Improving cold season precipitation prediction by the nested CWRF-CFS system

Authors

  • Xing Yuan,

    1. Division of Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
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  • Xin-Zhong Liang

    1. Division of Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
    2. Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
    3. Now at Department of Atmospheric and Oceanic Science and Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA.
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Abstract

[1] This study uses the newly developed Climate extension of Weather Research and Forecasting (CWRF) model nested in the National Centers for Environmental Prediction (NCEP) operational Climate Forecast System (CFS) to improve interannual prediction of cold season precipitation over the United States. An ensemble of 5 retrospective forecasts for 27-cold seasons (December–April) during 1982–2008 has been conducted to assess the predictive skill. The CWRF downscaling reduces CFS forecast errors of seasonal mean precipitation by 22% on average, increases the equitable threat score by 0.08–0.15, and produces greater skill for heavy rainfall events. The CWRF simulates more accurate number of rainy days than the CFS over the northern and western U.S. due to the refined representation of orographic effect, shallow convection, and terrestrial hydrology. The CWRF also more realistically captures the broad region of extreme rainfall over the Gulf States and maximum dry spell length along the Great Plains, as well as their contrasts between El Niño and La Niña events. The results demonstrate the significant advantage of the CWRF downscaling for regional precipitation prediction, especially during years with weak planetary anomalies.

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