# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
###### Abstract
Early retrieval based on satellite optical data relies on the relationship between infrared cloud top temperature and rainfall probability and intensity, that is, a high cloud top indicates strong convection and heavy precipitation [7]. This retrieval method is suitable for deep convective precipitation processes. These deep convective clouds can be easily identified in infrared or water vapor channels [8]. Yet, stratus precipitation processes caused by frontal cyclones at mid-latitudes show considerable deficiencies [9] and are characterized by relatively uniform and warm cloud top temperatures. There is little difference between precipitation and non-precipitation regions. Thus, retrieval techniques based only on the infrared cloud top temperature will lead to an underestimate of the detected precipitation area [10]. Therefore, for the retrieval of warm cloud precipitation with a relatively low cloud top height, especially stratospheric cloud precipitation, it is necessary to consider the physical properties of water droplets or ice particles in the cloud [11]. Thus, the multi-channel threshold method was later developed to extract cloud radiation characteristics and physical parameters from satellite multi-spectral data to further improve the precipitation retrieval algorithm. However, these threshold algorithms rely heavily on the parametric relationship between cloud physical properties and the precipitation process. Based on this theoretical background, only a few variables are used, and there is actually a nonlinear relationship between remote sensing information and precipitation, so it is difficult to improve the accuracy of the algorithm [12; 13; 14; 15]. With the improvement of computing power, artificial intelligence has ushered in a wave of development. Machine learning (ML) is a method of data prediction after training a model with a large amount of data as its input. Quantitative precipitation retrieval using ML methods can supplement traditional physical driving methods and is also a reliable and effective way to further improve the accuracy of satellite precipitation retrieval [16]. A number of scholars have carried out research in this area. For instance, [PERSON] et al. [17] classified convective and stratiform precipitation regions during the day, at night and at twilight using the random forest (RF) method, further improving the accuracy of their precipitation estimations. [PERSON] et al. [18] built a combined model of support vector machine (SVM), artificial neural network (ANN) and random forest (RF) classifiers to improve the classification of convective precipitation and stratiform precipitation. The developed scheme was superior to the different classifiers used alone, reaching a total classification accuracy of 97.40%. [PERSON] et al. [19] extracted some features related to precipitation based on Himawari-8 satellite data and topographic height data in East Asia by using a gradient decision tree. Their results showed the algorithm had a higher hit rate and a lower false alarm rate as a whole. In another study, [PERSON] et al. [20] established an RF classification and regression model and adopted sample balance technology to optimize it. The results showed the precipitation fall area and intensity were basically consistent with those of the GPM product. [PERSON] et al. (2018) used an RF to estimate precipitation based on Himawari-8 satellite data, which have a higher estimation accuracy for heavy rain from warm precipitation clouds. [PERSON] et al. (2018) used an SVM and the quadratic curve-fitting statistical analysis method to estimate precipitation based on Himawari-8 satellite data. The results of this study showed the SVM retrieval results were superior to those of the quadratic curve-fitting analysis. [PERSON] et al. (2018) used the dictionary learning and regularization constraint methods to estimate precipitation. Their findings showed the retrieval precipitation and GPM precipitation to have a good degree of similarity. [PERSON] et al.