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Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations



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  • Journal Title:
    Atmosphere
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.
  • Keywords:
  • Source:
    Atmosphere, 12(12), 1630
  • DOI:
  • ISSN:
    2073-4433
  • Format:
    PDF
  • Publisher:
    MDPI AG
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA17OAR4590187
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha256:2edcfe3525bb3530401c4cad27bf4e616a9a7b9b17a0fe911f063f3b03c47abd
  • Download URL:
  • File Type:
    Filetype[PDF - 8.48 MB ]
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