Enhancing precipitation prediction accuracy with Transformer-GAN hybrid models
Hailong Shu,
Huichuang Guo,
Zhen Song
et al.
Abstract:Accurate precipitation prediction is crucial for a range of sectors, including agriculture, water resource management, and disaster preparedness. Traditional meteorological models often struggle to capture the complex spatial and temporal patterns associated with precipitation events. To address this gap, this study introduces a groundbreaking approach that combines Transformer and Generative Adversarial Network (GAN) technologies. The objective is to downscale lowresolution (25km) precipitation data to a fine… Show more
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