Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information
Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information
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DOI：
10.1016/j.envsoft.2020.104856
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
M. Sadeghi;P. Nguyen;K. Hsu;S. Sorooshian
中科院分区：
文献类型：
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作者：
M. Sadeghi;P. Nguyen;K. Hsu;S. Sorooshian
可靠的近实时降水量估计对于监测和管理洪水等自然灾害至关重要。输入数据的质量和反演算法的能力是发展基于卫星的降水数据集的两个重要方面。由于红外信息具有良好的时空分辨率和近瞬时可用性，大多数检索算法都使用红外信息作为输入。然而，它们对IR信息的唯一依赖限制了它们在训练期间学习不同降水机制的能力，导致不太准确的估计。此外，机器学习领域的最新进展为改进降水反演算法提供了有吸引力的机会。本研究探讨了在红外信息中加入地理信息（即纬度和经度）的有效性，以及基于U-Net的卷积神经网络在提高检索算法准确性方面的应用。这项研究表明，在地理和红外信息上应用适当的CNN架构为改进基于卫星的降水产品提供了机会。
Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This study investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.