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Precipitaion Nowcasting using Deep Neural Network

2022-03-24 17:28:08
Mohamed Chafik Bakkay, Mathieu Serrurier, Valentin Kivachuk Burda, Florian Dupuy, Naty Citlali Cabrera-Gutierrez, Michael Zamo, Maud-Alix Mader, Olivier Mestre, Guillaume Oller, Jean-Christophe Jouhaud, Laurent Terray

Abstract

Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. We propose the use three popular deep learning models (U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps for precipitation nowcasting. We proposed an algorithm for patch extraction to obtain high resolution precipitation maps. We proposed a loss function to solve the blurry image issue and to reduce the influence of zero value pixels in precipitation maps.

Abstract (translated)

URL

https://arxiv.org/abs/2203.13263

PDF

https://arxiv.org/pdf/2203.13263.pdf


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