Evaluation of the 3-km High Resolution Rapid Refresh (HRRR) as Nowcast Guidance

Curtis Alexander, Steve Weygandt, Stan Benjamin, David Dowell, Eric James, Patrick Hofmann, Tanya Smirnova, Ming Hu, and John Brown – CIRES and NOAA Earth System Research Laboratory, Boulder, CO

The High Resolution Rapid Refresh (HRRR) is a 3-km, convection permitting model, run hourly in real-time at the Global System Division (GSD) of the NOAA Earth System Research Laboratory (ESRL). The WRF-ARW-based HRRR is run out to fifteen hours over a domain covering the entire coterminous United States (CONUS), using initial and boundary conditions from an hourly-cycled 13-km mesoscale model, formerly the Rapid Update Cycle (RUC), and currently the Rapid Refresh (RR). Both the RR and RUC include a diabatic digital filter-based radar reflectivity data assimilation procedure to improve specification of the divergent component of horizontal wind in areas of precipitation.

During the past year additional resources have been implemented to further improve the HRRR forecast guidance. These additional resources include both an in-house multi-scale verification system and a parallel (shadow) HRRR model whereby impacts of model changes are assessed in both real-time and thru retrospective forecasts.

In this presentation, we will describe the changes implemented in the real-time HRRR model for 2011, including the adoption of the RR as a parent mesoscale model, and adjustments to the numerical diffusion in the HRRR. HRRR forecast evaluation from 2011 will include both the cool-season and warm-season and include verification of forecast fields such as ceiling, visibility, surface winds, precipitation and reflectivity (convection). Evaluation of model changes and forecasts will include regional, diurnal (valid-time), lead-time, and multi-scale stratification of verification statistics. Case studies highlighting comparisons of the HRRR with the coarser-resolution parent model (RUC and/or RR) will be presented along with a quantification of the impact of radar data assimilation in HRRR short-term forecasts.

Time-lagged ensemble HRRR forecasts have been used to generate probabilistic forecasts of convection, known as the HRRR Convective Probabilistic Forecast (HCPF), which compliment the deterministic information from individual HRRR model runs. Examples of these probabilistic forecasts will be shown.