9.3 Evaluation of Wind Ramp Forecasts using Variational and Ensemble Data Assimilation over the WFIP Southern Study Region

Wednesday, 7 August 2013: 4:00 PM
Multnomah (DoubleTree by Hilton Portland)
Brian C. Ancell, Texas Tech University, Lubbock, TX

Accurate, high-resolution wind ramp forecasting is crucial to efficient wind power systems. Two of the primary goals of the DOE-NOAA Weather Forecast Improvement Project (WFIP) are to understand how wind ramp forecasts vary with regard to 1) different data assimilation systems, and 2) an enhanced observational network. This study examines the quality of wind ramp forecasts with the Weather Research and Forecasting (WRF) mesoscale model using two independent data assimilation systems: the Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) system, and the Data Assimilation Research Testbed (DART) ensemble Kalman filter (EnKF). 0-24hr forecasts of 10 wind ramp events at existing wind farms are verified against 80-meter meteorological tower data, and lower atmospheric winds are also verified against 80-meter tower data, surface observations, and radiosonde observations over a month-long period. These experiments are conducted at 12km and 3km grid spacing and include the southern WFIP project domain over Texas. Furthermore, the observational impacts of both surface mesonet observations, as well as sodar and profiler observations aloft deployed during the project, are assessed for both the ramp cases and month-long period.
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