## 5 Conclusions  
This work focuses on the evaluation and calibration of remotely sensed winds from the HY-2B/C/D scatterometers in tropical cyclones over the period from July 2019 to December 2021. SMAP high wind speeds are used as a stable reference to investigate the performance of the HY-2B/C/D scatterometers. After matching, it is found that HY-2 has a good ability to capture tropical cyclones globally due to its wide swath of 1700 km. However, comparisons demonstrate that high wind speeds are obviously underestimated by HY  
Figure 11: Statistical analysis (**a**) before and (**b**) after revision by the BP-NN algorithm under rainfall conditions.  
Figure 12: Comparisons before and after revision by the BP-NN algorithm with respect to SFMR. (**a**) Two-dimensional histogram of rain rate in the range 0–30 mm/h. (**b**) RMSE in wind speed before and after revision by the BP-NN algorithm under different rain rate conditions. (**c**) Bias in wind speed before and after revision by the BP-NN algorithm under different rain rate conditions.  
2B/C/D scatterometers. In order to overcome the deficiency of HY-2B/C/D measurements, six machine learning algorithms are used to calibrate the high wind speeds of HY-2B/C/D, including the BP-NN, KNN, DT, SVM, RF, and BR algorithms. Through the verification of the 30% test dataset, it can be seen that the BP-NN algorithm achieves the best inversion accuracy, with a small RMSE of 3.40 m/s, and a high correlation coefficient of 0.88. In addition, the spatial distribution of the revised wind speed is in good agreement with that of SMAP, which will be of benefit for tropical cyclone monitoring by HY-2B/C/D scatterometer satellites. Finally, the performance of the six machine learning algorithms is verified by SFMR data, demonstrating that BP-NN also achieves the best inversion accuracy and that the performance can be improved by 12.5%. We also compared the wind speed accuracy under different rain rates and found that the BP-NN model can also provide stable wind speed accuracy under rainy conditions.  
By using the relationship between HY-2B/C/D wind speed measurements and SMAP measurements, the performance of HY-2B/C/D in high wind speeds has been improved significantly based on machine learning algorithms. However, the parameters of each machine learning algorithm can be further optimized by GridsearchCV technology. The revised high wind speed of HY-2B/C/D scatterometers using machine learning algorithms shows good agreement with SMAP and SFMR up to 60 m/s, which is expected to play an important role in tropical cyclone intensity estimation, structure identification, model validation, and the numerical simulation of storm surges.  
Conceptualization, [PERSON], [PERSON] and G.H.; methodology, [PERSON] and [PERSON]; software, validation, and formal analysis, [PERSON]; writing--original draft preparation, [PERSON] and J.W.; writing--review and editing, [PERSON], [PERSON] and G.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the Scientific Research Fund of the Second Institute of Oceanography, MNR, grant no. JB2205, and was supported by the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) (no. 311021004).  
Not applicable. Conflicts of Interest: The authors declare no conflict of interest.