Deep Temporal Interpolation of Radar-based Precipitation

Autor:	Michiaki Tatsubori, Takao Moriyama, Tatsuya Ishikawa, Paolo Fraccaro, Anne Jones, Blair Edwards, Julian Kuehnert, Sekou L. Remy
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning I.2.10 J.2 Computer Science - Artificial Intelligence I.3.7 Computer Science - Computer Vision and Pattern Recognition I.6.5
Popis:	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. Comment: 5 pagers, 4 figures, ICASSP-22. arXiv admin note: text overlap with arXiv:1712.00080 by other authors
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::590682fa677196c8975232118c9221d7 http://arxiv.org/abs/2203.01277 Zobrazit plný text záznamu