4.2 Wind Forecast Uncertainty Derived from a HRRR Ensemble

Tuesday, 24 January 2017: 1:45 PM
606 (Washington State Convention Center )
Wayne M. Angevine, CIRES, University of Colorado, and NOAA/ESRL, Boulder, CO; and I. Jankov, J. Berner, J. Beck, H. Jiang, G. Grell, J. B. Olson, T. Smirnova, J. M. Brown, J. Wilczak, L. Bianco, R. M. Banta, Y. Pichugina, A. Brewer, M. Marquis, and S. Benjamin

An ensemble using the High Resolution Rapid Refresh model is examined for information about uncertainty in wind forecasts for wind energy purposes.  Data from the second Wind Forecast Improvement Project (WFIP2) provides ground truth for the evaluation of several case studies.  Most global and regional numerical weather prediction (NWP) ensemble systems are under-dispersive, producing unreliable and overconfident ensemble forecasts. With growing evidence that initial-condition uncertainties are not sufficient to entirely explain forecast uncertainty, the role of model uncertainty is receiving increasing attention.  Here we address the model uncertainty by using stochastic physics.  Stochastic approaches including stochastically perturbed parameters within individual physics schemes, Stochastic Kinetic Energy Backscatter (SKEB), Stochastic Perturbation of Physics Tendencies and combinations of these approaches are explored.
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