Spatiotemporal rainfall rorecasting through GAN-based deep learning with enhanced temporal coherence

Accurate short-term rainfall forecasting based on radar is becoming increasingly important as extreme weather events occur more frequently on Earth. However, accurately predicting rainfall remains a challenge for researchers due to the complex and dynamic nature of the atmospheric system, making it difficult to accurately predict. To address this, we introduce GAN Analysis, a deep learning model that combines spatial and temporal prediction networks with a generative adversarial framework. This module uses multiple decoding modules connected with neural network layers to better capture the temporal structure in radar data so that the model can retain temporal information over a long period of time. Furthermore, we also use an improved loss function to improve the clarity and accuracy of the predicted rainfall map so that the model can learn well. Experimental results show that the model achieves good performance especially under high-intensity rainfall conditions.