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

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Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
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) were 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 and day-to-day wind 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 over a project domain centered 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 the month-long period.