Acquisition of the Significant Wave Height From CFOSAT SWIM Spectra Through a Deep Neural Network and Its Impact on Wave Model Assimilation
Wang, J. K. ; Aouf, Lotfi ; Dalphinet, Alice ; Li, B.x. ; Xu, Y. ; Liu, J. Q.
The wave numerical simulation accuracy can be improved by assimilating remotely sensed wave observations. In addition to the nadir, significant wave height (SWH), the Surface Waves Investigation and Monitoring (SWIM) onboard Chinese-French Oceanic SATellite (CFOSAT) provides two additional columns of wave spectra observations within wavelengths from 70 to 500 m. A model based on a deep neural network (DNN) is developed to retrieve the total SWH from the partially wave spectra observed by SWIM. The DNN model uses the parameters from both the SWIM spectra and the nearest nadir as the inputs, and the DNN is trained on the SWH from cross-matched altimeter observations. The DNN-based acquisition of the SWH is verified to achieve a high accuracy. A set of assimilation experiments are performed based on MFWAM and show promising results. Compared to the assimilation of SWIM nadir SWHs only, the addition of the newly obtained SWIM SWH notably enhances the positive impacts of assimilation, not only proving the effectiveness and accuracy of the DNN model but also demonstrating the unique potential of SWIM in wave assimilation.</p>
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