A conditional Generative Adversarial network model for the Weather4Cast 2024 Challenge

This study explores the application of deep learning for rainfall prediction, leveraging the Spinning Enhanced Visible and Infrared Imager (SEVIRI) High rate information transmission (HRIT) data as input and the Operational Program on the Exchange of weather RAdar information (OPERA) ground-radar re...

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Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Deshpande, Atharva, Gopalan, Kaushik, Shah, Jeet, Simu, Hrishikesh
Format: Article
Language:English
Subjects:
Algorithms
Generative adversarial networks
Image enhancement
Infrared imagery
Infrared radar
Machine learning
Meteorological radar
Optical flow (image analysis)
Radar data
Radar imaging
Radiance
Rainfall
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Summary:This study explores the application of deep learning for rainfall prediction, leveraging the Spinning Enhanced Visible and Infrared Imager (SEVIRI) High rate information transmission (HRIT) data as input and the Operational Program on the Exchange of weather RAdar information (OPERA) ground-radar reflectivity data as ground truth. We use the mean of 4 InfraRed frequency channels as the input. The radiance images are forecasted up to 4 hours into the future using a dense optical flow algorithm. A conditional generative adversarial network (GAN) model is employed to transform the predicted radiance images into rainfall images which are aggregated over the 4 hour forecast period to generate cumulative rainfall values. This model scored a value of approximately 7.5 as the Continuous Ranked Probability Score (CRPS) in the Weather4Cast 2024 competition and placed 1st on the core challenge leaderboard.
ISSN:2331-8422