Towards operational data-driven regional weather forecasting: extreme-event skill and near-real-time verification with HRRRCast
Improving convective-scale skill in data-driven regional weather models requires better representing rare high-impact events, training at native resolution, broadening variable output, and demonstrating performance in real-time settings. Here, we evaluate these challenges using HRRRCast, a data-driven convection-allowing emulator trained on the High-Resolution Rapid Refresh (HRRR) analysis data. HRRRCast combines native 3-km training, moderate sigmoid-weighted loss reweighting for reflectivity and precipitation, and broader system improvements including expansion of the training archive from 3 to 9 years (2016--2025), increased vertical resolution, expanded outputs, and architectural upgrades. The weighted loss improves high-threshold skill while preserving common-event behavior. Native-resolution training avoids aliasing from 6-km subsampling and substantially improves fractions skill score at convective scales, with gains that persist across pooling windows. HRRRCast has also been running in near-real-time mode for the past year. In seven months of independent observation-based verification, HRRRCast ensemble means improve several near-surface metrics relative to operational HRRR and maintain strong precipitation and reflectivity performance. Single-member reflectivity skill is strongest at 20--30 dBZ; at 40 dBZ, skill is near parity at 6-km pooling and better at pooling radii $\geq$30 km. Precipitation skill remains competitive through 1 in/h and extends to about 3 in/h in the best recent configuration. A focused comparison with HREF further shows useful low-cost probabilistic guidance for 3-h precipitation. At storm scale, HRRRCast exhibits a displacement--structure trade-off, with generally better storm placement than storm morphology.