High-quality precipitation data are essential for research in hydrology, meteorology and ecology. Nevertheless, in mountainous regions with intricate terrain, the reliability of gridded precipitation data derived from station data interpolation is low due to the limited number of stations caused by the difficulty of station setup. Current satellite precipitation products suffer from low spatial resolution, making them unsuitable for hydrological and meteorological research at small and medium scales. Their application in mountainous regions with significant spatiotemporal heterogeneity is even more challenging. To this end, downscaling satellite precipitation products has become an effective method for obtaining accurate spatial distribution information of precipitation in these regions. This study employs a method of first calibration followed by downscaling analysis of GPM daily precipitation product in the Chongqing area using random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) algorithms. Ultimately, the spatial resolution of GPM product is improved from 0.1° to 0.01° (∼1 km). The findings demonstrate that: (1) the station-calibrated GPM precipitation product performed better than the original GPM product, and it is closer to the station measurements; (2) in practical applications, the LSTM downscaling algorithm can effectively enhance spatial resolution without compromising accuracy, whereas RF and XGBoost incur considerable accuracy loss when enhancing spatial resolution; (3) the downscaled results from all three algorithms were consistent with the calibrated GPM precipitation maps and significantly improved the spatial details of precipitation. Among them, the results of the LSTM method exhibited greater continuity in the spatial distribution of precipitation, aligning more closely with the characteristics of precipitation distribution. In summary, the LSTM algorithm demonstrates greater potential for the downscaling of GPM precipitation product in the study area. This research provides a promising high-quality precipitation data generation scheme for mountainous regions with sparse station coverage and complex terrain and landforms.

中文翻译：

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山区GPM卫星降水产品的机器学习降尺度——以重庆为例


高质量的降水数据对于水文学、气象学和生态学研究至关重要。然而，在地形复杂的山区，由于建站困难，站点数量有限，由站点数据插值得到的网格化降水数据的可靠性较低。目前的卫星降水产品空间分辨率较低，不适合中小尺度的水文气象研究。它们在时空异质性显着的山区的应用更具挑战性。为此，降尺度卫星降水产品成为获取这些地区准确降水空间分布信息的有效方法。本研究采用先校准后使用随机森林（RF）、极端梯度提升（XGBoost）和长短期记忆（LSTM）算法对重庆地区GPM日降水量产品进行降尺度分析的方法。最终，GPM产品的空间分辨率从0.1°提高到0.01°（∼1 km）。研究结果表明：（1）台站校准的GPM降水产品比原始GPM产品表现更好，更接近台站测量结果； （2）在实际应用中，LSTM降尺度算法可以在不影响精度的情况下有效增强空间分辨率，而RF和XGBoost在增强空间分辨率时会产生相当大的精度损失； (3)三种算法的降尺度结果与校准的GPM降水图一致，显着改善了降水的空间细节。 其中，LSTM方法的结果在降水空间分布上表现出更大的连续性，更符合降水分布特征。综上所述，LSTM算法展示了研究区GPM降水产品降尺度的更大潜力。该研究为站点覆盖稀疏、地形地貌复杂的山区提供了一种有前景的高质量降水数据生成方案。