Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning

Abstract

Cloud properties governing precipitation formation remain poorly understood due to their intrinsic complexity and the difficulty of identifying physically consistent predictors. This study identifies the key variables in precipitation retrievals over the East Asian mid-latitude and tropical warm pool regions using the geostationary satellite data collected in the year 2023, in conjunction with machine learning and Shapley additive explanations. In the East Asian mid-latitudes, light-to-moderate precipitation is associated with the ice water path and the cloud particle growth characteristics, as indicated by the spectral reflectance (R) difference between 1.61 µm and 0.47 µm (R0.47 − R1.61) and between 1.61 µm and 0.64 µm (R0.64 − R1.61), respectively. Heavy precipitation events are linked to the presence of high-altitude cirrus clouds indicated by R1.37 and the upper-tropospheric moisture content characterized by the brightness temperature (BT) difference between 6.2 µm and 9.6 µm (BT6.2 − BT9.6). Over the tropical warm pool region, light-to-moderate precipitation is related to ice water path and cloud optical thickness, while heavy precipitation is predominantly related to the surface reflectance contrast, represented by R0.47 − R0.86 and R0.64 − R0.86, which modulates the initiation and intensity of convection. These results demonstrate that the optimal cloud properties and environmental predictors for satellite-based precipitation retrievals vary by regions and intensities.