Four-dimensional variational assimilation of multi-time wind profile observations: the impact and potential applications to wind power meteorology

Accurate wind forecasts are the important component for predicting wind energy production. Therefore, it is essential to accurately specify the wind conditions in numerical forecasting models. This talk presents recent results in assimilating multi-time wind profile observations from a single lidar wind profiler with a four-dimensional variational (4DVAR) data assimilation method. It is shown that the assimilation of high temporal and vertical resolution lidar wind profiles has a significant influence on the spatial distribution of the wind analysis and the numerical simulation (forecast) of a convective system. Results demonstrate a great potential of 4DVAR data assimilation method in improving representation of the spatial distribution of wind fields thought assimilating multi-time wind profile data from a single wind profiler. The potential applications of the method to wind power meteorology will be discussed in the presentation.