Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using the China’s latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a Random Forest (RF) model framework for FY-4A QPE during daytime/nighttime is established by using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations, and physical quantities from reanalysis data. During daytime (nighttime), the probability of detection of the RF model for precipitation is 0.99 (0.99), while the correlation coefficient and root-mean-square error between the retrieved and observed precipitation are 0.77 (0.82) and 1.84 (2.32) mm/h, respectively, indicating that the RF model of FY-4A QPE has high precipitation retrieval accuracy. In particular, the RF model exhibits good spatiotemporal predictive ability for precipitation intensities within the range of 0.5–10 mm/h. For the retrieved accumulated precipitation, the precipitation intensity exhibits a greater impact on the predictive ability of the QPE algorithm than the precipitation duration. Due to the higher density of automatic stations in urban areas, the accuracy of FY-4A QPE over such areas is higher compared with rural areas. Both the accumulated precipitation and the distribution density of automatic stations are more important factors for the predictive ability of the RF model of FY-4A QPE. In general, our proposed FY-4A QPE algorithm has advantages for near-real-time monitoring of summer precipitation over East Asia.