论文标题
基于雷达降水的深度时间插值
Deep Temporal Interpolation of Radar-based Precipitation
论文作者
论文摘要
当提供水文洪水模型的边界条件并估算相关风险时，在非常高的时间分辨率（例如5分钟）下插值降水是至关重要的，不要错过当地地区洪水的原因。在本文中，我们研究了来自卫星的全球可用天气雷达图像的基于光流的插值。所提出的方法使用深层神经网络进行多个视频帧的插值，而地形信息则与暂时粗粒的降水雷达观察结合在一起，作为自我监督训练的输入。对Meteonet雷达降水数据集进行了对Aude的洪水风险模拟的实验，该部门在法国南部（2018年）（2018年），证明了该方法的优势优于线性插值基线，误差降低了20％。
When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minutes) is essential not to miss the cause of flooding in local regions. In this paper, we study optical flow-based interpolation of globally available weather radar images from satellites. The proposed approach uses deep neural networks for the interpolation of multiple video frames, while terrain information is combined with temporarily coarse-grained precipitation radar observation as inputs for self-supervised training. An experiment with the Meteonet radar precipitation dataset for the flood risk simulation in Aude, a department in Southern France (2018), demonstrated the advantage of the proposed method over a linear interpolation baseline, with up to 20% error reduction.
