WeatherFusionNet: Predicting Precipitation from Satellite Data

• URL: http://arxiv.org/abs/2211.16824v1
• Date: Wed, 30 Nov 2022 08:49:13 GMT
• Title: WeatherFusionNet: Predicting Precipitation from Satellite Data
• Authors: Ji\v{r}\'i Pihrt, Rudolf Raevskiy, Petr \v{S}im\'anek, Matej Choma
• Abstract summary: We aim to predict high-resolution precipitation from lower-resolution satellite radiance images. A neural network called WeatherFusionNet is employed to predict severe rain up to eight hours in advance. We achieved 1st place in the NeurIPS 2022 Weather4Cast Core challenge.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The short-term prediction of precipitation is critical in many areas of life. Recently, a large body of work was devoted to forecasting radar reflectivity images. The radar images are available only in areas with ground weather radars. Thus, we aim to predict high-resolution precipitation from lower-resolution satellite radiance images. A neural network called WeatherFusionNet is employed to predict severe rain up to eight hours in advance. WeatherFusionNet is a U-Net architecture that fuses three different ways to process the satellite data; predicting future satellite frames, extracting rain information from the current frames, and using the input sequence directly. Using the presented method, we achieved 1st place in the NeurIPS 2022 Weather4Cast Core challenge. The code and trained parameters are available at \url{https://github.com/Datalab-FIT-CTU/weather4cast-2022}.

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