# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 4 Results
### A Time-Series Analysis
#### 4.2.2 Analysis of the Deviation Between the Observed and Simulated Brightness Temperatures (O-B)  
Owing to the scattering effect of precipitation particles, the microwave channel brightness temperatures over precipitation areas are significantly lower than those over clear-sky regions. This subsequently leads to an amplification of the O-B of forward-modeled satellite radiation obtained via clear-sky profiles of temperature and moisture. Precipitation detection relies on high-quality satellite microwave observations that are not contaminated by precipitation, where the O-B before precipitation detection is noticeably lower than the O-B after precipitation detection. The precipitation detection results of _RF_SI_ are expected to have an O-B distribution similar to that of the _SI_ results. The peak weighting function values of channels 6 to 9 of AMSU-A occur between 400 and 90 hPa, and they are influenced considerably by the precipitation occurring within the troposphere. This study focuses on analyzing the O-B distributions of these four channels. Figure 16 presents the O-B distributions observed before performing bias correction on the nonprecipitation pixels in these four channels. Both the _RF_SI_ and _SI_ methods are capable of filtering out approximately 10-20% of the precipitation pixels from the raw data. Moreover, after the precipitation exclusion step is complete, the O-B distribution trends of the _RF_SI_ and _SI_ methods are consistent, with their curves overlapping relatively well. Channels 6 and 7 exhibit nonrainfall pixels in the ranges of [\(-3\) K, \(-4.3\) K] and [\(-3.2\) K, \(-3.7\) K], respectively. This result suggests that _RF_SI_ might have potential issues when detecting warm cloud precipitation below cirrus clouds. The detection heights of channels 6 and 7 of AMSU-A are below 270 hPa, where large-diameter precipitation particles covered by cirrus clouds are still able to trigger microwave scattering. After performing _RF_SI_ detection, channels 8 and 9 still show samples in both the peak-value and valley-value regions. This finding implies that issues related to the underdetection and overdetection of precipitation are present. Specifically, issues are encountered with respect to performing _RF_SI_ detection via microwave observations under multilayer cloud conditions. In the  
Figure 15: Latitude analysis of _RF_SI_. (**a**) Variations in the _ACC_, _POD_, and _FAR_ values of _RF_SI_ and (**b**) the distribution of data along the latitudinal gradient. multilayer cloud cover case, the VISSR can obtain cloud-top information, which may lead to the underidentification or overidentification of precipitation by _RF_SI_. However, in the peak-value region, _RF_SI_ retains more samples than the _SI_ does. In the middle and lower levels of the atmosphere, the values detected via the two methods corresponding to the peak data count are basically the same. Compared with those of the _SI_ method, the peak data count values in the upper troposphere (channel 9) are offset by approximately 0.2 K according to the _RF_SI_ method. For the paired samples collected between 6 June 2017 and 10 June 2017, the daily variation exhibited by the numbers of retained nonprecipitation pixels determined using both detection methods is shown in Figure 17a. The daily statistics reveal that _RF_SI_ retains more nonprecipitation pixels than the _SI_ threshold method does, with a maximum of approximately 500 pixels. Over the five-day period, the _SI_ threshold method retains 20,639 nonprecipitation pixels, whereas _RF_SI_ retains a total of 22,040 pixels, approximately 5% more than the former. Figure 17b displays the _RMSE_ distributions of the O-B results produced for the four channels. The daily _RMSE_ curves of the nonprecipitation pixel O-B distributions obtained after performing _RF_SI_ and _SI_ detection are nearly identical.