Atmospheric aerosol particles absorb and scatter solar radiation, directly altering the Earth’s radiation budget. These particles also have a complex role in weather and climate by changing cloud physical properties such as reflectivity by acting as cloud condensation nuclei or ice nuclei. Aerosol particles in the boundary layer are important because they pose a negative impact on air quality and human health. In addition, elevated aerosol from volcanic dust or desert dust present an imminent threat to aviation safety. To improve our understanding of the role of aerosol in influencing climate and the capability to detect volcanic ash, a ground-based network of Halo Doppler lidars at a wavelength of 1565 nm is used to collect data of atmospheric vertical profiles across Finland. By comparing the theoretical values of depolarization ratio of liquid clouds with the observed values, bleed through of each lidar is detected and corrected to improve data quality. The background noise levels of these lidars are also collected to assess their stability and durability. A robust classification algorithm is created to extract aerosol depolarization ratios from the data to calculate overall statistics. This study finds that bleed through is at 0.017 ± 0.0072 for the Uto-32 lidar and 0.0121 ± 0.0071 for the Uto-32XR lidar. By examining the time series of background noise level, these instruments are also found to be stable and durable. The results from the classification algorithm show that it successfully classified aerosol, cloud, and precipitation even on days with high turbulence. Depolarization ratios of aerosol across all the sites are extracted and their means are found to be at 0.055 ± 0.076 in Uto, 0.076 ± 0.090 in Hyytiala, 0.076 ± 0.071 in Vehmasmaki and 0.041 ± 0.089 in Sodankyla. These mean depolarization ratios are found to vary by season and location. They peak during summer, when pollen is abundant, but they remain at the lowest in the winter. As Sodankylä is located in the Artic, it has aerosols with lower depolarization ratio than other sites in most years. This study found that in summer, aerosol depolarization ratio is positively correlated with relative humidity and negatively correlated with height. No conclusion was drawn as to what processes play a more important role in these correlations. This study offers an overview of depolarization ratio for aerosol at a wavelength of 1565 nm, which is not commonly reported in literature. This opens a new possibility of using Doppler lidars for aerosol measurements to support air quality and the safety of aviation. Further research can be done test the capability of depolarization ratio at this wavelength to differentiate elevated aerosol such as dust, pollution, volcanic ash from boundary layer aerosol.

The renewable energy sources play a big role in mitigating the effects of power production on climate change. However, many renewable energy sources are weather dependent, and accurate weather forecasts are needed to support energy production estimates. This dissertation work aims to develop meteorological solutions to support wind energy production, and to answer the following questions: How accurate are the wind forecasts at the wind turbine hub height? What is the annual distribution of the wind speed? How much energy can be harvested from the wind? How does the atmospheric icing affect wind energy production and how do we forecast these events? The first part of this dissertation work concentrates on resource mapping. Wind and Icing Atlases bring valuable information when planning wind parks and where to locate new ones. The Atlases provide climatological information on mean wind speed, potential to generate wind power and atmospheric icing conditions in Finland. Based on mean wind speed and direction, altogether 72 representative months were simulated to represent the wind climatology of the past 30 years. A similar detailed selection could not be made with respect to icing process due to lack of icing observations. However, sensitivity tests were performed with respect to temperature and relative humidity, which have an influence on icing formation. According to these sensitivity tests the selected period was found to represent the icing climatology as well. The results are presented in gridded form with 2.5 km horizontal resolution and for 50 m, 100 m and 200 m heights above the ground, representing typical hub heights of a wind turbine. Daily probabilistic wind forecasts can bring additional value to decision making to support wind energy production. Probabilistic weather forecasts not only provide wind forecasts but also give estimations related to forecast uncertainty. However, probabilistic wind forecasts are often underdispersive. In this thesis the statistical calibration methods combined with a new type of wind observations were utilized. The aim was to study if Lidar and Radar wind observations at 100 m’s height can be used for ensemble calibration. The results strongly indicate that the calibration enhances the forecast skill by enlarging the ensemble spread and by decreasing RMSE. The most significant improvements are identified with shorter lead times and with weak or moderate wind speeds. For the strongest winds no improvements are seen, as a result of small amount of strong wind speed cases during the calibration training period. In addition to wind speed, wind power generation is mostly affected by atmospheric icing at Northern latitudes. However, measuring of icing is difficult due to many reasons and, furthermore, not many observations are available. Therefore, in this thesis the suitability of a new type of ceilometer-based icing profiles for atmospheric icing model validation have been tested. The results support the usage of this new type of ceilometer icing profiles for model verification. Furthermore, this new extensive observation network provides opportunities for deeper investigation of icing cloud properties and structure.