A conically scanning, continuous-wave LIDAR is placed on an island in the central Baltic Sea with large open-water fetch, providing wind and turbulence profiles up to 300 m height. LIDAR and Weather Research and Forecasting (WRF) profiles from one year are used to characterize the marine boundary layer, at the same time performing an evaluation of the WRF model against LIDAR measurements with a focus on low-level jet representation. A good agreement is found between the average wind speed profile in WRF and LIDAR, with the largest bias occurring during stable conditions. The LLJ frequency is highest in May with frequency of occurrence ranging between 18% and 27% depending on the method of detection. Most of the LLJs occur during nighttime, indicating that most of them do not have local origin. For cases with simultaneous LLJs in both data sets the WRF agrees well with the LIDAR. In many cases, however, the LLJ is misplaced in time or space in the WRF simulations compared to the LIDAR. This shows that models still must be improved to capture mesoscale effects in the coastal zone.

A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.

The offshore wind climate over the Baltic Sea area has been investigated using the three-dimensional higher-order closure MIUU-model from Uppsala University. A technique for modelling the wind climate with this type of model is presented. The results are verified against measurements. Following the good agreement between model estimates and observations, it is judged that the model output may be used to analyse different aspects of the offshore winds in a detail, which is not possible with existing observations. The influence from land/sea temperature differences on the wind climate and the effects of the related thermally driven flows could be investigated. An attempt was made to quantify these effects on the offshore wind power potential over the Baltic Sea.

Global oceans are an important sink of atmospheric carbon dioxide (CO₂). Therefore, understanding the air–sea flux of CO₂ is a vital part in describing the global carbon balance. Eddy covariance (EC) measurements are often used to study CO₂ fluxes from both land and ocean. Values of CO₂ are usually measured with infrared absorption sensors, which at the same time measure water vapor. Studies have shown that the presence of water vapor fluctuations in the sampling air potentially results in erroneous CO₂ flux measurements resulting from the cross sensitivity of the sensor. Here measured CO₂ fluxes from both enclosed-path Li-Cor 7200 sensors and open-path Li-Cor 7500 instruments from an inland measurement site are compared with a marine site. Also, new quality control criteria based on a relative signal strength indicator (RSSI) are introduced. The sampling gas in one of the Li-Cor 7200 instruments was dried by means of a multitube diffusion dryer so that the water vapor fluxes were close to zero. With this setup the effect that cross sensitivity of the CO₂ signal to water vapor can have on the CO₂ fluxes was investigated. The dryer had no significant effect on the CO₂ fluxes. The study tested the hypothesis that the cross-sensitivity effect is caused by hygroscopic particles such as sea salt by spraying a saline solution on the windows of the Li-Cor 7200 instruments during the inland field test. The results confirm earlier findings that sea salt contamination can affect CO₂ fluxes significantly and that drying the sampling air for the gas analyzer is an effective method for reducing this signal contamination.

Good knowledge about on-site wind climate is necessary in wind power assessments. Due to yearly and natural variability, a few years' measurements do not give information about wind climate. Long-term reference data can be used to correct observations to better correspond a normal year. The method doing this is commonly referred to as measure-correlate-predict (MCP). Uncertainties are introduced in predicted wind climate due to method weaknesses. In this paper uncertainties connected to length and season of on-site measurements are studied. An on-site series of 29 years, different MCP-methods and reanalysis datasets have been used. An additional high-resolution model dataset has been used to study variation within a wind farm. At least 12 months of observations and a long-term reference longer than 15 year are recommended. Large seasonal influences are seen; summer periods tend to overestimate while winter periods underestimate wind climate.