## 4 Results
### Comparisons of Revised Winds and SFMR Winds  
According to the space-time window, the data of HY-2B/C/D winds from July 2019 to December 2021 are matched with SFMR measurements within the time window of \(\pm\)30 min. Then, the spatial average of SFMR measurements within 25 km is selected as the reference for comparison. There are 38 tropical cyclones jointly captured by HY-2B/C/D and SFMR during the period 2019-2021, and 12,287 data pairs are used to evaluate the performance of the machine learning models. Figure 9 shows the comparisons of high wind speed between HY-2B/C/D and SFMR from July 2019 to December 2021. The performance of HY-2B/C/D high winds can be obtained with a bias of -2.46 m/s, an RMSE of 4.97 m/s, and an R of 0.81. From the distribution of scattered points in Figure 9, HY-2 also underestimates the wind speed (>20 m/s) relative to SFMR wind speed, which is the same as the result of SMAP comparison. The wind speed of SFMR is up to 50 m/s, while the wind speed of HY-2 is limited to within 30 m/s. In practical application, this underestimation of wind speed is fatal, making it necessary to correct it.  
After collocating, the matchups of HY-2B/C/D and SFMR are input to these six machine learning models. Figure 10 demonstrates the performance and generalization ability of the machine learning models, including the (a) BP-NN, (b) KNN, (c) DT, (d) SVM, (e) RF and (f) BR algorithms. First, the BR algorithm shows a performance with a large RMSE of 4.62 m/s and a low correlation coefficient of 0.81, similar to the results of the 30% test dataset verification. From the distribution of scattered points in Figure 10f, it  
Figure 8: The wind field distribution before and after revision by the BP-NN algorithm in the tropical cyclone eye.  
can be seen that the retrieved high wind speed is obviously underestimated. Second, the other five machine learning algorithms show relatively stable inversion performance and generalization ability with respect to those from the SFMR measurements. In particular, BP-NN also gives an optimal accuracy of 4.35 m/s in terms of RMSE, and the accuracy of HY-2B/C/D can be improved by 12.5%. The bias can be decreased from \(-\)2.46 m/s to \(-\)0.91 m/s and the correlation coefficient can also be improved slightly.  
Figure 10: _Cont_.  
Figure 9: Two-dimensional scatter comparisons of high wind speed between HY-2B/C/D and SFMR from July 2019 to December 2021.  
Heavy rainfall is often accompanied by tropical cyclones, which is likely one of the major drivers of algorithm performance [73,74]. The rain rate data are collocated from SFMR by the same procedure of wind collocation. As shown in Figure 11, it is found that the RMSE can be decreased from 5.03 m/s to 4.38 m/s, the bias can be decreased from \(-2.42\) m/s to \(-0.24\) m/s, and the correlation coefficient can also be improved slightly under rainy conditions. The comparison shows that the BP-NN method can also give stable wind speed accuracy under such conditions.  
Figure 12a illustrates the histogram distribution of rain rate from 1 to 30 mm/h. Since the HY-2 scatterometer data are all subject to quality control, the rainfall rate of the data matched with the SFMR data is around 1 mm/h; however, the amount of strong rainfall data is not sufficient. From Figure 12b,c, it can be seen that the performance of the revised winds is better than that of the HY-2 scatterometer wind speed measurements, which shows the effectiveness of our model.  
Figure 10: Scatterplots of the revised high wind speed measurements from HY-2B/C/D scatterometers by machine learning with respect to those from SFMR measurements. (**a**) BP-NN algorithm; (**b**) KNN algorithm; (**c**) DT algorithm; (**d**) SVM algorithm; (**e**) RF algorithm; and (**f**) BR algorithm.