Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

中文翻译：

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深度学习方法在气象私有云中估算地面雷达降水

准确的降水估计非常重要，因为它对每个人的社会和经济活动都至关重要，并且对监测和预测灾害非常重要。传统方法利用雷达反射率因子与降水之间的指数关系（称为ZR关系），在降水估算中精度较低。随着云计算中计算能力的飞速发展，最近的研究表明，人工智能是一种很有前途的方法，尤其是在学习准确模式方面的深度学习方法，并且在充分考虑雷达数据的情况下，似乎非常适合于降水估算的任务。在这项研究中，我们将这些方法引入降水估算，分别提出了基于反向传播神经网络（BPNN）和卷积神经网络（CNN）的两种模型，以与气象服务系统中的传统方法进行比较。三种方法的结果表明，深度学习算法优于传统方法，均方误差分别降低了75.84％和82.30％。同时，所提出的CNN方法比BPNN具有更好的性能，因为它能够通过保持像素之间的互连来保留空间信息，与BPNN相比提高了26.75％。均方误差分别降低30％。同时，所提出的CNN方法通过保持像素之间的互连来保留空间信息的能力比BPNN具有更好的性能，与BPNN相比提高了26.75％。均方误差分别降低30％。同时，所提出的CNN方法比BPNN具有更好的性能，因为它能够通过保持像素之间的互连来保留空间信息，与BPNN相比提高了26.75％。