This article focuses on computations of low frequency sound propagation from an off-shore wind farm. Two different methods for sound propagation calculations are combined with meteorological data for every 3 hours in the year 2010 to examine the varying noise levels at a reception point at 13 km distance. It is shown that sound propagation conditions play a vital role in the noise impact from the off-shore wind farm and ordinary assessment methods can become inaccurate at longer propagation distances over water. Therefore, this paper suggests that methodologies to calculate noise immission with realistic sound speed profiles need to be combined with meteorological data over extended time periods to evaluate the impact of low frequency noise from modern off-shore wind farms. (C) 2014 Acoustical Society of America.

A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

A new index to estimate aircraft icing in clouds from operational meteorological models has been developed by Swedish meteorologists. Although rather simple it takes into account, directly or indirectly, all the principal meteorological variables for icing. The index has been evaluated during three winter seasons and is now operational in the Swedish HIRLAM model. A graphical representation of the index is presented.