ASSESSING THE SKILL OF UPDATED PRECIPITATION TYPE DIAGNOSTICS FOR RAPID REFRESH WITH mPING

Previous work shows that the Rapid Refresh (RAP) model severely under-represents ice pellets in its grid, with a skill near zero and a very low bias. An ice pellet diagnostic upgrade was devised at the Earth System Research Laboratory (ESRL) to resolve this issue. Parallel runs of the experimental ESRL-RAP with the fix and the operational NCEP-RAP without the fix provide an opportunity to assess whether this upgrade has improved the performance of the ESRL-RAP, both for the models overall and for individual precipitation types, using the meteorological Phenomena Identification Near the Ground (mPING) project as verification. The overall Gerrity Skill Score (GSS) for the ESRL-RAP is improved relative to the NCEP-RAP at 3 hour lead time but degrades with increasing lead time, a difference which is statistically significant but may not have much practical significance. Some improvement was found in the bias and skill scores of ice pellets and snow in the ESRL-RAP, although the model continues to under-represent ice pellets, while rain and freezing rain were generally the same or slightly worse with the fix. The ESRL-RAP was also found to depict a more realistic spatial distribution of precipitation types in transition zones involving ice pellets and freezing rain.