Abstract
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.
Highlights
Precipitation is an important element of weather and climate systems, as well as the global cycling of water and energy (Hobbs, 1989; Fu et al, 2017; Yang et al, 2021)
It is found that there exists a notable difference in the performance between testing and training for the quantitative precipitation estimation (QPE) algorithm
The cross-validation results indicate that both daytime quantitative precipitation estimate (DQPE) and nighttime quantitative precipitation estimate (NQPE) random forest (RF) algorithms performed well in estimating QPE, with a Bias, R and RMSE of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively
Summary
Precipitation is an important element of weather and climate systems, as well as the global cycling of water and energy (Hobbs, 1989; Fu et al, 2017; Yang et al, 2021). Accurate precipitation observations are important to industrial and agricultural production, water use, and flood and drought monitoring (Behrangi et al, 2014; Gan et al, 2016; Lolli at al., 2018, 2020). Ground-based radar observations can give the spatial and temporal distribution of precipitation within a 300 km radius range, but their spatial coverage cannot be scaled up to the global scale (Lee et al, 2015). With the rapid development of remote sensing, meteorological satellites have become the only viable way to observe precipitation globally at both high spatial and temporal resolution (Tang et al, 2016; Hou et al, 2014). Large-scale and high-quality precipitation products derived from satellite
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