency buoy records from the Andaman Sea. Validation results show that IWSR effectively restores detailed soliton structures that are missed in coarse‐resolution data, achieving a peak signal‐to‐noise ratio of 38.83 dB, an RMSE of 0.22°C, and a thermocline‐specific masked RMSE of 1.59°C. When applied to buoy data from 2012 to 2014, IWSR increases estimated ISW amplitudes by an average of 5.60%, reaching up to 18.96%, and uncovers more high‐amplitude events. The statistics of the reconstructed data also reflect seasonal changes linked to thermocline depth fluctuations. By reducing the resolution‐duration trade‐off, IWSR improves the accuracy of buoy‐based ISW observations and offers better input for remote sensing‐based ISW amplitude inversion models, enabling a more precise analysis of subsurface ocean dynamics.