Evaluating the performance of a sodar gap-filling algorithm in short term wind forecasting

Wind energy industries around the world have witnessed a significant surge in recent years. However, to make wind a cost-effective and reliable alternative energy source, the industry will need to make significant scientific advancements in several arenas of wind power meteorology. As the future wind turbines are designed with increasingly higher hub-heights and larger rotor diameters, accurate measurements of lower boundary layer wind fields will become more important. Given the high construction, operation, and maintenance costs associated with tall-tower-based wind measurements, the wind energy community is exploring standalone, and inexpensive alternatives. Active ground-based remote sensing instruments like sound detection and ranging (sodar), radio detection and ranging (radar), and laser imaging detection and ranging (lidar) are gaining popularity for various wind resource assessment, inflow characterization, and short-term forecasting. Modern-day sodars are very portable and can measure vertical profiles of three-dimensional velocity components and turbulence characteristics with high spatial and temporal resolutions.

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.