Evaluating the performance of a sodar gap-filling algorithm in short term wind forecasting
Susceptibility to ambient background noise and associated sodar data degradation is a scientific research challenge. We recently developed an algorithm, based on statistical learning theory, for gap-filling the sodar data. Wind data from a collocated 200m tall meteorological tower were used to validate the results. The goal of the present study is to explore the performance of the algorithm in short-term wind forecasting using the sodar data collected during the Wind Forecast Improvement Project (WFIP) field campaign in Texas, USA. We will document the improvements, by incorporating 4- dimensional data assimilation (FDDA), in the performance of the Weather Research & Forecasting model (WRF) with and without the gap-filled sodar data. The wind speed spectra derived from these WRF forecasts will be compared to the one derived from sodar data. The effectiveness of a nonlinear time series approach in short-term wind prediction will also be evaluated and compared with the WRF model based prediction.