A near-real-time precipitation estimation product derived from geosynchronous Earth-orbiting (GEO) satellite data is highly desirable due to its ability to provide extensive coverage with high spatial and temporal resolution. This research presents a novel Deep-Learning-based Precipitation Estimation algorithm using a Multi-SpatioTemporal network (DLPE-MST), to investigate the potential of Geostationary Operational Environmental Satellite-16 (GOES-16) multitemporal images in precipitation estimation. First, a series of Advanced Baseline Imager (ABI) bispectral satellite images (6.19 and $10.35~\mu \text {m}$ ) from GOES-16 are used as inputs. Second, a module based on 3-D convolutional neural networks (3-D CNNs) is proposed to be embedded into the DLPE-MST for extracting motion features within rainfall areas. Third, a novel loss function, separated domain error (SDE), is proposed for DLPE-MST to mitigate the issue of underestimation arising from imbalanced precipitation datasets. Finally, to assess the feasibility of the DLPE-MST, GOES-16 satellite images covering the eastern Continental United States (CONUS) of America during the summer of 2020–2021 are utilized to generate raster maps depicting hourly rainfall rates at a resolution of $ 0.04^{\circ } \times 0.04^{\circ }$ . The experimental results indicate that our algorithm outperforms others in terms of probability of detection (POD) and correlation coefficient (CC), achieving scores of 91.79% and 0.58, respectively. The statistical analysis of multiple rainfall events also demonstrates that the DLPE-MST outputs are closer to the ground truth compared to other products. Furthermore, the SDE shows significant potential in alleviating the underestimation of heavy rain events. After testing, this algorithm takes only 0.09 s to generate one raster map of the rainfall rate.

中文翻译：

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使用多时相 GOES-16 图像的改进的基于深度学习的降水估计算法


来自地球同步地球轨道（GEO）卫星数据的近实时降水估算产品非常受欢迎，因为它能够以高空间和时间分辨率提供广泛的覆盖范围。本研究提出了一种使用多时空网络 (DLPE-MST) 的新型基于深度学习的降水估计算法，以研究对地静止运行环境卫星 16 (GOES-16) 多时相图像在降水估计中的潜力。首先，一系列高级基线成像仪 (ABI) 双谱卫星图像（6.19 和$10.35~\mu \text {m}$ ）来自 GOES-16 用作输入。其次，建议将基于 3-D 卷积神经网络 (3-D CNN) 的模块嵌入到 DLPE-MST 中，用于提取降雨区域内的运动特征。第三，为 DLPE-MST 提出了一种新的损失函数——分离域误差（SDE），以缓解因降水数据不平衡而引起的低估问题。最后，为了评估 DLPE-MST 的可行性，利用 2020 年至 2021 年夏季覆盖美国大陆东部 (CONUS) 的 GOES-16 卫星图像生成栅格地图，描绘每小时降雨率，分辨率为$ 0.04^{\circ } \times 0.04^{\circ }$ 。实验结果表明，我们的算法在检测概率（POD）和相关系数（CC）方面优于其他算法，分别达到 91.79% 和 0.58。对多次降雨事件的统计分析还表明，与其他产品相比，DLPE-MST 输出更接近地面真实情况。 此外，SDE 在缓解暴雨事件低估方面显示出巨大潜力。经测试，该算法仅需0.09 s即可生成一张降雨率栅格图。