European cold season lightning map for wind turbines based on radio soundings

Transcription

1 2014 International Conference on Lightning Protection (ICLP), Shanghai, China European cold season lightning map for wind turbines based on radio soundings Stephan Vogel, Joachim Holbøll Department of Electrical Engineering Technical University of Denmark Copenhagen, Denmark Javier López, Anna Candela Garolera, Søren Find Madsen, Global Lightning Protection Services Herning, Denmark Abstract In this paper, the meteorological data of cold season thunderstorms in Japan and Spain are reviewed to determine the threshold conditions at which cold season lightning was recorded in the past. The variables investigated are the height of the -10 C and 0 C isotherms above ground, the wind velocity, the precipitable water in the cloud, and the wind direction. Meteorological data of 72 radio sounding stations in Europe is analyzed for a 5 year period ( ) in the months from October until March. Based on this information, a European map has been created indicating areas where the meteorological conditions for self-triggered upward lightning, as being observed in Japan and Spain, are identified. This map may give an indication if a potential wind power plant or structure has the risk to be affected by frequent lightning attachments in the cold season which are predominantly upward initiated. The advantage of using meteorological parameters to define cold season thunderstorm areas is the independence of Lightning Location Systems (LLS), which are limited to detect upward lightning. Additionally, meteorological data is publicly available. Keywords: upward lightning, tall structures, wind turbines, winter lightning, cold season lightning, radio sounding, I. INTRODUCTION Cold season lightning, also referred as winter lightning, is a well-known local hazard to tall structures such as wind turbines, and it has been studied in the last decades due to its severity primarily in certain parts of the world, such as Japan [1]. Similar lightning discharge observation are also reported in other parts of the world like Spain and the USA [2][3]; however, the phenomenon seems to be limited to particular locations [4]. Recently, a global map was published where cold season thunderstorm locations were indicated [5]. For his analysis, the author used the temperature criterion of 5 C at the 900hPa level and the lightning registrations from the World Wide Lightning Location Network (WWLLN) in order to determine winter lightning activity. The majority of all lightning events to tall structures occurring during cold season thunderstorms are upward lightning discharges initiated at the structure, which may be self-initiated or triggered due to other nearby lightning events. Only few downward lightning strikes are observed during cold season conditions [6]. The reason for this observation may be the low separation distance between the charge in the cloud and the ground. This leads to an intensified electric field above tall grounded objects such as towers or wind turbines which are the starting point for upward initiated leaders. Upward lightning is characterized by large charge transfers, in some cases exceeding 1000 C, which can be critical for grounded structures [7]. It can be separated into three different types of lightning according to the characteristics of the current waveform. According to measurements at the Gaisberg Tower, the most common upward lightning type is characterized by an initial continuous current ICCOnly which was recorded in 48% of all cases, followed by initial continuous currents with a return stroke ICCRs (30%), and initial continuous currents and pulses ICCP (22%) [8]. The detection of the current parameters from cold season thunderstorms with Lightning Location System (LLS) remains problematic. The probability to detect upward lightning with a LLS depends on the sensor technology, the amount of nearby sensors, the computation algorithm and the di/dt of the current waveform. Recently, a publication stated a local detection efficiency for upward lightning with Lightning Location System (LLS) to be 42% [9]. This low percentage is a result of the inability of LLS to detect the most common characteristic upward lightning current waveform ICCOnly. The probability to detect ICCP or ICCRs, are 58% and 96%, respectively. The author obtained these numbers by direct comparison of LLS data from the Austrian Lightning Detection and Information System (ALDIS) with ground measurements at the Gaisberg Tower. In recent years, ALDIS steadily improved the network infrastructure and location algorithms [10] within their network boundaries; however, other LLS are likely to have different performance characteristics. A similar study has been performed in Japan [11]. The author correlated 18% of upward lightning flashes observed by current measuring systems utilizing Rogowski coils with the Japanese lightning detection network (JLDN). Unfortunately, to the knowledge of the author, studies similar to [9][11] are not available for other parts of Europe or the world since upward lightning, especially ICCOnly events, are normally excluded from the performance evaluation of LLS [12][13]. Long-range LLS are known to have inferior performance characteristics compared to medium-range LLS. According to [14], the WWLLN is able to detect 10 % of lightning incidences with peak current amplitude larger than ±35 ka and below 2 % of the lightning incidences with peak

2 currents below 10 ka. Recently, GLD360 has updated the location algorithm increasing the CG flash detection efficiency up to 55% - 85% depending on the local time of day, determined with National Lightning Detection Network (NLDN) as reference [15], which in turn has a limited efficiency to determine upward lightning [4]. As can be seen, lightning detection technologies and efficiencies vary vastly and it is problematic to make conclusive statements of the ability of LLS to detect upward lightning due to cold season storms in other parts of Europe. Due to the uncertainties and the costs of using LLS data to evaluate which locations are prone to cold season lightning, another approach to predict cold season lightning affected areas is investigated in this paper. The meteorological conditions in which these lightning discharges were observed have been used in order to assess possible exposed spots in Europe. The meteorological analysis of cold season lightning in Japan and Spain are used to define threshold conditions when cold season lightning was recorded in the past. Meteorological data from 72 radio sounding stations in Europe are then analyzed for a 5 year period ( ) in the months from October until March. With this information, a European map has been created to identify the same meteorological conditions observed during cold season lightning. This map can be used to determine if a potential wind power plant has a risk to be affected by cold season lightning. II. Characteristics of Cold Season Lightning In a previous publication [16], Lightning Location System (LLS) data provided by the European network LINET were used in order to identify the most severe thunderstorm incidences for five different wind power plant locations in Europe. For the analysis, five different sites in Europe were selected which were located in Croatia, Italy, France, Spain and one offshore site in the North Sea. In total, 27 thunderstorm days were analyzed. The analysis showed that two sites (Italy, France) were exposed to lightning mainly in the warm season with nearly no lightning activity in the winter months, whereas three sites (Croatia, Spain, North Sea) were also exposed to high lightning activity during the cold season. An in-depth analysis of the meteorological conditions of these events conducted by radio soundings and radar observations revealed that the atmospheric conditions during cold season lightning detected by the LLS were very similar in all cases. These conditions were characterized by ground temperatures slightly above freezing point, a relative distance of the -10 C isotherm above the ground not exceeding 2000 meter, a relative distance of the 0 C isotherm not exceeding 400 meter, and wind velocities above 12 ms -1. The temporal analysis of the thunderstorm revealed many low-amplitude discharges of a few ka in the vicinity of the wind turbines which were continuously recorded during a long time period up-till 18 hours. The vicinity of the wind turbine was determined by the so called collection area, which is defined in IEC , and can be described as a circle around the wind turbine with a radius of 3 times the turbine height. This observation indicates that cold season thunderstorms, which produce lightning discharges, are characterized by steady lightning environments. This gives the possibility to investigate the atmospheric conditions with radio soundings which are usually performed every 12 hours. In addition, radar observations in Spain which were overlaid with LLS data revealed that the electrified clouds were moving from the sea towards the land. These storms are spatially spread over wide areas (>200km). Lightning discharges were detected mainly within a range of 50 kilometers from the shoreline. Due to the low apparent cloud height, there is a high probability that most of these discharges were upward lightning discharges. Furthermore, due to the limitation of LLS [9], most likely not all lightning discharges were detected. However, as reported in [17], LINET has continuously improved the sensor network in the Basque country which presumably enables to detect a good fraction of ICCP or ICCRs events. When comparing the lightning environment of a wind power plant which is located in a cold season lightning area by utilizing medium-range Lightning Location System (LLS) data, remarkable differences between the warm and cold season can be found. Figure 1 maps five years of LLS data from a wind power plant in Spain in the non-convective season (October- March). Figure 1: Wind power plant in Spain which is exposed to cold season lightning Cluster formation. Severe upward lightning formation. 5 years LLS data (October March) Figure 2: The same wind power plant in Spain during the summer months. Discharges scattered in area. Downward lightning activity. 5 years LLS data (October March)

3 Wind turbines are marked with a black triangle facing down and lightning detections are marked with colored circles. As can be seen, the local flash density around the wind turbines is distinctly enhanced whereas the remaining areas of the map show infrequent lightning activity. This observation is indicative for upward lightning activity since the upward leader is starting at the extremities of the structure. If wind power plants are built in such an environment, frequent low-peak current amplitude attachments need to be considered for the lightning protection purposes. Even though peak current amplitudes may be low, the charge content of upward lightning may be high, leading to degradation of lightning receptors. Figure 2 illustrates the same wind power plant in the convective season (April-September). Lightning detections are scattered in the entire area and only small cluster around the wind turbines can be identified presumably by other-triggered upward lightning discharges from nearby downward lightning. Similar current discharge patterns were observed during a five year measurement campaign in the north-west coast of Japan which took place from The discharges were measured by Rogowski coils and the majority of them were low amplitude current discharges with a median value of approximately 5kA. Nearly all the measured lightning discharges, incepted by the wind turbines, were upward lightning and only 1.5% were classified as downward lightning [7]. The analysis of the meteorological environment of these thunderstorms in the non-convective season (October-April) was investigated in [1]. Two different classifications of the cold season storm were made according to the severity: the inactive type with less than 100 lightning detections and the storm type with more than 1000 lightning detections per day - where the latter is clearly the more dangerous type for wind turbines. The author correlated the height of the -10 C isotherm with the exposure of wind turbines and found that in most cases, the isotherm was below 2000 meters altitude when lightning struck the wind turbines. Interestingly the cold season lightning incidences are not spread all over Japan but are focused within the first 35km of the north-west coast of Japan. Considering the predominant wind direction during the cold season being westnorthwest, the thunderstorms reach the north western coast first and expose the turbines to the severe winter lightning activity. This indicates that the wind direction may have a big impact when investigating cold season lightning conditions. To summarize the observations, the following parameters are used as inputs for the meteorological model. All quantities can be determined from radio sounding measurements. Furthermore, a physical relation of the parameters concerning cold season lightning is highlighted. Microscale effects of the parameters due to topography cannot be modeled with this approach. Charge indicator: The distance of the -10 C isotherm to ground is based on the observations of cold season lightning which were reported in Japan and Spain. The parameter is used as an indicator to determine at which height the charge is located in the cloud[18]. A threshold value of 2000m to ground is used. Furthermore, few lightning activity was observed when the -10 C isotherm was below a distance of 600m to ground [1][16][19]. Temperature indicator: The distance of the 0 C isotherm to ground establishes the conditions of temperatures around the freezing point. Cold season thunderstorms developed in ground temperature below 0-6 C. A threshold distance of 400m to ground for the 0 C isotherm is derived from the analysis of [16] for cold season thunderstorms. Wind Speed: Cold season thunderstorms are often, but not exclusively, combined with high wind velocities [6]. To define a threshold level for the model, a minimum value of 12ms -1 at ground level is used as observed in the analysis in [16]. Moisture indicator: When cold season lightning was observed, significant humidity up to saturation was reported. Furthermore, precipitation in form of snow or hail was apparent. Often alternating high humidity and dry areas were observed. Since an investigation of the relative humidity %rh as function of the height during cold season thunderstorms remained inconclusive, the precipitable water content of a cloud provides a better prediction for the moisture. The value determines the amount of water that can be potentially released by the cloud. The threshold of 9.5 [mm] is used from [16]. Wind Direction: As observed in Japan[1], only the northwest coast of the main island is exposed to winter lightning. In Japan the main wind direction during the non-convective season is west-northwest, a correlation between exposure and wind direction is established. As it seems, the clouds are discharged within the first tens of kilometers after the cloud reaches the main land. Similar observations were recorded with LLS and radar observations in Spain [16]. Figure 3: Overview of 72 radio sounding used for data analysis

4 Table I summarizes the meteorological conditions which are used to determine if the conditions for cold season lightning conditions are met in a certain site. TABLE I. METEOROLOGICAL THRESHOLD VALUES Description Parameter Unit Threshold Source Distance -10 C isotherm to ground [m] [1][16][19] Distance 0 C isotherm to ground [m] < 400 [16] Wind Speed at ground [ms-1] >12 [1][16] Precipitable water [mm] >9.5 [16] Wind Direction at - 10 C isotherm v [deg] f(x,y) [1][16] III. METEOROLOGICAL MODEL The model uses the combination of meteorological and topographic data in order to show areas which experience similar meteorological conditions in which cold season lightning has been observed before. A. Elevation Data Publicly available elevation data are used from the Shuttle Radar Topography Mission (SRTM) dataset. An initial geospatial resolution of 30arc-second is further downscaled to about 2.46 arc-minutes in order to improve the calculation time of the model. The bounding box corner points of Europe are defined as Longitude (-10, 30 ) and Latitude (35, 60 ). This gives in a resolution of 960x600 points. In terms of absolute tile sizes, one tile has metric distance of Longitude: kilometer and Latitude: kilometer, depending on the location (x,y). Since the map illustrates the potential risk for cold season lightning to wind turbines, each elevation point has been increased a difference of Δh=150m, which represents the standard total height of a new-generation wind turbine. B. Meteorological Data The weather data of 72 radio sounding stations is used to analyze the condition of the atmosphere. An overview of the locations of the radio sounding stations is presented in Figure 3. For the model, 5 years of data from the nonconvective season (October March 2014) is downloaded from the University of Wyoming. Usually, two measurements per day were obtained for each station at 00:00 UTC and 12:00 UTC; in the event of missing data, the results were interpolated from other radio sounding stations. Following data are available through radio soundings as a function of the altitude: pressure, temperature, dew-point, relative humidity, mixingratio, wind-speed, wind direction, potential temperature, equivalent potential temperature, and virtual potential temperature. Furthermore, there are several sounding indices available which are derived from the above mentioned parameters. One of the indicators, the precipitable water for the entire sounding, is used as indicator for a high moisture content which is often observed during cold season lightning. It is defined by the integrated mixing-ratio of water as the function of pressure multiplied by the inverse of the density of water and acceleration of gravity : (1) Figure 4: Example of a section of the streamline vector map. The wind direction of the -10 C isotherm is calculated for each element based on the interpolation of the radio sounding data. It is further classified into one of the cardinal or intermediate wind directions. The figure represents only a small section of the map. Figure 5: Each location classified as exposed (1) is evaluated regarding the wind direction. Only the first 5 elements facing the wind direction (green elements) are kept. Red elements are discarded from the investigation. Notice, in the upper cluster, the wind direction is North, whereas in the lower cluster, the wind direction is North West. Red arrows indicate the apparent wind direction at location (x,y).

5 Figure 6: European map with locations indicated in which meteorological conditions for self-triggered upward lightning are fulfilled. The color scale on the right indicates the number of days, where cold season lightning is possible per year. Red circles indicate positions, where cold season lightning was observed with a LLS or is known to be apparent by instrumented towers (Wind power plants: Spain and Croatia; Instrumented Towers: Gaisberg Tower, Saentis Tower, Peissenberg Tower, Tower at San Salvatore). Magenta crossed circles indicate positions where no cold season lightning was observed with LLS (Wind power plant in Italy, France (2x), Germany, Poland, UK). Ground elevation offset is Δh=150m in order to evaluate the possible presence of wind turbines. In general, radio sounding measurements are available globally. Therefore, there is a high potential to adapt this methodology to other parts of the world. C. Implementation To automate the process, a script has been created which downloads the necessary weather data automatically, filters unusable data, sorts the datasets in the right format, and processes the information. For each half-day, one surface map of the -10 C isotherm, one surface map of the 0 C isotherm, one surface map of precipitable water, and one streamline vector map of the wind direction are created. Furthermore, the wind velocity is calculated throughout the atmosphere for each step in altitude of Δz=41 meters in order to approximate the wind velocities at each height in the model. The data points of the 72 measurements are then linearly interpolated over the elevation grid (960x600) in order to have an evaluation of the weather conditions at each point (x,y). Locations outside the radio sounding stations are extrapolated with the nearest neighbor method. After all the datasets are calculated, each position (x,y) in Europe is individually evaluated according to the threshold criteria for cold season thunderstorms (Table. I). If the distance of -10 isotherm is below 2000 meters and above 600m relative to the ground, the distance of the 0 isotherm is below 400 meters, the wind speed at ground is above 12ms -1, and the precipitable water content is above 9.5 millimeter, the location is marked as exposed to cold season lightning at location (x,y) in the evaluation grid (960x600) on that day for the given time of the radio sounding (which is either 00:00 UTC or 12:00 UTC). The analysis of the data revealed that often a cluster of exposed spots was marked, for instance at coastlines at mountainous areas. At this point, the influence of the wind direction is applied to deselect positions in the grid which were not the facing the wind direction. This reproduces the effect of a cloud with finite charge content which discharges preferably at the first locations where the

6 previous four cold season criteria are met. To perform the computation, the wind direction at the -10 isotherm is calculated as a streamline vector map. Each grid element, is further classified into one of the cardinal or intermediate directions, as illustrated in Figure 4 Then, the algorithm detects the neighbor elements of the marked position and evaluates whether it is the one of the first five elements towards the wind direction. Cardinal directions relate to the neighbor elements of the grid in horizontal or vertical manner, whereas intermediate directions use the diagonal elements in respect to the neighbor. If the element is equal or bigger than the sixth element in order, the location is deselected. An illustrative example of the process is shown in Figure 5. Green elements indicate locations which are facing the calculated wind direction, whereas red areas are shielded by green locations. The red elements will be deselected. The number five in the model is a measure of distance and represents the movement of the cloud over the ground which presumably discharges within the first kilometers when the meteorological conditions are met. Five positions are related to kilometers, depending on the location on the map. This evaluation is performed for each spot which fulfilled the first four criteria and is thereby marked with a one in the grid. Finally, the analysis is conducted for each time data set (twice a day) and the exposed locations are summed-up and normalized by the amount of years under survey. IV. RESULTS AND DISCUSSION In Figure 6, the results from meteorological assessment of Europe are illustrated. In this map, the x and y axis represent the longitude and latitude of Europe, respectively, and the z axis defines the number of days in which cold season thunderstorm conditions are fulfilled. The visualization of the locations is performed with a color scale and an 2D grid according to the data point of the location. Certain hot spots can be identified in the map. The highest values are reported in descending order in the south of Italy around the volcano Etna (38.3 days/year), the mountain formation Picos de Europa in north Spain (34.6 days/year), and around the national park Sierra Nevada in the south of Spain (34.3days/year). Furthermore, exposed areas are found to be in the mountainous regions around the Alps and in the Pyrenees. These spots are clearly highlighted due to the big elevation difference compared to the adjacent surroundings. Smaller values of around cold season thunderstorm days are indicated in the north of Spain, central/south Italy, the coastline of the Balkans, north of Africa and Scotland. Locations with less than 10 cold season thunderstorms days are Norway, some areas in central Germany, central Spain, south France, and the Transylvanian Alps. It needs to be emphasized that this map aims to indicate upward lightning areas due to meteorological measurements from radio soundings of cold season thunderstorm conditions in Spain and Japan. It does not provide any information of natural downward lightning. In order to try to approach a validation of this map, several positions are marked with red and magenta color. Red circles indicate locations where frequent cold season lightning (hence cluster formation as indicated in Figure 1) was observed by LLS or tower measurements in the past (Wind power plant in Croatia and Spain, Saentis Tower, Gaisberg Tower, Peissenberg Tower and Tower at San Salvatore) and magenta colored crossed circles indicate locations in which LLS data did not indicate the presence of cold season thunderstorms (Italy, France, Germany, UK, and Poland) [16][20]. Indeed, there is a tendency that positions indicated by red markers are located in the vicinity of predicted cold season thunderstorm areas. The usage of LLS data for verification of this map requires the detailed daily analysis of the meteorological conditions of the thunderstorm. Especially in South Europe, sporadically occurring high temperatures in October March can trigger convective thunderstorms which cause natural downward lightning. These events are likely to be captured by LLS data which are then erroneously declared as cold season lightning. In order to validate this map further, more lightning current measurements are necessary which are preferably based on on-site measurements instead of LLS data. V. CONCLUSION In this work, the data of the meteorological analysis of cold season thunderstorms in Japan and Spain was used to define threshold values when cold season lightning was recorded in the past. Meteorological data of 72 radio sounding stations in Europe is analyzed for a 5 year period ( ) in the cold seasons of these years from October until March. The data has been used in order to build a model that estimates the probability of exposure to cold season lightning all over Europe. For each tile of the map, the outcome of the model is the number of days in which meteorological winter lightning conditions are fulfilled. This may be used to estimate the risk of a potential wind power plant or structure to be affected by high frequent upward lightning attachments in the cold season. The conditions evaluated are valid for onshore sites only since there were not enough cold season lightning events at offshore wind power plants recorded. Some factors will be definitely relevant such as the temperature gradient between the sea surface and the atmosphere layers above. These conditions are difficult to estimate with radiosondes launched from land and should be analyzed in a future work in order to refine the model for offshore sites. The model considers already the existence of tall structures all over the study area and estimates the exposure based on this. In the future, the method of this paper has potential to be improved by using numerical weather prediction models such as the Weather Research and Forecast Model (WRF) to obtain better temporal resolution of the cold season lightning conditions. This would also enable to investigate instability factors of the atmosphere, which are the results of convective processes. In general, the methodology to identify cold season thunderstorms directly based on conditional instability would be physically more meaningful instead of the comparison of few cold season measurement samples from radio soundings. Unfortunately, an extensive amount of data processing resources are necessary to make such computations on continental level.

7 ACKNOWLEDGMENTS The research has been partly funded by the Danish Energy Agency through the Energy Technology Development and Demonstration Program (EUDP), as part of the ELITE [21] project. Furthermore, we gratefully thank the Basque weather agency Euskalmet for consultation and support. REFERENCES [1] F. Fujii, M. Ishii, M. Saito, M. Matsui, and D. Natsuno, Characteristics of winter lightning threatening wind turbines in coastal area of the sea of Japan, Electr. Eng. Japan (English Transl. Denki Gakkai Ronbunshi), vol. 184, no. 2, pp , [2] J. López, J. Montanyà, M. Maruri, D. De la Vega, J. A. Aranda, and S. Gaztelumendi, Lightning initiation from a tall structure in the Basque Country, Atmos. Res., vol. 117, no. 0, pp , [3] P. S. Market, A. M. Oravetz, D. Gaede, E. Bookbinder, A. R. Lupo, C. J. Melick, L. L. Smith, R. Thomas, R. Redburn, B. P. Pettegrew, and A. E. Becker, Proximity soundings of thundersnow in the central United States, J. Geophys. Res., vol. 111, no. D19, p. D19208, [4] S. Vogel, J. Holbøll, J. López, and A. C. Garolera, Lightning Attachment Estimation to Wind Turbines by Utilizing Lightning Location Systems, in International Lightning Detection Conference, [5] J. Montanyà, F. Fabró, O. van der Velde, V. March, E. R. Williams, N. Pineda, D. Romero, G. Solà, and M. Freijo, Global Distribution of Winter Lightning: a threat to wind turbines and aircraft, Nat. Hazards Earth Syst. Sci. Discuss., no. January, pp. 1 16, [6] N. Takagi and D. Wang, Characteristics of Winter Lightning that Occurred on a Windmill and its Lightning Protection Tower, IEEJ Trans. Power Energy, vol. 131, no. 7, pp , [7] M. Ishii, NEDO R & D Project for Measures of Lightning Protection of Wind Turbines in Japan, pp , [8] G. Diendorfer, H. Pichler, and M. Mair, Some parameters of negative upward-initiated lightning to the gaisberg tower ( ), IEEE Trans. Electromagn. Compat., vol. 51, no. 3 PART 1, pp , [9] G. Diendorfer, H. Pichler, and W. Schulz, LLS Detection of Upward Initiated Lightning Flashes, in 9th Asia-Pacific International Conference on Lightning, 2015, pp [10] W. Schulz, Location Accuracy Improvements of the Austrian Lightning Location System During the Last 10 Years, Light. (APL), th Asia-Pacific Int. Conf., pp. 1 5, [11] M. Saito, A. Sugita, and D. Natuno, Lightning Strokes Forming Hot Spots, Light. Prot. (ICLP), 2012 Int. Conf. Light. Prot., [12] M. Azadifar, F. Rachidi, M. Rubinstein, M. Paolone, G. Diendorfer, H. Pichler, W. Schulz, D. Pavanello, and C. Romero, Evaluation of the Performance Characteristics of the European Lightning Detection Network EUCLID in the Alps Region for Upward Negative Flashes Using Direct Measurements at the Instrumented Säntis Tower, J. Geophys. Res. Atmos. J. Geophys. Res. Atmos. J. Geophys. Res. Atmos, p. n/a n/a, [13] W. Schulz, D. POELMAN, S. PEDEBOY, C. V. H. PICHLER, and G. Diendorfer, Performance Validation of the European Lightning Location System EUCLID, in International Colloquium on Lightning and Power Szstems, [14] S. F. Abarca, K. L. Corbosiero, and T. J. Galarneau, An evaluation of the Worldwide Lightning Location Network (WWLLN) using the National Lightning Detection Network (NLDN) as ground truth, J. Geophys. Res. Atmos., vol. 115, no. 18, pp. 1 11, [15] R. Said and M. Murphy, GLD360 Upgrade : Performance Analysis and Applications, in 24th International Lightning Detection Conference, [16] S. Vogel and J. Lopez, Lightning Location System Data from Wind Power Plants Compared to Meteorological Conditions of Warm- and Cold Thunderstorm Events, in CIGRE - International Colloquium on Lightning and Power Systems, [17] V. March, Upward lightning observations on a wind turbine and its implications to environmental factor for risk assessment, in APL2015, 2015, pp [18] V. A. Rakov and M. A. Uman, Lightning: Physics and Effects. Cambridge University Press, [19] N. Kitagawa and K. Michimoto, Meteorological and electrical aspects of winter thunderclouds, J. Geophys. Res., vol. 99, no. D5, pp , [20] H. Zhou, G. Diendorfer, R. Thottappillil, H. Pichler, and M. Mair, The Influence of Meteorological Conditions on Upward Lightning Initiation at the Gaisberg Tower, pp , [21] K. Bertelsen, J. Lopez, S. Vogel, S. F. Madsen, and H. I. Park, Enhanced lightning effects testing for optimized wind turbine reliability, 2015.