DeePS at: A deep learning model for prediction of satellite images for nowcasting purposes

Due to the increasing number of severe phenomena in many regions of the world, weather nowcasting, which is the weather forecast for a short time period, is one of the most challenging topics in meteorology. The weather radar and satellite are essential tools currently used by operational meteorologists for nowcasting. Issuing nowcasting warnings based on radar and satellite data is a complex task, due to the large volume of data that should be analyzed by meteorologists in order to make decisions. We are introducing in this paper DeePS at, a convolutional neural network architecture for short-term satellite images prediction that would be useful for weather nowcasting. The experimental evaluation is conducted on satellite data collected by EUMETSAT's Meteosat-11 satellite, using five satellite products. The obtained results are analyzed and compared to the results of similar approaches. An average normalized mean of absolute errors of 3.84% was obtained for all satellite products, highlighting this way the effectiveness of DeePS at model. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

Automated summary

This paper introduces DeePS at, a convolutional neural network architecture for short-term satellite images prediction, which has been evaluated for its effectiveness.