Abstract
Ensemble forecasting is essential for quantifying forecast uncertainty and providing probabilistic weather predictions. However, the substantial computational demands of current global ensemble prediction systems based on conventional models limit ensemble sizes, hindering the representation of diverse weather scenarios. Recent advances in machine learning (ML) have greatly reduced computational costs and improved deterministic forecasting. Nonetheless, applying ML to ensemble forecasting poses challenges in addressing uncertainties in initial conditions and models, which are the major sources of forecasting errors. To address these challenges, we introduce FuXi-ENS, an advanced ML model that generates 6-hourly global ensemble weather forecasts up to 15 days ahead at a spatial resolution of 0.25°. Using a variational autoencoder framework, FuXi-ENS optimizes a loss function that combines the continuous ranked probability score (CRPS) with the Kullback-Leibler divergence, enabling flow-dependent perturbations. Comprehensive evaluations demonstrate that FuXi-ENS outperforms the ECMWF ensemble in key forecast metrics such as CRPS and Brier score.