## 3 Methodology
### Relationship between the Satellite-Observed Parameters and the Maximum Wind Speed
#### 3.1.1 TB Parameters  
In all, 1680 TB parameters were computed. In this section, the correlations between the TB parameters and TC intensity will be analyzed. The relationship between the single parameter of TB  
Figure 3: An example of how the calculation regions for parameters are named. The circles are concentric circles whose radii represent every 0.25\({}^{\circ}\) latitude from 0.5\({}^{\circ}\) to 2.5\({}^{\circ}\) latitude. The cross is the center of the TC. C100 represents a concentric circle; the radius is 1\({}^{\circ}\). A100125 represents the annuli whose inner radius is 1.0\({}^{\circ}\) and the outer radius is 1.25\({}^{\circ}\) (adapted from Hoshino & Nakazawa, 2007 (2017)). and the MSW of TC was analyzed by regression analysis, and then the Root Mean Square Error (RMSE) between the estimated and the best track MSW was calculated. The correlation coefficients between all of the parameters and the MSW of TC was calculated, and a significant horizontal \(t\)-test was conducted for the correlation coefficients. The 10 highest correlated TB parameters with the maximum wind speeds for the best CMA tracks are shown in Table 2. The correlation coefficients and RMSEs are also given in the table. The highest correlated parameters were for TB19H_MIN_C100. The highest correlation coefficient was 0.84, and the RMSE was 6.67 m/s. The following conclusions can be inferred from Table 2:  
There is a tendency for the parameters of 19, 22, and 37 GHz channels to give higher correlations with the TC maximum wind speed. The high frequency band reaches saturation when the cloud liquid water path is greater than 0.3 mm [37], while the cloud liquid water path in the eyewall of TC is between 0 and 0.5 mm [38]. Lower frequencies are less sensitive to the path of liquid water in clouds within this range. With the decreased sensitivity to the atmosphere, these channels will provide more information about the surface, and therefore lower frequencies could have higher correlations with the maximum wind speed. The best correlated parameters for all frequencies are MIN and a few RAPT, but not MAX. MAX brightness temperature will be very high when there are more precipitation particles in a small area of TC, but this does not represent the overall intensity of TC [39]. Also, with the increase of precipitation intensity, different frequencies will have different degrees of saturation. MIN informs that all other points are larger than this number, and RAPT indicates the ratio of an active area, and hence it may be a more appropriate indicator to represent the TC intensity. The parameters in the surrounding region (0.75-1.25\({}^{\circ}\) away from the TC center, especially 1\({}^{\circ}\)) tend to give higher correlations with the TC intensity. It is inferred that the intensity of the TC would be close to the radius of maximum wind speed. TC intensity estimates using a fixed annulus or circle could be impacted by TC eye size [39], so we should consider the sizes of the eyes, or the TCs in the future work. The test dataset in Table 1 was used to verify the performance of only using TB19H_MIN_C100 parameter. The results of the validation are shown in Figure 4. The horizontal axis is the maximum wind speed that is estimated by the TB parameter model, and the vertical axis is the CMA TC best track intensity data. The R\({}^{2}\) of the verification result for the test dataset was 0.76, the RMSE was 6.97 m/s, the MAE was 5.50 m/s, and the bias was \(-\)1.78 m/s. Figure 4b,c show the differences between the TB parameter model-estimated TC intensity and the CMA best-track data in two subgroups. Figure 4b relates to lower-intensity TCs, i.e., tropical depressions, tropical storms, and severe tropical storms. Figure 4c relates to typhoons, severe typhoons, and super typhoons.