# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 1 Introduction  
In recent years, satellite microwave sounding data, which are represented by Advanced TIROS Operational Vertical Sounder (ATOVS) radiance measurements, have gradually become one of the main data sources for numerical weather prediction centers, such asthe European Center for Medium-term Numerical Prediction (ECMWF) and the National Environmental Prediction Center (NCEP) in the United States. Microwave detection has the advantage of providing information concerning the atmospheric thermal structures of nonprecipitation cloud regions, which cover approximately 70% of the Earth's surface. Therefore, identifying precipitation-contaminated pixels is essential for effective microwave data assimilation. The detection of microwave precipitation significantly influences the microwave sounding data assimilation's effectiveness [1]. On the one hand, strong scattering by large hydrometeors attenuates microwave penetration through precipitating clouds. On the other hand, microwave scattering is highly nonlinear and thus cannot be accurately simulated by the linearized forward algorithm operator in assimilation systems [2]. Therefore, before clear-sky microwave satellite data are assimilated, pixels contaminated by precipitation must be removed. Current operational precipitation detection methods for microwave temperature sounders utilize brightness temperature differences across the 23-, 31-, and 89-GHz channels to compute the scatter index (_SI_), which determines precipitation intensity over land surfaces [3] and on the ocean surface [4]. [PERSON] et al. [5] used cloud liquid water retrieved from the brightness temperature data in the 23- and 31-GHz channels as a microwave scattering signature, which is widely performed in gridpoint statistical interpolation (GSI) assimilation systems. However, the Microwave Temperature Sounder II (MWTS2) onboard the FY-3C/D satellite, which has a nadir spatial resolution of 33 km, lacks the 23-, 31-, and 89-GHz channels required for precipitation detection [6; 7]. Therefore, the traditional microwave precipitation detection algorithms are no longer applicable to these microwave instruments. [PERSON] and [PERSON] [8] developed a new precipitation detection scheme by matching visible and infrared radiometer (VIRR)/medium-resolution spectral imager (MERSI) cloud mask data with MWTS2 pixels and used the matched cloud fraction as an alternative to the traditional method employed for MWTS2 data. This method treats clouds as integrated systems, accounting for microphysical processes to infer precipitation from cloud-top imager data [9]. Eventually, the assimilation of MWTS2 observation data is achieved, which makes a positive contribution to the global forecasts of the Global/Regional Assimilation and Prediction System (GRAPES) [10]. [PERSON] and [PERSON] [9] combined Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and Visible Infrared Scanner (VIRS) data with ERA5 reanalysis to analyze cloud/precipitation interactions in a case study. Recent advancements in artificial intelligence have spurred growing interest in its application to satellite-based precipitation detection. However, most existing machine learning models remain \"black-box\" systems, limiting mechanistic insights into precipitation processes and hindering model interpretability. Notably, the random forest (RF) algorithm stands out as one of the few interpretable machine learning models, leveraging the Gini importance index to quantitatively evaluate feature contributions. This capability has driven its successful adoption in satellite-based precipitation detection. For instance, [PERSON] et al. demonstrated the effectiveness of integrating precipitation zoning with RF regression for spatial downscaling of satellite rainfall estimates in the Lancang-Mekong River Basin [11]. [PERSON] et al. employed RF regression with FY-4A satellite infrared brightness temperatures to improve precipitation estimation accuracy [12], while [PERSON] et al.