Have you ever checked the weather forecast for the weekend and made plans, only to be surprised by rain? Then you may have wondered about the reliability of weather forecasts.
Predicting the weather is hard. The atmosphere is a chaotic system with many unknowns. Traditional weather models require complex numerical methods to predict the future state of the atmosphere.
Over the past five decades, meteorological services have built up a weather archive of reanalysis data. Driven by satellite observations, ground-based weather stations, and numerical weather prediction, this archive represents the past weather to the best of our knowledge.
The AI revolution in weather forecasting
Five years ago, the AI revolution in weather prediction began. Using reanalysis weather archives from the past 50 years, researchers developed AI models to forecast weather.
These models learn weather patterns from training data. In inference, they take the current state of the atmosphere and output the future state. This can be applied iteratively, allowing the model to forecast for several days.
AI weather models typically operate at a spatial resolution of 25 kilometers, which is the spatial resolution of the training data. In contrast, high-resolution numerical weather models operate at a spatial resolution of 9 kilometers.
For medium-range AI weather forecasts, up to 10 days ahead, the typical temporal resolution is six hours.
Current AI weather models
The last two years have seen an explosion of AI weather models. The WeatherBench project comprehensively evaluates them. We focus here on those models that have been evaluated in an operational setting, i.e., initialized with the same data as a numerical weather forecast.