Understanding Model Sensitivity and Uncertainty—An Application of Uncertainty Quantification to Wind Energy

Wind resource characterization and short-term wind power forecasts make extensive use of models such as the Weather Research and Forecasting (WRF) model. Because of the finite nature of the model grid, parameterizations must be used to represent processes that are sub-grid scale, such as those associated with boundary-layer turbulence and the surface layer. Relatively few published studies have attempted to rigorously define the uncertainty in the forecast wind speed, wind shear across the rotor diameter, and wind power associated with the assumed constants applied in the boundary layer and surface layer parameterizations. Likewise, the design of most field studies with the goal of improving boundary-layer parameterizations have been based on the intuition of the investigator, rather than an explicit analysis of the causes of uncertainty within the parameterization. In this study we use uncertainty quantification (UQ) techniques to address these shortcomings and provide guidance for the design of the anticipated second US Department of Energy Wind Forecast Improvement (WFIP) study. In this presentation, UQ analysis will be used to document the parametric sensitivity and uncertainty in hub-height wind, wind shear across the rotor diameter, and wind power for WRF simulations completed using the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization and the recently revised Mesoscale Model 5 (MM5) surface layer scheme. Suggestions for the instrument deployment, based on the UQ results, will be made in the context of WFIP.