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ConvSNow: A tailored Conv-LSTM architecture for weather nowcasting based on satellite imagery.
- Source :
- Procedia Computer Science; 2023, Vol. 225, p298-307, 10p
- Publication Year :
- 2023
-
Abstract
- Nowcasting represents a short-term weather forecast of how the atmospheric state will evolve during the next time period, typically less than two hours. It is vital for generating society-level emergency alerts in order to take timely actions and responses to potential disasters. The objective of the paper is to improve upon current nowcasting methods by applying a Deep Learning model that uses Convolutional Long-Short Term Memory Networks on a combination of satellite data. It is proposed a model ConvS Now for short-term prediction of satellite images that would be useful for precipitation nowcasting. The proposed model was trained and evaluated on satellite imagery collected by EUMESAT's Meteosat-11 satellite utilizing the Severe Storms RGB product. The experimental results performed a subset of the Meteosat-11 data spanning Europe demonstrate that this model can enhance weather short-term forecasting, reduce costs and time, and improve the general quality of predictions, as a normalized mean of absolute errors of 1.6% was attained, outperforming every other baseline approaches considered for comparison. A relative improvement of more than 30% has been achieved by the ConvS Now compared to the baselines, our proposed model being able to capture the spatio-temporal features of the weather evolution. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE-sensing images
DEEP learning
WEATHER
SEVERE storms
EMERGENCY management
Subjects
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 225
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 174059066
- Full Text :
- https://doi.org/10.1016/j.procs.2023.10.014