Near-real-time estimation of precipitation from geostationary satellites plays a vital role in natural disaster mitigation due to timely monitoring, high spatial–temporal resolution, and large coverage, yet this letter remains a large challenge. In this letter, a novel deep learning-based algorithm entitled precipitation estimation using a multiscale network (DLPE-MS) is proposed to estimate precipitation during summer over the eastern Continental United States (CONUS) of America. When inputting bispectral satellite information (10.3 and 6.2 $\mu \text{m}$ ), this algorithm provides near-real-time rainfall rates at hourly and $0.04^{\circ}\times 0.04^{\circ}$ resolution. In order to emphasize the information of a precipitation region at different scales using satellites’ data, we design a multiscale framework based on convolutional neural networks (CNNs). In addition, a loss function named balanced weight mean square error (BWMSE) is proposed to settle the problem of underestimation caused by a shortage in heavy rainy samples. Compared with the mean square error (MSE), the BWMSE has more balance parameters for different objects when training, which is able to mitigate the deviation between the prediction and ground truth in tailed categories (heavy rainy). Results show that this algorithm achieves the highest probability of detection (POD) and correlation coefficient (CC) with the value of 95.5% and 0.5. The statistical results of the precipitation cases also show that the DLPE-MS can significantly improve the estimated values in tailed categories than other products and methods. After testing, this algorithm is able to estimate the precipitation for the study area within 0.19 s.

中文翻译：

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使用深度学习改进近实时降水估计算法

由于实时监测、高时空分辨率和大覆盖范围，地球静止卫星降水的近实时估计在减轻自然灾害中发挥着至关重要的作用，但这仍然是一个巨大的挑战。在这封信中，提出了一种新的基于深度学习的算法，题为使用多尺度网络 (DLPE-MS) 估计降水量，以估计美国东部大陆 (CONUS) 夏季的降水量。输入双光谱卫星信息时（10.3 和 6.2 $\mu \text{m}$ )，该算法提供近乎实时的每小时降雨率和 $0.04^{\circ}\times 0.04^{\circ}$解析度。为了利用卫星数据强调不同尺度降水区域的信息，我们设计了一个基于卷积神经网络（CNN）的多尺度框架。此外，为了解决由于暴雨样本不足而导致的低估问题，提出了一种称为平衡权重均方误差（BWMSE）的损失函数。与均方误差 (MSE) 相比，BWMSE 在训练时针对不同对象有更多的平衡参数，能够减轻尾部类别（大雨）中预测与地面实况的偏差。结果表明，该算法实现了最高的检测概率（POD）和相关系数（CC），分别为95.5%和0.5。降水案例的统计结果也表明，DLPE-MS比其他产品和方法可以显着提高尾部类别的估计值。经测试，该算法能够在0.19 s内估算出研究区的降水量。