Hybrid ensemble forecasting combining physics‐based and machine‐learning predictions through spectral nudging

We present the first application of spectral nudging in a probabilistic ensemble forecasting framework, combining the physics‐based European Centre for Medium‐Range Weather Forecasts' Integrated Forecasting System ensemble (IFS‐ENS) with forecasts from the probabilistic machine‐learned Artificial Intelligence Forecasting System Ensemble. Large scales of virtual temperature and vorticity are relaxed toward the machine‐learned forecasts, while mesoscale structures remain governed by the physics‐based model. This hybrid ensemble shows substantial improvements in large‐scale forecast skill, with gains in predictive skill extended by up to 2 days in the Tropics and by approximately half a day in the extratropics relative to IFS‐ENS. Despite nudging being applied only to upper‐air fields, improvements are also found in several near‐surface parameters. Tropical cyclone track forecasts improve significantly, consistent with improved representation of the large‐scale steering flow, without degrading storm intensity or ensemble spread. These results demonstrate that spectral nudging can be successfully extended to ensemble prediction and provide an attractive pathway for combining machine‐learned and physics‐based weather prediction systems.