92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012
Meteorological Prediction in Complex Terrain for Wind Energy Applications Using a State-of-the-Art Ensemble Data Assimilation System
Hall E (New Orleans Convention Center )
Paul T. Quelet, Siemens Energy, Inc., Boulder, CO; and P. C. Fowler

Siemens Energy is testing a meteorological prediction system for wind energy applications. To compare with observational data, Siemens has installed a 2.3 MW test turbine at the U.S. National Renewable Energy Laboratory's (NREL) National Wind Technology Center (NWTC). Located just south of Boulder, Colorado, the NWTC is located on a small plateau in the lee of the Rocky Mountains, flanked to the east by gently sloping rolling terrain. Moreover, the NWTC is ideally situated for extreme wind testing because of channeled wind flows through the nearby Eldorado canyon. As a result, extreme winds in a complex terrain environment are commonly observed at the Siemens Test Turbine. This presents a significant challenge for numerical weather prediction to predict wind speed and direction accurately.

This research calculates the improvement of meteorological prediction for the NWTC by applying a state-of-the-art data assimilation method. The Weather Research and Forecasting (WRF) model is the basis numerical weather prediction model. This study utilizes the Data Assimilation Research Testbed (DART) (Anderson et al. 2009) system with an Ensemble Kalman Filter (EnKF) algorithm at its core. The combination WRF-DART ingests thousands of observations during a single simulation; prior model error and observation error are estimated through the EnKF algorithm so that the ensemble mean, spread, and error structures can be calculated. Updated ensembles serve as the starting point for subsequent meteorological predictions. Additionally, DART can ingest unique remote sensing observations. Predictions for the NWTC are performed in this research using specialized instrumentation: a Windcube LIDAR, a Triton SODAR, and multiple tall meteorological tower measurements. Prediction sensitivity to data assimilation of additional specialized observations is demonstrated.

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