###### Abstract  
Haiyang-2 scatterometers (HY-2A/B/C/D) have limitations in high wind speed retrieval due to the complexity of the remote sensing mechanism and the influence of rainfall on the radar cross section under the conditions of tropical cyclones. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from HY-2B/C/D over the period from July 2019 to December 2021. HY-2B/C/D scatterometer wind products are collocated with SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The results show that the underestimation of high wind speed occurs in the HY-2B/C/D wind speed products. The machine learning algorithms are explored to improve this underestimation issue, including the back propagation neural network (BP-NN), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and Bayesian ridge (BR) regression algorithms. Comparisons show that the BP-NN algorithm shows the best performance with a small RMSE (root-mean-square error) of 3.40 m/s, and high correlation coefficient of 0.88, demonstrating an improvement of 20.4% in RMSE (root-mean-square error) compared with the HY-2B/C/D wind speed products. In addition, the revised high winds are in good agreement with the ground truth measurements from the SFMR (Stepped Frequency Microwave Radiometer), which are useful for tropical cyclone disaster prevention and mitigation and are of vital importance in the numerical simulation of storm surges. H Y-2; scatterometer; ocean surface winds; tropical cyclones; machine learning 14654. 11484654. 11484654.  
surface height, significant wave height, marine gravity, ocean circulation, and sea surface temperature [7]. This satellite is an important means of monitoring for marine disaster prevention and mitigation [8; 9; 10; 11; 12]. Compared with traditional wind measurement methods such as buoys, ships, and aircraft, scatterometers can provide continuous and wide-range datasets [13; 14]. These datasets are an important part of the basis for marine disaster prevention and mitigation and are of vital importance in numerical weather prediction (NWP) systems [2; 15; 16], which has been proven by the long-term application of the remotely sensed winds observed from the HY-2 satellite series [8]. HY-2 can measure more than 90% of the global sea surface per day with an accuracy of 2 m/s or 10% in wind speed and 20\({}^{\circ}\) in wind direction [3; 17; 18]. The premise of HY-2 scatterometer data application is data evaluation. For example, [PERSON] et al. [18] used buoy, ERA-Interim, and ASCAT data to evaluate HY-2A scatterometer wind vectors for the period 2012-2014, demonstrating the effectiveness of HY-2 data. In addition, previous studies also confirmed that HY-2A meets the operational accuracy requirement (<2 m/s) at medium and low wind speeds (2-24 m/s) [14; 19; 20; 21; 22]. The wind speed inversion of the HY-2B/C/D scatterometers also benefits from the achievements of HY-2A. The accuracy of sea surface winds observed by the HY-2B scatterometer was investigated in [23; 24; 25]. These studies show that the wind speed of HY-2B is in good agreement with the measured data. The effectiveness of HY-2C was also verified in [26], with a wind speed standard deviation of 1.03 m/s and a direction root-mean-square error (RMSE) of 15.9\({}^{\circ}\). Compared to NWP models, the wind speed deviation of the HY-2B/C/D series of scatterometers has similar characteristics in the wind speed range of 4 to 20 m/s [27]. On the whole, the HY-2 microwave scatterometer satellite series in operation can provide accurate sea surface wind field data for medium and low wind speeds (<20 m/s) in rainless areas. However, the sea surface wind field data provided by the HY-2 microwave scatterometer satellite series are not accurate under the conditions of tropical cyclones and heavy rainfall. One reason is the poor quality of the observables from the microwave scatterometer in rainy conditions [28].