Forecast Performance During Low-level jet Regimes for the WFIP Southern Study Area

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Tuesday, 6 January 2015: 3:30 PM
224B (Phoenix Convention Center - West and North Buildings)
Jeff Freedman, Atmospheric Sciences Research Center, Albany, NY; and J. Zack, J. Manobianco, and E. J. Natenberg

The Low level Jet (LLJ) is a phenomenon that has been investigated by wind energy interests for nearly 40 years LLJs occur regularly throughout the year in the southern Great Plains, coincident with the greatest concentration of wind farms in the U.S. and one of the two regions selected for study under the first Department of Energy (DOE) sponsored Wind Forecast Improvement Project (WFIP). In the Electric Reliability Council of Texas (ERCOT) domain, LLJs can result in hourly capacity factors exceeding 80% in aggregated wind farm power production. Moreover, as the height of the LLJ wind speed maximum varies between 50 m and 400 m, but typically occurs at about 200 m, LLJs present a special concern for wind energy interests and a short-term forecasting challenge given the large vertical shear (upwards of 8 m s-1 per 100 m) that can occur across the turbine rotor plane. For example, model forecast vertical displacement by just a few 10s of meters, or mis-timing of onset or dissipation can lead to large errors in forecast power production. Thus, the forecasting of LLJs was a priority concern in model enhancements and the strategic deployment of additional remote sensing instrumentation during the WFIP campaign.

There was a marked improvement in forecasting the amplitude and phase of the LLJ by the WFIP SSA optimized forecast ensemble, as compared with the baseline forecast system pre-WFIP. However, the individual model member raw forecasts were often significantly in error regarding the amplitude and phase of the diurnal wind speed cycle, indicating that statistical post-processing and bias correction were the main drivers behind forecast improvement. This highlights a continuing issue models have with capturing the temporal and spatial distributions of LLJs. Here, we use observations from a network of remote sensing instruments deployed during WFIP (and assimilated into the modeling system) to show how individual members and the ensemble forecast system performed, with particular emphasis on the LLJ regime as it influenced wind power production.