# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 4 Results
### A Single-Case Analysis
#### 4.1.1 Matching \(RF\_SI\) to AMSU-A Pixels
In the area containing the Gulf of Thailand (13 degN, 102 degE), which is on the edge of the orbit of AMSU-A, the _SI_ has a value of 25 K, indicating the presence of clouds but without any scattering. The _CLWP_ is identified as a partially cloudy area with 0 g/kg-0.3 g/kg. The _RF_SI_ value is approximately 25 K, and its distribution matches that of the _SI_. At 13 degN, 85 degE, where the _SI_ is 24 K and the _CLWP_ is 0.25 g/kg, the area is identified as a cloudy region with no scattering. At this point, _RF_SI_ equals 50, indicating a strong scattering region. Compared with that in Figure 9a, this region corresponds to cumulonimbus clouds in the VISSR image. Although cumulonimbus clouds are the primary contributors to _RF_SI_, VISSR-based cumulonimbus cloud identification cannot be used to determine their life stages. When the density of cloud hydrometeors is small enough such that they do not induce microwave scattering, both the _SI_ and _CLWP_ demonstrate low values, whereas _RF_SI_, which is related primarily to the structures of cumulonimbus clouds, remains high. This is one of the limitations found when optical observations are used to retrieve microwave measurement data within clouds. A further analysis is conducted on the distribution of the AMSU-A pixels contained in the Bay of Bengal region. In Figure 11, pink pixels represent occurrences of precipitation, whereas yellow pixels represent nonprecipitation instances. The grayscale background in  
Figure 10: Pixel analysis of the AMSU-A scanning region: (**a**) _RF_SI_ results, (**b**) AMSU-A _SI_, and (**c**) AMSU-A _CLWP_. the figure represents the VISSR cloud-top brightness temperatures. Figure 11a displays the results produced after the high-resolution _RF_SI_ data are mapped onto the AMSU-A pixels. Notably, good correspondence is observed between the precipitation pixels derived from _RF_SI_ and the regions with low observed brightness temperatures. Figure 11b,c depict the distributions of the AMSU-A _SI_ and _CLWP_ values. The relationships between the _SI_, _CLWP_, and brightness temperature are consistent with those shown in Figure 11a. Similar to the _SI_ and _CLWP_, _RF_SI_ corresponds only to regions with deep convective clouds, effectively indicating the horizontal distribution of microwave scattering. To more clearly demonstrate the effectiveness of the _RF_SI_ precipitation detection approach, the results of the VISSR cloud classification product are visualized in Figure 11d. Upon examination, it can be observed that within the 8-12\({}^{\circ}\)N region, _RF_SI_ is capable of detecting a greater number of convective cloud pixels than the _SI_ and _CLWP_ methods can, which highlights the superior performance of _RF_SI_. To quantitatively evaluate the _RF_SI_ method via (11)-(13), the _ACC_, _POD_, and _FAR_ relative to results of the _SI_ and _CLWP_ methods can be calculated, as shown in Table 3. The table shows that the _ACC_ of _RF_SI_ relative to the _SI_ is greater than 95%, and the _POD_ and _FAR_ relative to the _SI_ are lower, at 92% and 3%, resp  
Figure 11: Precipitation detection results obtained for the Bay of Bengal region in terms of different indicators by (**a**) _RF_SI_; (**b**) the _SI_; (**c**) and _CLWP_ (the pink points represent occurrences of precipitation, and the yellow points represent nonoccurrences of precipitation). (**d**) The FY-2F VISSR CLC products. \(RF\_SI\) algorithm is comparable to traditional precipitation detection methods that are commonly used.