Rui Zhang, Jinpeng Qi, Qiushuang Yan, Chenqing Fan, Yuchao Yang, Jie Zhang, Yong Wan
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Calibration of CFOSAT Off-Nadir SWIM SWH Product Based on CNN-LSTM Model
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.
期刊介绍:
Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.