# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### Satellite Microwave Radiometer  
#### 2.1.1 Advanced Microwave Sounding Unit-A (AMSU-A)  
The NOAA-19 polar-orbiting meteorological satellite is the fifth operational polar-orbiting meteorological satellite in the ATOVS series launched by the NOAA, with an orbital altitude of approximately 870 km, an inclination angle of 98.9\({}^{\circ}\), and a period of 101.4 min. The onboard AMSU-A is a 15-channel microwave radiometer that was designed for profiling atmospheric temperatures in the 50-60 GHz atmospheric oxygen absorption band and has detection channels in the 23.8-, 31.2-, and 89-GHz atmospheric window areas for precipitation identification purposes. The AMSU-A instrument adopts a cross-track scanning strategy, with each scanning line containing 30 detection pixels. The imaging resolution at the subsatellite point is 45 km, and the width of each scanning line is approximately 2200 km. The scanning angle corresponding to the orbital edge pixels is 55.4\({}^{\circ}\). Since the first AMSU-A was launched on NOAA-15 in 1998, AMSU-A and its subsequent ATMS observations have become among the most influential data sources for numerical weather prediction [16].  
Clouds are essentially black bodies relative to the infrared band in terms of their radiation, and they strongly absorb infrared radiation. Infrared (IR) sensors predominantly capture information from cloud-top surfaces, which makes reflecting the internal precipitation information contained within cloud bodies difficult. In contrast, microwaves, with their longer wavelengths, can penetrate clouds to a certain extent and detect precipitation information within cloud bodies. Hence, the AMSU-A satellite microwave thermometer is chosen as the training set for precipitation detection.  
The indicator that reflects the scattering effect of AMSU-A caused by precipitation and other factors is usually represented by the _SI_, which is selected as the label for the training dataset in this paper:  
\[SI=ETB15-TB15 \tag{1}\]  
\[ETB15=a+bTB1+cTB2+dTB3 \tag{2}\]  
where _SI_ is the scatter index; _TB1_, _TB2_, _TB3_, and _TB15_ are the brightness temperatures of AMSU-A channels 1 (23.8 GHz), 2 (31.4 GHz), 3 (50.3 GHz), and 15 (89.0 GHz), respectively; _ETB15_ is the simulated clear-sky brightness temperature of channel 15; and a, b, c, and d are tangent polynomials that describe the scanning angles. The _SI_ is crucial for enhancing the efficiency of microwave observation data utilization and has a significant effect on numerical weather prediction.  
In addition to the _SI_, this study selects the cloud liquid water path (_CLWP_) as a diagnostic criterion for precipitation detection, which can be expressed as follows:  
\[CLWP=\cos(local\_zenith)*(a+0.754 log(285-TB1))-2.265\log(285-TB2) \tag{3}\]  
\[a=8.240-(2.622-1.846*\cos(local\_zenith))*\cos(local\_zenith) \tag{4}\]where _CLWP_ is the cloud water path, local_zenith is the zenith angle of the satellite, and _TBI_ and _TB2_ are the brightness temperatures of channels 1 and 2, respectively. When the _CLWP_ is greater than 0.5 g/kg [17], precipitation is considered to have occurred.