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GEFS Precipitation Forecasts and the Implications of Statistical Downscaling over the Western United States

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  • Journal Title:
    Weather and Forecasting
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Contemporary operational medium-range ensemble modeling systems produce quantitative precipitation forecasts (QPFs) that provide guidance for weather forecasters, yet lack sufficient resolution to adequately resolve orographic influences on precipitation. In this study, cool-season (October-March) Global Ensemble Forecast System (GEFS) QPFs are verified using daily (24 h) Snow Telemetry (SNOTEL) observations over the western United States, which tend to be located at upper elevations where the orographic enhancement of precipitation is pronounced. Results indicate widespread dry biases, which reflect the infrequent production of larger 24-h precipitation events (greater than or similar to 22.9mm in Pacific ranges and greater than or similar to 10.2 mm in the interior ranges) compared with observed. Performance metrics, such as equitable threat score (ETS), hit rate, and false alarm ratio, generally worsen from the coast toward the interior. Probabilistic QPFs exhibit low reliability, and the ensemble spread captures only similar to 30% of upper-quartile events at day 5. In an effort to improve QPFs without exacerbating computing demands, statistical downscaling is explored based on high-resolution climatological precipitation analyses from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM), an approach frequently used by operational forecasters. Such downscaling improves model biases, ETSs, and hit rates. However, 47% of downscaled QPFs for upper-quartile events are false alarms at day 1, and the ensemble spread captures only 56% of the upper-quartile events at day 5. These results should help forecasters and hydrologists understand the capabilities and limitations of GEFS forecasts and statistical downscaling over the western United States and other regions of complex terrain.
  • Source:
    Weather and Forecasting, 32(3), 1007-1028.
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA13NWS4680003
  • Rights Information:
    Other
  • Compliance:
    Submitted
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