Weather Forecasting

Weather forecasting aims to accurately predict atmospheric conditions, crucial for numerous societal applications from daily planning to disaster mitigation. Current research emphasizes improving forecast accuracy and efficiency through advanced deep learning models, including transformers, convolutional neural networks, and recurrent neural networks, often incorporating techniques like masked autoencoding and diffusion models to handle complex spatiotemporal dependencies and quantify uncertainty. These advancements are leading to more precise and reliable forecasts across various timescales and spatial resolutions, impacting diverse fields such as agriculture, energy management, and public safety.