###### Abstract
Another is that the geophysical model function (GMF) still struggles to effectively retrieve high wind speed in terms of intense air sea interaction under the conditions of tropical cyclones [29]. To date, much work has been done to study the high wind speed retrieval of scatterometers. Many high wind speed algorithms (the forward models and GMFs) for microwave scatterometers and radiometers were established with the goal of retrieving extreme winds under the conditions of tropical cyclones [2; 30]. In addition to the traditional inversion algorithm, machine learning has also been applied in the field of high wind speed inversion, with rapid development, especially with regard to the artificial neural network (ANN) \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **HY-2A** & **HY-2B** & **HY-2C** & **HY-2D** \\ \hline Launch time & 16 August 2021 & 25 October 2018 & 21 September 2020 & 19 May 2021 \\ Platform orbit & Sun-synchronous & Sun-synchronous & Sun-synchronous & Sun-synchronous \\ Orbit altitude & 971 km & 980 km & 971 km & 971 km \\ Revisit period & 10 day & 10 day & 10 day & 10 day \\ Frequency & 13.256 GHz & 13.256 GHz & 13.256 GHz & 13.256 GHz \\ Bandwidth & 1 MHz & 1 MHz & 1 MHz & 1 MHz \\ Polarization & HH + VV & HH + VV & HH + VV & HH + VV \\ Antenna & Rotating pencil-beam & Rotating pencil-beam & Rotating pencil-beam & Rotating pencil-beam \\ Spatial resolution & 25 km & 25 km & 25 km & 25 km \\ Swath & 1700/1350 km & 1700/1350 km & 1700/1350 km & 1700/1350 km \\ Equator crossing time & 6 pm ascending /6 am & 6 pm ascending /6 am & 6 pm ascending /6 am & 6 pm ascending /6 am \\ (local time zone) & descending & descending & descending & descending & descending \\ Wind speed precision & 2 m/s or 10\% & 2 m/s or 10\% & 2 m/s or 10\% & 2 m/s or 10\% \\ Wind direction & 20\({}^{\circ}\) & 20\({}^{\circ}\) & 20\({}^{\circ}\) & 20\({}^{\circ}\) \\ precision & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic information of the Haiyang-2 satellite series. algorithm. [PERSON] et al. [31] proposed a novel scatterometer forward model based on ANN, which has good inversion accuracy in the range of high wind speeds. In addition, the Ku-band has obvious attenuation under rainfall conditions, which directly affects the accuracy of high wind speeds. [PERSON] and [PERSON] [32] successfully applied ANN to improve the performance of the extreme winds retrieved by a scatterometer in the presence of precipitation. For the scatterometer onboard the HY-2 satellite, [PERSON] et al. [33] used the ANN algorithm to retrieve medium and high wind speeds. It was shown that the retrieved wind field can meet the accuracy requirements of the HY-2 microwave scatterometer [33]. The performance of the neural network model depends on the completeness of the training set. However, the data measured during tropical cyclones are not sufficient to support ANN model training and verification [30]. Considering that tropical cyclones have a great impact on the economy and safety of coastal areas, it is necessary to carry out the verification and calibration of the satellite remote sensing of high wind speeds based on limited in situ datasets. In this paper, we investigate the HY-2 microwave scatterometer as a remote sensing technique for monitoring extreme winds globally during the period 2019-2021. The performance of the Chinese HY-2B/C/D series scatterometer wind product in tropical cyclones is evaluated and analyzed with respect to the remotely sensed measurements of the SMAP (Soil Moisture Active Passive) L-band radiometer. Then, we explore machine learning methods to improve the extreme wind measurements retrieved by HY-2B/C/D series scatterometers. Finally, the stability of machine learning models is verified by the measured data from the SFMR (Stepped Frequency Microwave Radiometer). A description of the datasets for the HY-2B/C/D scatterometers and SMAP radiometer is provided in Section 2. The methodology of the machine learning algorithms is described in Section 3.