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Assessing RRFS versus HRRR in Predicting Widespread Convective Systems over the Eastern CONUS



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  • Journal Title:
    Weather and Forecasting
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.
  • Source:
    Weather and Forecasting, 39(1), 121-140
  • DOI:
  • ISSN:
    0882-8156 ; 1520-0434
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha256:2a7ace37dccb3a20327e4fde48615d26d8c1413c7da74f7c757dd120172c747b
  • Download URL:
  • File Type:
    Filetype[PDF - 10.94 MB ]
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