Weather Benchmark

Weather benchmark research aims to evaluate and improve the accuracy of data-driven weather forecasting models, focusing on both short-term (nowcasting) and medium-to-long-term predictions. Current efforts utilize various deep learning architectures, including transformer models, convolutional neural networks (especially deformable CNNs), and generative diffusion models, often incorporating multimodal data (images, text) to enhance prediction capabilities. These advancements hold significant potential for improving the accuracy and reliability of weather forecasts, leading to better preparedness for severe weather events and more effective resource management in various sectors.