Statistics

Bathymetry

Engineering

Internal wave

Correlation coefficient

Remote sensing

Physics

Sea surface temperature

Moderate-resolution imaging spectroradiometer

Geology

Aerospace engineering

Meteorology

Wind wave

Mathematics

Climatology

Significant wave height

Geodesy

Mean squared error

Satellite

Sea-surface height

Oceanography

IEEE Transactions on Geoscience and Remote Sensing

Internal waves (IWs), observed in the world oceans, have significant impacts on ocean engineering and environments. In this study, we collected satellite images from Moderate-Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite sensors in the Sulu-Celebes Sea from 2016 to 2019 to understand the IW generation and propagation. Satellite observations show a coherent IW phase difference in both seas, indicating that the IWs are alternatively generated when the tidal currents oscillate back and forth in the Sulu Archipelago, which separates two seas. A new generation site is found for occasionally observed long IWs in the eastern Sulu Sea. To understand the IW propagation characteristics, we developed a machine-learning-based forecast model. We trained the model with both IW wave crest curves extracted from satellite images and published climatological ocean temperature–salinity profiles. Since many satellite images contain IW packets generated at two or three tidal cycles, we can validate the model performance by matching the model prediction after one or two tidal cycles with the second or third wave crests in satellite images. Three factors are adopted to evaluate the forecast results: the root-mean-square error (RMSE), the Fréchet distance (FD), and the correlation coefficient (CC). The forecast model has an average error with an RMSE of 12.92 km, an FD of 18.73 km, and a CC of 0.98. Analysis shows that a smaller time step is preferred in regions where the water depth changes significantly. Comparison with the Korteweg–de Vries equation solutions shows that the developed forecast model is more robust when errors introduced to the model inputs.

Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas