Weather Forecasting
Overview
Weather forecasting estimates how atmospheric conditions will evolve to support decisions ranging from daily planning to severe-weather warnings. Numerical weather prediction solves physical equations, while machine-learning approaches use transformers, graph networks, state-space models, and generative methods trained on reanalysis, satellite, radar, or station observations; data assimilation combines observations with model states. Current work also develops probabilistic ensembles, short-range radar nowcasts, regional downscaling, and forecasts for events such as tropical cyclones and heavy precipitation. A central challenge is producing reliable long-range and extreme-event forecasts while preserving physical consistency, representing local detail, and validating performance against real observations rather than reanalysis alone.
Latest papers
178 papers34 papers in the last four weeks, up 55% on the four weeks before. 0.2% of all new papers.