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Evaluation of the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA) for use in wind energy potential assessment in the Great Basin of Nevada

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Wednesday, 26 January 2011
Evaluation of the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA) for use in wind energy potential assessment in the Great Basin of Nevada
K.C. King, DRI, Reno, NV; and M. L. Kaplan, A. Joros, M. Liddle, and E. Uher

In order to further assess the wind energy potential for Nevada, the accuracy of a computational meteorological model, the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA), was evaluated by comparing simulation results with data collected from a wind monitoring tower near Tonopah, NV. The state of Nevada is characterized by high mountains and low-lying valleys, therefore, in order to determine the wind potential for the state, meteorological models that predict the wind must be able to accurately represent and account for terrain features and simulate topographic forcing with accuracy. Topographic forcing has a dominant role in the development and modification of mesoscale flows in regions of complex terrain, like Tonopah, especially at the level of wind turbine blade heights (~80 m). Additionally, model factors such as horizontal resolution, terrain database resolution, model physics, time of initialization, stability regime, and source of initial conditions may all affect the ability of a mesoscale model to forecast winds correctly. Unlike other mesoscale atmospheric models, OMEGA incorporates an unstructured triangular horizontal grid which allows for increased flexibility and accuracy in characterizing areas of complex terrain.

The observational tower used for comparison was located at Stone Cabin, Nevada. The tower had both sonic anemometers and cup anemometers installed at heights of 40 m, 60 m, and 80 m above the surface. During a previous experiment, tower data were collected for the period February 9 through March 10, 2007 and compared to model simulations using the MM5 and WRF models at a number of varying horizontal resolutions. In this previous research, neither the MM5 nor the WRF showed a significant improvement in ability to forecast wind speed with increasing horizontal grid resolution.

The present research evaluated the ability of OMEGA to reproduce point winds as compared to the observational data from the Stone Cabin Tower at heights of 40 m, 60 m, and 80 m. Model sensitivity to horizontal grid resolution, initial conditions, and terrain dataset resolution were tested. OMEGA was run over five different horizontal grid resolutions with minimum horizontal edge lengths of: 18 km, 6 km, 2 km, 666 m, and 222 m. The 666 m and 222 m minimum grid resolution simulations were also run with both a 90 m and a 1 km resolution terrain database to determine the sensitivity to terrain features. For each resolution, the model was initialized using both the Global Forecasting System (GFS) and North American Regional Reanalysis (NARR) to determine model sensitivity to initial conditions. For both the NARR and GFS initializations, the model was started at both 0000 UTC and 1200 UTC to determine the effect of start time and stability regime on the performance of the model. An additional intensive study into the model's performance was also conducted by a detailed evaluation of model results during two separate 24-hour periods, the first a period where the model performed well and the second a period where the model performed poorly to determine which atmospheric factors most affect the predictive ability of the OMEGA model.

Each 30-day model run was then analyzed using statistical analysis to determine how accurately the model generated winds compared to the observed winds. The statistical results were then compared with the results from the MM5 and WRF simulations to determine the most appropriate model for wind energy potential studies in complex terrain. In addition, the studies into initial conditions, grid resolution, stability regimes, and terrain database resolution provide guidance into the best practices for wind modeling and forecasting.