论文标题
基于深度学习的沉淀与地面气象站数据和雷达数据
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data
论文作者
论文摘要
最近，许多深度学习技术已应用于各种与天气相关的预测任务，包括降水现象（即在不久的将来预测降水量和位置）。然而，大多数现有的基于深度学习的基于降水的方法，仅将雷达和/或卫星图像视为输入，以及从地面气象站收集的气象观测，这些观测位置很少，该地点位于稀疏的地面，相对尚未探索。在本文中，我们提出了ASOC，这是一种新型的专注方法，用于有效利用多个气象站的地面气象观测。 ASOC旨在捕获观测值的时间动态以及它们之间的上下文关系。 ASOC很容易与现有的基于图像的降水现象模型相结合，而无需更改其体系结构。我们表明，这种组合可以通过使用原始的基于图像的模型（使用来自2014年的韩国收集到的韩国收集到2020年收集的雷达图像和地面观测值），改善了预测重量（至少10 mm/hr）和光（至少1 mm/hr）的降雨事件的平均临界成功指数（CSI）。
Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most existing deep-learning-based approaches for precipitation nowcasting, however, consider only radar and/or satellite images as inputs, and meteorological observations collected from ground weather stations, which are sparsely located, are relatively unexplored. In this paper, we propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observations from multiple weather stations. ASOC is designed to capture temporal dynamics of the observations and also contextual relationships between them. ASOC is easily combined with existing image-based precipitation nowcasting models without changing their architectures. We show that such a combination improves the average critical success index (CSI) of predicting heavy (at least 10 mm/hr) and light (at least 1 mm/hr) rainfall events at 1-6 hr lead times by 5.7%, compared to the original image-based model, using the radar images and ground-based observations around South Korea collected from 2014 to 2020.
