2022/03/01 by Michiaki Tatsubori, Takao Moriyama, Tatsubori, Michiaki +13
Earth and Planetary Sciences · Environmental Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Flood Risk Assessment and Management #I.2.10 #I.3.7 #I.6.5 #J.2 #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #Precipitation Measurement and Analysis
paper · pdf · doi:10.48550/arxiv.2203.01277
openalex publication_date 2022/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.