IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.

An analytical wind turbine wake model is proposed to predict the wind velocity distribution for all distances downwind of a wind turbine, including the near-wake. This wake model augments the Jensen model and subsequent derivations thereof, and is a direct generalization of that recently proposed by Bastankhah and Porté-Agel. The model is derived by applying conservation of mass and momentum in the context of actuator disk theory, and assuming a distribution of the double-Gaussian type for the velocity deficit in the wake. The physical solutions are obtained by appropriate mixing of the waked-and freestream velocity deficit solutions, reflecting the fact that only a portion of the fluid particles passing through the rotor disk will interact with a blade.

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Conventional MCP (Measure Correlate Predict) techniques often employ linear regression to model the relationship between wind speeds at two sites and use this model to predict the long term wind regime at one site from the long term wind regime at the other. In considering sites whose wind speeds are Weibull distributed it can be demonstrated analytically that linear relationships do not, in fact, hold. The general relationship is found to be of the form y = mx α + c where α is the ratio of the shape parameters of the Weibull distributions. Linearity prevails only when the shape parameters are equal. This is seen to have implications for MCP analysis.

This paper presents a three‐dimensional numerical model for horizontal axis wind turbines, capable of simulating both the dynamic response of the turbine to changing flow conditions, and the full wake generated by the turbine. The turbine model is coupled to computational fluid dynamics software using Large Eddy Simulation to solve for unsteady flow conditions, with the Smagorinsky method handling sub‐grid turbulence. Wind tunnel simulations are compared with experimental data, and then a simulation of a real turbine in complex terrain is compared with LIDAR data from the same site, in both cases agreeing favourably. Copyright © 2011 John Wiley & Sons, Ltd.