插值(计算机图形学)
地形
降水
计算机科学
雷达
洪水(心理学)
大洪水
气象雷达
定量降水量估算
气象学
多元插值
人工智能
线性插值
遥感
计算机视觉
地理
模式识别(心理学)
地图学
电信
考古
运动(物理)
双线性插值
心理治疗师
心理学
作者
Michiaki Tatsubori,Takao Moriyama,Tatsuya Ishikawa,Paolo Fraccaro,Anne Hudson Jones,B. Edwards,Julian Kuehnert,Sekou L. Remy
标识
DOI:10.1109/icassp43922.2022.9747829
摘要
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.
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