Wind Direction Retrieval From CYGNSS L1 Level Sea Surface Data Based on Machine Learning

Abstract

Using Cyclone Global Navigation Satellite System (CYGNSS) L1 data with large amount and wide coverage, this paper establishes a sea surface wind direction retrieval model based on three machine learning algorithms. Wind direction will cause the asymmetry of Delay Doppler Map (DDM). Based on this, this paper extracts two angle characteristic parameters from DDM. Compared with CYGNSS full DDM, L1 compact DDM has a reduced dimension. Therefore, this paper expands more characteristic parameters, including L1 parameters and geophysical parameters such as wind speed, mean sea surface pressure (MSL), sea surface temperature (SST). Wind speed, direction, MSL and SST are from the European Centre for Medium-Range Weather Forecasts (ECMWF). After data preprocessing, the experimental data set is generated. Based on this data set, this paper establishes SVM, BP and CNN wind direction retrieval models and verifies their model performance and generalization performance. In addition, a filter that can optimize the accuracy of CNN model is constructed, and the retrieval effect under different wind direction intervals is further studied. The results show that the accuracy of CNN model for L1 data is higher than that of SVM and BP, and the retrieval error of global sea surface wind direction after filtering is less than 20°. The accuracy difference of different wind direction intervals also has a significant impact on the wind direction retrieval results.