## 5 Discussion  
The sea surface wind field can be derived directly from the microwave scatterometer. However, there was a large error under high wind speed conditions, which makes it impossible to directly determine the typhoon intensity from scatterometer observations. However, the scatterometer data can still provide useful information when estimating the TC intensity. In our study, when choosing the model predictors, we consider different radius ranges, and we take into account the maximum, minimum, and average values of the wind speed obtained from HY-2A scatterometer. The results (as shown in Table 3) show that parameters, such as the mean wind speed near the inner center of TC are much correlated with TC intensity. Therefore, when the wind speed is higher than 30 m/s, the active microwave observations will still add some useful information. This is why we want to introduce the active microwave observations into the TC intensity estimation model. The overall accuracy of our estimated model has been improved, compared with the method of using the microwave radiometer alone [18]. But there are still large errors in some cases. Here are several possible reasons. (1) Errors in the determination of the cyclone centers, because of unorganized clouds with the TC in its earlier or dissipation stage. In the future, we will need to improve the accuracy of TC center determination, especially in the early or dissipation stage. Automated techniques such as the Automated Rotational Center Hurricane Eye Retrieval [15; 49] method might be of benefit for center determination. (2) The time difference between the overpass time of HY-2A and F17 will introduce errors into the model estimates. Adopting more restrictive criteria when matching the training dataset could reduce this error. (3) The asymmetry of cyclone shape has an influence on our circular hypothesis. Also, to solve the problem of asymmetry, we need to introduce asymmetric characteristic factors and other parameters to increase the stability of the model. (4) We used the radius of the TC center in the range of 2.5 degrees, so that the size of the TC also had a certain impact on our model. Therefore, we may consider the size of the TC as a parameter for future research. (5) One other possible source of error are the potential for lag in the intensity, especially during rapid intensification, where the satellite presentation leads the intensity by several hours. In follow-up work, we should take into account the occurrence of this rapid intensification. In this paper, firstly, we selected 15 parameters from many parameters by the stepwise regression method. Three out of the 15 selected parameters were related to 92 GHz. In the proposed model, the optimal combination of six parameters was chosen, but there were no 92 GHz parameters. In the future, more feature parameters will be introduced, and we will try to use more complicated statistical  
Figure 11: Images of Super Typhoon Noru (1705) at 30 July 2017, during the mature stage of development, (**a**) SSMIS, (**b**) HY-2A. The estimated maximum wind speed was 45.98 m/s, but the CMA best-track data was 52 m/s. The cross symbol is the center of Noru. The radii of the circles from inside to outside are 0.5\({}^{\circ}\), 0.75\({}^{\circ}\), 1\({}^{\circ}\), and 1.25\({}^{\circ}\), respectively. methods to select the optimal combination of the feature parameters. Our feature parameters are selected for all stages of TC. Some parameters may be better for certain TC stages; therefore, one may consider different feature parameter combinations in different stages of TC in their new models. The proposed model is developed based on observations of typhoons in the Northwestern Pacific Ocean. To further test whether it will still be useful to hurricanes in the Atlantic and East Pacific Oceans, we have collected more test data in these regions. Unlike the Northwestern Pacific, there are airborne observations for hurricanes.