Ice on wind turbine blades reduces efficiency and causes financial loss to energy companies. Thus, it is important to know the possible risk of icing already in the planning phase of a wind park. This paper presents a new Finnish Icing Atlas and the methodology behind it and is prepared by applying the mesoscale numerical weather prediction model AROME with 2.5 km horizontal resolution and an ice growth model based on ISO 12494. The same meteorological dataset is used as was used in the Finnish Wind Atlas (published in 2009), and thus is fully compatible with and comparable with existing climatological wind resource estimations. Representation of the selected time period is evaluated from an icing point of view. Comparing reanalysed temperature and humidity datasets for both the past 20 years and the wind atlas period, we conclude that the used time period represents large-scale atmospheric conditions favourable for icing. We perform a series of sensitivity tests to evaluate how sensitive this ice model is to input from the weather model. The new atlas presents climatological distributions of active and passive icing periods and wind power production loss in map form for three different heights (50, 100 and 200 m) over all of Finland. The results show that the risk for active icing is much greater in coastal areas, while the risk of passive icing is larger inland.

The Stochastically Perturbed Parameterizations scheme (SPP) is here implemented and tested in HarmonEPS - the convection-permitting limited area ensemble prediction system by the international research program High Resolution Limited Area Model (HIRLAM) group. SPP introduces stochastic perturbations to values of chosen closure parameters representing efficiencies or rates of change in parameterized atmospheric (sub)processes. The impact of SPP is compared to that of the Stochastically Perturbed Parameterization Tendencies scheme (SPPT). SPP in this first version in HarmonEPS perturbs 11 parameters, active in different atmospheric processes and under various weather conditions. The main motivation for this study is the lack of variability seen in cloud products in HarmonEPS, as reported by duty forecasters. SPP in this first version is able to increase variability in a range of weather variables, including the cloud products. However, for some weather variables the root mean squared error of the ensemble mean is increased and the mean bias is impacted, especially in winter. This indicates that (some) parameter perturbation distributions are not optimal in the current configuration, and a further sensitivity analysis is required. SPPT resulted in a smaller increase in variability in the ensemble, but the impact was almost completely masked out when combined with perturbations of the initial state, lateral boundaries and surface properties. An in-depth investigation into this lack of impact from SPPT is here presented through examining, among other things, accumulated tendencies from the model physics.

Modern society is very dependent on electricity. In the energy sector, the amount of renewable energy is growing, especially wind energy. To keep the electricity network in balance we need to know how much, when, and where electricity is produced. To support this goal, the need for proper wind forecasts has grown. Compared to traditional deterministic forecasts, ensemble models can better provide the range of variability and uncertainty. However, probabilistic forecasts are often either under- or overdispersive and biased, thus not covering the true and full distribution of probabilities. Hence, statistical postprocessing is needed to increase the value of forecasts. However, traditional closer-to-surface wind observations do not support the verification of wind higher above the surface that is more relevant for wind energy production. Thus, the goal of this study was to test whether new types of observations like radar and lidar winds could be used for verification and statistical calibration of 100-m winds. According to our results, the calibration improved the forecast skill compared to a raw ensemble. The results are better for low and moderate winds, but for higher wind speeds more training data would be needed, either from a larger number of stations or using a longer training period.

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.

Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone-related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large-eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long-term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially-averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to Associated post doc with Vaisala through PoDoCo program.