Generation of Ensemble-Based Hazardous Weather Guidance Products from Rapidly Updating Models: The HRRR Convective Probabilistic Forecast (HCPF) and Related Post-Processing Work
One factor in designing optimal model post-processing algorithms is deciding what portion of the processing to include during the model integration vs. in separate post-processing procedures that operate on standard model output grids. Certain diagnostics benefit greatly from information that is not routinely output in the standard model grids (internal model fields or field sampling at every model time-step), which has driven a trend to include more of these diagnostic calculations in the model itself.
In this talk we will present recent results on the performance of the HCPF and plans to optimize it, as well other model post-processing diagnostics being computed in the HRRR and RAP. These include various convective indicators (updraft helicity, hail threat, lightning risk) and renewable energy guidance fields (wind and solar). Included in these is a wind speed change diagnostic, which provides a very good indicator of gust fronts and other boundaries. We will also discuss issues related to the generation of optimal probabilistic products from ensemble guidance (both time-lagged and tradition) and constructing probability distribution functions from small ensembles.