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Original Research Papers

The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performance results

Authors:

J. P. Hacker ,

Naval Postgraduate School, Monterey, CA, US
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S.-Y. Ha,

National Center for Atmospheric Research, Boulder, CO, US
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C. Snyder,

National Center for Atmospheric Research, Boulder, CO, US
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J. Berner,

National Center for Atmospheric Research, Boulder, CO, US
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F. A. Eckel,

National Weather Service Office of Science and Technology, Silver Spring, MD, US
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E. Kuchera,

Air Force Weather Agency, Bellevue, NE, US
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M. Pocernich,

National Center for Atmospheric Research, Boulder, CO, US
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S. Rugg,

Air Force Weather Agency, Bellevue, NE, US
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J. Schramm,

National Center for Atmospheric Research, Boulder, CO, US
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X. Wang

University of Oklahoma, Norman, OK, US
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Abstract

This work evaluates several techniques to account for mesoscale initial-condition (IC) and model uncertainty in a short-range ensemble prediction system based on the Weather Research and Forecast (WRF) model. A scientific description and verification of several candidate methods for implementation in the U.S. Air Force Weather Agency mesoscale ensemble is presented. Model perturbation methods tested include multiple parametrization suites, landsurface property perturbations, perturbations to parameters within physics schemes and stochastic ‘backscatter’ streamfunction perturbations. IC perturbations considered include perturbed observations in 10 independent WRF-3DVar cycles and the ensemble-transform Kalman filter (ETKF). A hybrid of ETKF (for IC perturbations) and WRF-3DVar (to update the ensemble mean) is also tested. Results show that all of the model and IC perturbation methods examined are more skilful than direct dynamical downscaling of the global ensemble. IC perturbations are most helpful during the first 12 h of the forecasts. Physical parametrization diversity appears critical for boundary-layer forecasts. In an effort to reduce system complexity by reducing the number of suites of physical parametrizations, a smaller set of parametrization suites was combined with perturbed parameters and stochastic backscatter, resulting in the most skilful and statistically consistent ensemble predictions.

How to Cite: Hacker, J.P., Ha, S.-Y., Snyder, C., Berner, J., Eckel, F.A., Kuchera, E., Pocernich, M., Rugg, S., Schramm, J. and Wang, X., 2011. The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performance results. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.625–641. DOI: http://doi.org/10.1111/j.1600-0870.2010.00497.x
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  Published on 01 Jan 2011
 Accepted on 1 Dec 2010            Submitted on 14 Apr 2010

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