Satellite‐based precipitation retrieval is an essential and long‐standing scientific problem. With an increase of observational satellite data, the advances of data‐driven approaches such as machine learning (ML)/deep learning (DL) are favored to deal with large data sets and potentially improve the accuracy of precipitation estimates. In this study, we took advantage of new technologies by wrapping up a ML/DL‐based model pipeline (LinkNet segmentation + tree ensemble). This approach is applied to the Advanced Microwave Sounding Unit (AMSU) on National Oceanic and Atmospheric Administration 18 and 19 flight, and compared with the MultiRadar MultiSensor. Four simulations were configured to examine the performance gain by incorporating three components: (1) precipitation identification, (2) nonlocal features, and (3) precipitation classification. More importantly, we examined the interpretability of the “black box” model to get a better understanding of the underlying physical connections. First, the results by this model pipeline suggest the advantages of the ML model by reducing the systematic error and instantaneous error to a factor of two. Second, identifying precipitation pixels helps to reduce the systematic error by 130%, and predicting precipitation classification benefits improved correlations by 32%. Last, channels at higher frequencies (beyond 150 GHz) are favored to identify precipitation regions, and also channels at 89 and 150 GHz are ranked as the two most important features to precipitation retrieval. This study explores the potentials of AMSU precipitation estimates with ML algorithms and provides means of interpreting the models to facilitate the better prediction of precipitation.

中文翻译：

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用数据驱动的方法推进卫星降水的检索：黑匣子模型可以解释吗？

基于卫星的降水检索是一个必不可少且长期存在的科学问题。随着观测卫星数据的增加，诸如机器学习（ML）/深度学习（DL）之类的数据驱动方法的进步将有利于处理大型数据集，并有可能提高降水估计的准确性。在本研究中，我们通过包装基于ML / DL的模型管道（LinkNet分段+树集合）来利用新技术。该方法适用于国家海洋和大气管理局18和19航班的高级微波探测装置（AMSU），并与MultiRadar MultiSensor进行了比较。配置了四个模拟，以通过结合三个组件来检查性能增益：（1）降水识别，（2）非局部特征和（3）降水分类。更重要的是，我们检查了“黑匣子”模型的可解释性，以更好地了解基础物理连接。首先，该模型流水线的结果通过将系统误差和瞬时误差减小到两倍，从而表明了ML模型的优势。其次，识别降水像素有助于将系统误差降低130％，而预测降水分类将使相关性提高32％。最后，倾向于使用较高频率（超过150 GHz）的信道来识别降水区域，并且将89和150 GHz的信道列为降水检索的两个最重要特征。