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Explicit Ensemble Prediction of Hail in 19 May 2013 Oklahoma City Thunderstorms and Analysis of Hail Growth Processes with Several Multimoment Microphysics Schemes



Details

  • Journal Title:
    Monthly Weather Review
  • Personal Author:
  • NOAA Program & Office:
    NWS (National Weather Service)
  • Description:
    Hail forecast evaluations provide important insight into microphysical treatment of rimed ice. In this study we evaluate explicit 0–90-min EnKF-based storm-scale (500-m horizontal grid spacing) hail forecasts for a severe weather event that occurred in Oklahoma on 19 May 2013. Forecast ensembles are run using three different bulk microphysics (MP) schemes: the Milbrandt–Yau double-moment scheme (MY2), the Milbrandt–Yau triple-moment scheme (MY3), and the NSSL variable density-rimed ice double-moment scheme (NSSL). Output from a hydrometeor classification algorithm is used to verify surface hail size forecasts. All three schemes produce forecasts that predict the coverage of severe surface hail with moderate to high skill, but exhibit less skill at predicting significant severe hail coverage. A microphysical budget analysis is conducted to better understand hail growth processes in all three schemes. The NSSL scheme uses two-variable density-rimed ice categories to create large hailstones from dense, wet growth graupel particles; however, it is noted the scheme underestimates the coverage of significant severe hail. Both the MY2 and MY3 schemes produce many small hailstones aloft from unrimed, frozen raindrops; in the melting layer, hailstones become much larger than observations because of the excessive accretion of water. The results of this work highlight the importance of using a MP scheme that realistically models microphysical processes.
  • Keywords:
  • Source:
    Monthly Weather Review, 147(4), 1193-1213
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA10NWS4680001
  • Rights Information:
    Other
  • Compliance:
    CHORUS
  • Main Document Checksum:
    urn:sha256:c0681963a0db41900a62ac547a739b8436c73832234940a26f0d20058c769546
  • Download URL:
  • File Type:
    Filetype[PDF - 3.80 MB ]
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