As wind turbines have grown, new installation areas become possible. Placing wind turbines in forested landscapes introduce uncertainties to the wind resource estimation. Even though close-to-canopy processes have been studied intensively during the last thirty years, the focus has mostly been on exchange processes and the height span of the studies has been below the rotor of a modern wind turbine. This thesis contains analysis of new measurements from a 138 m high tower in a forested landscape. The previous knowledge of near-canopy processes is extended to the region above the roughness sublayer. It is shown that above the roughness sublayer, the surface layer behaves as over low vegetation, and Monin-Obukhov similarity is shown to hold for several variables. However, in stable stratification, effects that could be linked to the boundary layer depth are shown to be present in the measurements. These include wind turning with height, the behaviour of the turbulence length scale and the curvature of the wind profile. Two new analytical models are presented in the thesis. One is a flux-profile expression in the roughness sublayer, which allows for analytical integration of the wind gradient. The model suggests that the roughness-sublayer effect depends on stratification and that the aerodynamic roughness length changes with stability. A decrease of roughness length in stable stratification is confirmed with a new method to determine the roughness length using measurements from the 138 m tower. The other model determines the spectral tensor in stable stratification using analytical solution to the rapid distortion equations for stratified shear flow, with homogeneous stratification and shear. By using a formulation for the integration time of the distortions of an isotropic spectrum, a model is derived which provides the cross spectra of velocity and temperature at any two given points in space. Finally the existence of waves in the wind over forests is investigated and it is concluded that the Kelvin-Helmholtz instability can create waves which are coherent in time and exist over the entire height span of wind turbine rotors. Linear wave theory is shown to be able to explain certain features of the waves. / Vindforsk III, Wind power in forests / Vindforsk IV, Forest wind

Accurate forecasts of wind speed and power production are of great value for wind power producers. In Southwest Iceland, wind power installations are being planned by various entities. This study aims to create optimal wind speed and wind power production forecasts for wind power production in Southwest Iceland by applying statistical post-processing methods to a deterministic HARMONIE-AROME forecast at a single point in space. Three such methods were implemented for a 22 month-long set of forecast-observation samples in 1h resolution: Temporal Smoothing (TS), Observational Distributions on Discrete Intervals (ODDI - a relatively simple classification algorithm) and Quantile Regression Forest (QRF - a relatively complicated Machine Learning Algorithm). Wind power forecasts were derived directly from forecasts of wind speed using an idealized power curve. Four different metrics were given equal weight in the evaluation of the methods: Root Mean Square Error (RMSE), Miss Rate of the 95-percent forecast interval (MR95), Mean Median Forecast Interval Width (MMFIW - a metric to measure the forecast sharpness) and Continuous Ranked Probability Score (CRPS). Of the three methods, TS performed inadequately while ODDI and QRF performed significantly better, and similarly to each other. Both ODDI and QRF predict wind speed and power production slightly more accurately than deterministic AROME in terms of their Root Mean Square Error. In addition to an overall evaluation of all three methods, ODDI and QRF were evaluated conditionally. The results indicate that QRF performs significantly better  than ODDI at forecasting wind speed and wind power at wind speeds above 13 m/s. Else, no strong discrepancies were found between their conditional performance. The results of this study are limited by a relatively scarce data set and correspondingly short time series. The results indicate that applying statistical post-processing methods of varying complexity to deterministic wind speed forecasts is a viable approach to gaining a probabilistic insight into the wind power potential at a given location.