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Leading-edge erosion (LEE) of wind turbine blades is caused by the impact of particles, for example, raindrops, and leads to a loss in the power production and high maintenance cost. Investigations have shown that a reduction in tip speed, so-called erosion-safe mode, increases the blade lifetime but the influence of different drop-size parameterizations and rain amounts on the blade lifetime is unclear. This study compares blade lifetime calculations using two different drop-size parameterizations, which both describe a characteristic drop size of a rain measurement. Furthermore, changes in blade lifetime in case rain amount is increased or decreased are investigated as well as the effect of different wind shear exponents. The blade-lifetime calculations are based on a kinetic-energy model and an accumulated rain model. The results show that a drop-size parameterization based on rain rate leads to 44 times longer blade lifetime compared with a parameterization using in situ drop-size measurements. This large difference is probably due to the underestimation of large drops of the first mentioned parameterization. A change in rain amount of about ±17 % results only in a marginal change in blade lifetime. For both cases and models, an extension of blade lifetime was calculated when reducing the tip speed during specific rain events. The change of wind shear exponents caused as well a considerable effect on the lifetime prediction. Overall, blade lifetime is primary depending on the chosen model, where the kinetic-energy model is highly sensitive to the drop-size parameterization.

Precipitation is a key driver of leading-edge erosion of wind turbine blades, which leads to a loss in annual energy production and high cost for repair of wind turbines. Precipitation type, drop size and their frequency are relevant parameters, but not easily available. Reflectivity-Rain Rate (Z‑R) relationships as well as annual sums of rainfall amount and rainfall kinetic energy potentially could be used to estimate leading-edge erosion. Although Z‑R relationships and amounts are known for several places, their spatial variation and dependence on weather types is unknown in the North Sea and Baltic Sea area. We analysed time series of multiple disdrometers located on the coast of the North Sea and Baltic Sea to characterize the variation and weather-type dependence of the Z‑R relationship, precipitation type, rainfall amount and kinetic energy.The Z‑R relationship as indication for the mean drop-size distribution showed small variations within different locations, but had a large variability for specific, but rare weather types. Only the precipitation types snow and hail showed some tendencies of weather-type dependence. Rainfall amount and rainfall kinetic energy were higher for stations in the eastern part of the North Sea compared to the western part and the Baltic Sea. Highest values were related to advection from the West. Overall, variations with location and weather type were found. These results will need to be considered in leading-edge erosion modelling and site assessment.

Leading-edge erosion at wind turbine blades cause a loss in profit for wind farm owners, in particular offshore. The characterization of the rain erosion environmental load at wind turbine blades is based on the long-term rain rate and wind speed observations at 10-minute resolutions at coastal stations around the North Sea, Baltic Sea, and inland. It is assumed that an IEA Wind 15 MW turbine is installed at each station. The leading-edge lifetime is found to increase from the South to the North along the German and Danish North Sea coastline from 1.4 to 2.8 years. In the Danish and German Baltic Sea, the lifetime in the West is shorter (similar to 2 years) than further East (similar to 3 to 4 years). It is recommended to use a time series of 10 years or longer because shorter time series most likely will cause an overestimation of the lifetime. The loss in profit due to leading-edge erosion can potentially be reduced by similar to 70% using the erosion-safe mode, i.e., reduce the tip speed during heavy rain events, to reduce blade erosion, aerodynamic loss, repair costs, and downtime during repair. The aerodynamic loss for the 18 stations is on average 0.46% of the annual energy production.

Wind turbines operate 24/7 during 25 years to produce electricity for society. Beyond general service, it has recently been necessary to also repair many wind turbine blades due to erosion caused by the impact of precipitation particles, mainly raindrops. The erosion typically starts near the tip and grows along the leading edge of the turbine blade towards the hub. Modern turbines have high tip speeds when operating at full capacity, thus when intense rain occurs during those times, the erosion starts and develops. Turbines with eroded blades produce less electricity as the aerodynamic efficiency is lower. Blade repair mitigates this problem but is very costly and challenging at sea. Thus, there is a need to characterize the precipitation climate at sea for wind turbine operation, blade repair solutionsand for planning future wind farms. Learnings from soil erosion caused by rain, is a good starting point for assessing rain parameters for leading edge erosion at wind turbines. However, important differences are that wind speed is very relevant for wind turbines while e.g. terrain and slope are important for soil erosion. Our study focuses on rain from the Global Precipitation Mission to prepare a pilot investigation of the co-occurrence of rain and wind over the North Sea and the Atlantic Ocean offshore from Portugal. Our analyses include data from coastal meteorological stations as a proxy for the offshore environment. The rain climatology offshore was investigated by Tait et al. (1999) using SSM/I. Schlünzen and Krell (2004) also focus on the North Sea while Kidd (2001) reports at global scale. Interestingly, Tait et al. (1999) show that there are major differences in yearly rainfall in the North Sea. Kidd (2001) shows variation in the Atlantic between different data sources. Our aim is to utilize Earth Observation data for assessing the rain climate, e.g. from the Global Precipitation Mission, in combination with wind speed information from the Advance

Leading-edge erosion (LEE) of wind turbine blades iscaused by the impact of hydrometeors, which appear in a solid or liquid phase.A reduction in the wind turbine blades' tip speed during definedprecipitation events can mitigate LEE. To apply such an erosion-safe mode, aprecipitation nowcast is required. Theoretical considerations indicate thatthe time a raindrop needs to fall to the ground is sufficient to reduce thetip speed. Furthermore, it is described that a compact, vertically pointingradar that measures rain at different heights with a sufficiently highspatio-temporal resolution can nowcast rain for an erosion-safe mode.

A novel method is proposed to maximize the value of operating fleet of wind turbines suffering leading edge erosion. This method will ensure longer lifetime and reduce repair cost. The challenge is to balance the cost of repair, downtime of wind turbines and loss of energy production due to leading edge erosion. Current practice is to inspect the turbines and repair when it is considered necessary from the point of loss of Annual Energy Production (AEP) or structural failure. The cost for repair is high and tends to get higher while the weather windows get narrower as turbines increase in size and stand further away from service ports. The novel method called ‘erosion safe mode’ can be implemented readily to the operating fleet of turbines as well as to new wind farms. The method is cost effective.

Our current knowledge on multi-decadal to centennial changes of snow in different parts of the world is based largely on observations of snow depth and depth of snowfall from national weather and hydrographic services. Studies analysing these snow observations in the European Alps are predominantly based on national data and are therefore limited by their respective borders in the detection of robust, spatiotemporal snow trends. In order to overcome this limitation, data from Austria and Switzerland, which cover a substantial fraction of the Alps when taken together, are merged for this study (196 station-records). Additionally, it is the first time that such an analysis is based on homogenized data. Our homogenization study shows that, although the detection of breaks in snow depth series works quite well with the existing methods, further research is needed to adequately correct snow depth series at a daily resolution. Roughly, 70% (139 station-records) of the snow depth series could be homogenized and are used for further trend analysis. The findings concern seven climatologically different areas that are identified by a regionalization (using empirical orthogonal functions) using station records from 1961 to 2012. These regions share a high degree of inner similarity and outer separation, and the temporal trends detected are rather different across the Swiss-Austrian domain. Regions in the south show a clear decrease in the snow depth of up to −12 cm/decade on average, while those in the northeast are characterized by almost no change. The declining trend in the southern regions intensifies as altitude increases. Comparisons of these variations in depth changes with concurrent changes in air temperature and precipitation totals reveal a clear dichotomy with respect to elevation. Snow depths in low elevated areas are highly sensitive to air temperature changes, whereas those at high elevations strongly depend on alterations in precipitation totals.