## 4 Results
### A Revised Algorithm Based on Machine Learning
#### 4.3.2 Evaluation of Machine Learning Algorithms  
Figure 6 presents the comparisons of the revised winds by the six machine learning algorithms and SMAP. From the distribution of the scatter diagram, as shown in Figure 6f, the performance of the BR algorithm in retrieving wind speed is the worst, with a large RMSE of 3.60 m/s and a low correlation coefficient of 0.86. These six machine learning algorithms all exhibit the phenomenon of the scattered points not being concentrated in the high wind speed range. From the perspective of bias, the wind speed retrieved by the six machine learning algorithms is a slight underestimate. In terms of statistical metrics, the BP-NN algorithm achieves the best inversion accuracy with a small RMSE of 3.40 m/s, and high correlation coefficient of 0.88. Compared with the RMSE (4.27 m/s) of the original HY-2B/C/D product with respect to SMAP, the BP-NN algorithm leads to an overall improvement of 20.4%.  
Figure 7a shows the histogram of wind speed ranging from 1 to 35 m/s, which demonstrates that most data are distributed in the range 10-20 m/s. Figure 7b,c illustrates the average RMSE and bias for wind speed, ranging from 1 to 35 m/s in 1 m/s intervals of SMAP wind speed. It can be seen from Figure 7b that the RMSEs of the 5-25 m/s wind speed are almost the same before and after revision, and the RMSEs of the revised winds obtained by the BP-NN algorithm are smaller than those of the HY-2B/C/D original winds. This phenomenon also occurs in the evaluation of bias in wind speed before and after revision by the BP-NN algorithm compared to SMAP (Figure 7c). Overall, our proposed method can effectively improve the accuracy of high wind speed measurement.  
Through the above statistical analysis, we can confirm the effectiveness of our proposed method based on the BP-NN algorithm. In order to ensure the authenticity of the revised winds, we compared the spatial structure of tropical cyclones with SMAP. A variety of swirling patterns found in the tropical cyclone eye are presented in Figure 8, including Bavi, Vamco, and Molave in 2020, and Urigae in 2021 over the Western North Pacific; Dor in 2019, Genevieve in 2020, and Grace and Larry in 2021 over the Atlantic Ocean and Eastern Pacific Ocean; and Harold in 2020 and Faraji in 2021 over the southern hemisphere ocean. From the spatial distribution of tropical cyclones, the high wind speed of the HY-2 scatterometer is lower than that of the SMAP radiometer, especially during Dor in 2019 (up to 50 m/s) and Urigae in 2021 (up to 70 m/s). After calibration using the BP-NN algorithm, the revised wind speed is in good agreement with SMAP, including wind speed and spatial distribution.  
Figure 6: _Cont._Figure 6: Scatterplots of the revised high wind speeds from HY-2B/C/D scatterometers by machine learning with respect to those from the SMAP radiometer. (**a**) BP-NN algorithm; (**b**) KNN algorithm; (**c**) DT algorithm; (**d**) SVM algorithm; (**e**) RF algorithm; and (**f**) BR algorithm.  
Figure 7: Comparisons before and after revision by the BP-NN algorithm. (**a**) Two-dimensional histogram of 30% test set in the range 0–35 m/s. (**b**) RMSE in wind speed before and after revision by the BP-NN algorithm compared to SMAP. (**c**) Bias in wind speed before and after revision by the BP-NN algorithm compared to SMAP.