Calibration of CFOSAT Off‐Nadir SWIM SWH Product Based on CNN‐LSTM Model

Abstract

Abstract High‐precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off‐nadir SWH parameters, but there exists a certain bias with the in‐situ SWH values. To improve the accuracy of the SWH calculation bias from the off‐nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio‐temporal hybrid model that combines convolutional neural network (CNN) and long short‐term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off‐nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off‐nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m.

Keywords

• calibration of significant wave height
• spatio and temporal
• wind and wave relationship
• CNN‐LSTM model