Rossatto, F. C.; Harter, F.; Shiguemori, E.; Calvetti, L. Recurrent Convolutional Neural Networks Applied to Short-Term Weather Forecasting by Radar Images. Preprints2024, 2024081766. https://doi.org/10.20944/preprints202408.1766.v1
APA Style
Rossatto, F. C., Harter, F., Shiguemori, E., & Calvetti, L. (2024). Recurrent Convolutional Neural Networks Applied to Short-Term Weather Forecasting by Radar Images. Preprints. https://doi.org/10.20944/preprints202408.1766.v1
Chicago/Turabian Style
Rossatto, F. C., Elcio Shiguemori and Leonardo Calvetti. 2024 "Recurrent Convolutional Neural Networks Applied to Short-Term Weather Forecasting by Radar Images" Preprints. https://doi.org/10.20944/preprints202408.1766.v1
Abstract
In this study, a computational method is proposed that employs Recurrent Convolutional 1 Neural Networks, utilizing meteorological radar images to forecast storm movement and intensity up 2 to 3 hours ahead, a process known as nowcasting. For this purpose, images from a radar situated in 3 southern Brazil were used. These data are publicly accessible on the website of the National Institute 4 for Space Research (INPE) in Brazil. The approach involves evaluating a spatiotemporal learning 5 recurrent convolutional neural network called PredRNN++. The results were validated through 6 case studies of storms within the radar’s coverage area. To evaluate the performance of the neural 7 network, both visual assessments and metrics such as RMSE and SSIM were employed. The findings 8 indicate that PredRNN++ was effective in simulating the shape and location of the meteorological 9 system.
Keywords
Neural Network; Nowcasting; Radar
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
