Revisiting Sensitivity to Horizontal Grid Spacing in Convection-Allowing Models over the Central and Eastern United States
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Revisiting Sensitivity to Horizontal Grid Spacing in Convection-Allowing Models over the Central and Eastern United States

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  • Journal Title:
    Monthly Weather Review
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    Hourly accumulated precipitation forecasts from deterministic convection-allowing numerical weather prediction models with 3- and 1-km horizontal grid spacing were evaluated over 497 forecasts between 2010 and 2017 over the central and eastern conterminous United States (CONUS). While precipitation biases varied geographically and seasonally, 1-km model climatologies of precipitation generally aligned better with those observed than 3-km climatologies. Additionally, during the cool season and spring, when large-scale forcing was strong and precipitation entities were large, 1-km forecasts were more skillful than 3-km forecasts, particularly over southern portions of the CONUS where instability was greatest. Conversely, during summertime, when synoptic-scale forcing was weak and precipitation entities were small, 3- and 1-km forecasts had similar skill. These collective results differ substantially from previous work finding 4-km forecasts had comparable springtime precipitation forecast skill as 1- or 2-km forecasts over the central–eastern CONUS. Additional analyses and experiments suggest the greater benefits of 1-km forecasts documented here could be related to higher-quality initial conditions than in prior studies. However, further research is needed to confirm this hypothesis.
  • Keywords:
  • Source:
    Monthly Weather Review, 147(12), 4411-4435
  • DOI:
  • ISSN:
    0027-0644;1520-0493;
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA17OAR4590182
  • Rights Information:
    Other
  • Compliance:
    Library
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  • Download URL:
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