In addition to the development of the deterministic HRRR, two efforts to extend the hourly updating forecast capability into ensemble prediction are underway. The first of these efforts leverages the HRRR forecasts in a cost-effective time-lagged ensemble (HRRR-TLE) to estimate hourly-updating likelihood probabilities of various weather hazards associated with severe thunderstorms and heavy precipitation over the CONUS out to 24 hours. We will highlight the post-processing techniques used to produce these HRRR-TLE products including quantile-mapping and bias correction and temporal-spatial filtering to produce statistically reliable forecast probabilities.
The second effort involves the development of a more expensive HRRR 3-km 40-member data assimilation and forecast ensemble (HRRRE) for a limited sub-CONUS domain. Ensemble spread is produced through initial condition perturbations and hourly-cycling of assimilated conventional observations using a GSI-based Ensemble Kalman Filter system. The hourly data assimilation uses 40 3-km HRRR members with ensemble forecasts of 3-18 members. We will show comparisons of deterministic HRRR, HRRR-TLE and HRRRE forecasts with both case-studies from real-time forecasts and longer term statistics to highlight both the strengths of each system and avenues for future improvements including consolidation of all three efforts.