| Citation: | ZHANG Y，ZHAO X Y，YANG S H，et al. Quality control model of CYGNSS sea surface wind speed retrieval based on ML combination[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：20-29 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0220
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Convolutional neural networks (CNN) can be used for sea surface wind speed retrieval of cyclone global navigation satellite system (CYGNSS). There are still anomalous values in the observation data of CYGNSS, despite the fact that numerous quality control indicators have been set up to detect and weaken the abnormal observation data of CYGNSS before model training, which results in a drop in model retrieval accuracy and even incorrect retrieval results. Therefore, this paper proposes a wind speed retrieval model based on machine learning (ML) combination. Based on the CYGNSS retrieval of sea surface wind speed based on the CNN regression model, the ML classification model generates the quality flag of the CNN regression result, which can detect and remove the outliers of the CNN regression results to further improve the data quality of the wind speed retrieval results, and the ML classification model can better consider the interaction between various data errors, instead of using the threshold for each condition individually, to achieve better results. The effect of retrieval accuracy of sea surface wind speed. Six classification models were compared in the experiments, including Logistic regression (LR), decision tree (DT), naive Bayes model, K-nearest neighbor (KNN) algorithm, neural network (NN) model, and support vector machine (SVM). It was ultimately determined that the classification model based on KNN algorithm had the best impact on the quality control of wind speed retrieval. The wind speed retrieval combined model significantly improves the accuracy of the retrieval results. In the range of 0−20 m/s, the filtering rate of abnormal samples is 81.27%, and in all filtered data, the filtering correct rate is 86.03%; the root mean square error of the error is reduced from 1.7 m/s for the classification model without ML to 1.44 m/s for the classification model with ML. Among them, the training sample is 0−10 m/s retrieval results, and the accuracy improvement effect is more obvious, which proves the effectiveness of the ML combination model proposed in this paper for wind speed quality control.
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