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 China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

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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.