Wednesday, 25 January 2017
4E (Washington State Convention Center )
The wind energy industry needs accurate forecasts of wind speeds at hub height and in the rotor layer in order to accurately predict power output from a wind farm. These forecasts aid the industry in deciding if there will be enough power to meet the energy need or if back up power sources will be necessary and to site future wind farms. Current numerical weather prediction (NWP) models struggle to accurately predict low level winds such as those at hub height, partially because of systematic biases within the models. These systematic errors are addressed through this study with statistical post-processing techniques such as Model Output Statistics (MOS). Additionally, ensemble-based statistical techniques are employed to take advantage of the spread of solutions produced by the ensemble members and compared with the MOS approach. These techniques include Bayesian Model Averaging, Ensemble MOS, and Analog Ensemble. This study uses reforecasts from the Weather Research and Forecasting (WRF-ARW) model version 3.5.1 and observations from SODAR instruments to examine the skill added by corrected forecasts from the post-processing techniques tested here. Both deterministic MOS and each of the ensemble post-processing techniques are developed for winds throughout the layer observed by SODAR (roughly surface to 300 m). Results of this study show the degree of improvement each technique provided to the raw WRF forecasts. Each of these applied post-processing techniques are expected to translate directly into improved low-level wind prediction within the real-time Texas Tech University prediction system.
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