Enhancing precipitation prediction accuracy with Transformer-GAN hybrid models

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 finer resolution (8km) specifically for the Beijing region in China. Our proposed model enhances the accuracy of precipitation forecasts by leveraging a hybrid architecture that combines the strengths of Transformers and Generative Adversarial Networks (GANs). The model is particularly effective in downscaling lowresolution meteorological data to high-resolution precipitation forecasts. Comparative analyses with existing models like CorrectorGAN and ResDeepD indicate a significant improvement in forecast accuracy, validating the efficacy of our novel approach.