Assessing the Skill of Updated Precipitation-Type Diagnostics for the Rapid Refresh with mPING

Burg, Tomer; Elmore, Kimberly L.; Grams, Heather M.

doi:10.1175/WAF-D-16-0132.1

i

Assessing the Skill of Updated Precipitation-Type Diagnostics for the Rapid Refresh with mPING

2017
By Burg, Tomer ; Elmore, Kimberly L. ; Grams, Heather M.

Details

Journal Title:

Weather and Forecasting
Personal Author:

Burg, Tomer ; Elmore, Kimberly L. ; Grams, Heather M.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)
Description:

Previous work has shown that the Rapid Refresh (RAP) model severely underrepresents 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 overall performance and the performance of the individual precipitation types of the ESRL-RAP. Verification was conducted using the mobile Phenomena Identification Near the Ground (mPING) project. The overall Gerrity skill score (GSS) for the ESRL-RAP was improved relative to the NCEP-RAP at a 3-h lead time but degraded with increasing lead time; the difference is significant at p < 0.05. Whether this difference is practically significant for users is unknown. Some improvement was found in the bias and skill scores of ice pellets and snow in the ESRL-RAP, although the model continues to underrepresent 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.
Keywords:

Model Comparison Model Evaluation/performance Model Comparison Model Evaluation/Performance
Source:

Weather and Forecasting, 32(2), 725-732
DOI:

https://doi.org/10.1175/WAF-D-16-0132.1
Document Type:

Journal Article
Funding:

Grant no. NA11OAR4320072
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

urn:sha256:5710e9c8069440951c0a7bccc9f633d08644632457ca396da5201e003977c4b4
Download URL:

https://repository.library.noaa.gov/view/noaa/32310/noaa_32310_DS1.pdf
File Type:

[PDF - 1.31 MB ]

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