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dc.contributor.authorEric P. James
dc.contributor.authorCurtis R. Alexander
dc.contributor.authorDavid C. Dowell
dc.contributor.authorStephen S. Weygandt
dc.contributor.authorStanley G. Benjamin
dc.contributor.authorGeoffrey S. Manikin
dc.contributor.authorJohn M. Brown
dc.contributor.authorJoseph B. Olson
dc.contributor.authorMing Hu
dc.contributor.authorTatiana G. Smirnova
dc.contributor.authorTerra Ladwig
dc.contributor.authorJaymes S. Kenyon
dc.contributor.authorDavid D. Turner
dc.date.accessioned2023-04-12T18:53:09Z
dc.date.available2023-04-12T18:53:09Z
dc.date.copyright2022/08/01
dc.date.issued2022
dc.identifier.otherWAF-D-21-0130.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290416
dc.description.abstractThe High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.
dc.publisherAmerican Meteorological Society
dc.titleThe High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part II: Forecast Performance
dc.typeJournal Paper
dc.journal.volume37
dc.journal.issue8
dc.journal.titleWeather and Forecasting
dc.identifier.doi10.1175/WAF-D-21-0130.1
dc.journal.fristpage1397
dc.journal.lastpage1417
dc.page1397–1417
dc.treeWeather and Forecasting:;2022:;volume( 037 ):;issue: 008
dc.contenttypeFulltext


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