Lightning data as observed by the European Cooperation for Lightning Detection (EUCLID) network are used in combination with radar data to retrieve the temporal and spatial behavior of lightning outliers, i.e., discharges located in a wrong place, over a 5-year period from 2011 to 2016. Cloud-to-ground (CG) stroke and intracloud (IC) pulse data are superimposed on corresponding 5 min radar precipitation fields in two topographically different areas, Belgium and Austria, in order to extract lightning outliers based on the distance between each lightning event and the nearest precipitation. It is shown that the percentage of outliers is sensitive to changes in the network and to the location algorithm itself. The total percentage of outliers for both regions varies over the years between 0.8 and 1.7 % for a distance to the nearest precipitation of 2 km, with an average of approximately 1.2 % in Belgium and Austria. Outside the European summer thunderstorm season, the percentage of outliers tends to increase somewhat. The majority of all the outliers are low peak current events with absolute values falling between 0 and 10 kA. More specifically, positive cloud-to-ground strokes are more likely to be classified as outliers compared to all other types of discharges. Furthermore, it turns out that the number of sensors participating in locating a lightning discharge is different for outliers versus correctly located events, with outliers having the lowest amount of sensors participating. In addition, it is shown that in most cases the semi-major axis (SMA) assigned to a lightning discharge as a confidence indicator in the location accuracy (LA) is smaller for correctly located events compared to the semi-major axis of outliers.
The locations of the EUCLID sensors in the domain are indicated (black dots), as well as the positions of the radars (white stars) together with the respective collective detection range in Belgium and Austria. The red boxes indicate the two areas that are used in this study.
Present-day lightning location systems (LLSs) are the result of continuous development over the years with improved location accuracy (LA), peak current estimation and type classification for each observed lightning event. However, despite the great progress that has been made in the determination of those properties amongst others, occasionally some events remain poorly determined by the LLS. For instance, the uncertainty of the measurements related to a low peak current discharge tends to be larger than it is for a high peak current event. In addition, it is still common practice to categorize positive cloud-to-ground (CG) strokes with estimated peak currents smaller than five or 10 kA as intracloud (IC) pulses since those are more likely to be of IC nature (Cummins et al., 1998, 2006; Wacker and Orville, 1999a, b; Jerauld et al., 2005; Orville et al., 2002; Biagi et al., 2007). However, not all the properties are of equal importance for the different users of lightning data. Depending on the customers' application of the LLS data, different performance features are more important, while others are less important; e.g., power utilities normally do not care about the IC detection efficiency (DE) of an LLS, whereas the quality of the CG data is of utmost importance. On the other hand, aviation control and meteorological services which often trigger warning messages based on LLS data favor a good DE of CG and IC events coupled to a minimum of events located in a completely wrong position. It is therefore necessary to gain a thorough understanding of the LLS at hand.
During recent years the performance of LLSs has received more and more attention (Nag et al., 2015). A direct method to determine the quality of a network, and therefore the values assigned to each lightning event, is by comparing the data against so-called ground-truth observations. Those observations provide valuable information on the DE, location accuracy and in some cases even the peak current estimates retrieved from an LLS. This is done for instance by examining direct lightning strikes to instrumented towers (Diendorfer et al., 2000a, b; Pavanello et al., 2009; Romero et al., 2011; Schulz et al., 2012, 2013; Cramer and Cummins, 2014; Azadifar et al., 2016) through the use of rocket-triggered lightning (Jerauld et al., 2005; Nag et al., 2011; Mallick et al., 2014a, b, c) and/or by recording lightning strikes with high-speed video and E-field (electric field) measurements in open field (Biagi et al., 2007; Poelman et al., 2013a; Schulz et al., 2016). Although they are the best methods for retrieving robust information of a networks' performance, they are quite labor intensive when used to acquire a large enough dataset for a statistically reliable output. Other methods exist, such as the intercomparison of different LLSs within regions of overlapping coverage (Said et al., 2010; Pohjola and Mäkelä, 2013; Poelman et al., 2013b). However, the main disadvantage of those studies is the assumption that one network is the ground truth. In reality this is hardly the case for any existing LLS, except maybe for the short-baseline lightning mapping arrays (Rison et al., 1999; Thomas et al., 2004; van der Velde et al., 2013; Defer et al., 2015).
In this paper lightning data are combined with radar precipitation observations to analyze the temporal and spatial behavior of lightning outliers in two topographically different regions in Europe. Lightning outliers are sometimes also referred to in the literature as fake or ghost strokes and can be the result of signal interferences from power lines, radio frequencies, or other site-specific disturbances or are simply misplaced events by the location algorithm. The results presented here are obtained by combining lightning observations from the European Cooperation for Lightning Detection (EUCLID) network with radar precipitation data in Belgium and Austria, as described in Sect. 2. The results of the analysis are presented in Sect. 3 and summarized in Sect. 4.
Distribution of the
The European Cooperation for Lightning Detection network has been
operational since 2001 and processes as of January 2017 in real-time data of
164 sensors to provide European-wide lightning observations of high and
nearly homogeneous quality (Schulz et al., 2016; Poelman et al., 2016). All
of the sensors operate over the same low-frequency (LF) range and provide
amongst others timing and angle information. The individual raw sensor data
are sent in real time to a single processor, calculating the electrical
activity at any given moment. The locations of the EUCLID sensors are
displayed in Fig. 1. The network has been tested continuously over the years
against ground-truth data from direct lightning current measurements at the
Gaisberg tower in Austria (Schulz et al., 2016), Peißenberg tower in Germany
(Heidler and Schulz, 2016) and Säntis tower in Switzerland (Romero et
al., 2011; Azadifar et al., 2016) as well as data from E-field measurements and video recordings
in Austria, France and Belgium (Schulz et al., 2016). The latest
comprehensive performance analysis of the EUCLID network based on those
measurements revealed that the flash and stroke DE for negative CG discharges
in different regions of the EUCLID network are greater than 93 and 84 %,
respectively, while for positive events those are greater than 87 and
84 %, respectively (Schulz et al., 2016). To retrieve the latter values,
only those strokes that match certain quality
criteria such as
During the time period under consideration, significant changes in the EUCLID network regarding DE and LA were made (Schulz et al., 2016). Those are related to new sensor technology, timing error corrections and a new location algorithm which can influence the outlier behavior. One would think sensor upgrades have always a positive influence on a network's performance. While this is generally true in the long run, the upgrades can cause temporary problems in the beginning since those sensors are awaiting calibration. This is especially true for some sensors in Italy in 2014. From the day of the setup until the sensors were calibrated, those sensors were configured to provide timing information only. However, timing-only sensors often increase the number of outliers if they are used in solutions determined by two or three sensors only.
Figure 2a plots the annual distribution of the total stroke count over the years 2011 until 2016, as observed within the red boxes in Fig. 1. As expected, the CG distribution experiences a natural annual variability in Belgium as well as in Austria. With regard to the IC detections, one notices a sharp increase in 2015 and 2016. This increase is not climatological in nature, but is attributed to the increased amount of LS700x sensors in EUCLID and its capability to detect IC pulses in the low-frequency domain. The distribution of the total monthly stroke count is shown in Fig. 2b. A peak in activity is observed in June and July for Belgium and Austria, respectively. For both regions about 95 % of all the observed lightning activity occurs between May and September.
The weather radar data of the Royal Meteorological Institute of Belgium (RMIB) and of Austro Control in Austria are used in this study. Figure 1 shows the locations (white stars) and coverage (dashed lines) of the individual radars as well as the limit of the composite as the outer contour of all the radars (solid lines). The use of radar composites is preferred over the individual radar observations since individual radar observations can be hampered by shielding effects. This is true especially in mountainous regions such as the Alps in Austria, limiting the detection range where the radar data are still considered of sufficient quality. Additionally, since the height of the radar beam above ground increases with increasing distance from the radar, precipitation can be underestimated or even undetected at far range by overshooting when precipitation is produced below the height of the lowest radar beam. Therefore, to eliminate the latter effect, the two geographical areas in this study are limited to the red boxes as indicated in Fig. 1.
The composite radar reflectivity threshold is set at 12 dBZ. Following the
The radar composite used at RMIB consists of three radars. RMIB owns and
operates two of them: the radar at Wideumont in the southeast of Belgium and
the radar in Jabbeke located near the west coast which has only been
operational since 2013. The third weather radar at the center of the
composite is located at the airport in Zaventem near Brussels and is operated
by Belgocontrol, which is in charge of the safety of civil aviation. All of the radars
are C-band Doppler radars performing a multiple elevation reflectivity scan
every 5 min with a resolution of 1
Example of a 5 min precipitation field superimposed with the
lightning events within the time interval. The correctly located events are
indicated as black dots, whereas the derived outliers are plotted in red. For
clarity the underlying precipitation field has been given the same value
above the applied threshold of 0.2 mm h
Annual variation of outliers in
Austro Control, the Austrian civil air service provider, operates five
EEC (Enterprise Electronics Corporation)
C-band polarized Doppler weather radars in Austria, of which four of them
are used in this study. Two of the radar sites are located in the foothills
of the Alps close to Vienna and Salzburg (Rauchenwarth and Feldkirchen), while
the other two radar sites are situated in the west and south of Austria at
mountaintops above 2000 m, close to Innsbruck and Klagenfurt (Patscherkofel
and Zirbitzkogel). The underlying volume scan contains 16 elevations ranging
between
To account for border effects of the radar observations as mentioned in
Sect. 2.2, only lightning events within the red boxes as indicated in Fig. 1
are used. Those regions correspond approximately to the area where two or
more radars participate in the radar image with sufficient distance from the
border. Subsequently, CG strokes and IC pulses with timestamps that fall
within the start and end time of the radar scan are superimposed on the
corresponding 5 min radar precipitation fields. In order to have overall
homogeneous coverage of the weather radar data, only the time steps
for which all the radars within the composites were in operation were used. An event is
then categorized as an outlier when no precipitation within a certain
distance has been observed. The distance at which an event is classified as
an outlier is chosen arbitrarily. Different runs are performed by applying a
distance
The overall annual percentage of outliers for CG strokes and IC pulses
relative to the total number of events, as a function of
Panels
The left panels in Fig. 5 display the 6-year mean annual total lightning
event (CG strokes
Figure 6 illustrates the monthly variation of the percentage of outliers. An obvious decrease is observed in the percentage of outliers during May–September, compared to the other months of the year. This feature could be related to the fact that more sensor upgrades occur during winter or because precipitation of winter thunderstorms is more difficult to detect with the weather radars. In addition, the 3-D structure of lightning flashes in winter compared to summer is somewhat different (Lopez et al., 2017), which could increase the difficulty of locating those in winter accurately. Regarding the sensor upgrades, they often result in disabled angle information because systematic angle errors, i.e., site errors, are at first unknown and the correction takes a while because lightning data are necessary. Consequently, upgraded sensors start operating with disabled angle information during winter months. With respect to the observation of precipitation, during summer most of the storms are associated with large amounts of precipitation in vertically extended clouds, meaning that these storms are always very well detected by the radars. In contrast, winter storms are generally associated with less intense precipitation cells and with smaller vertical extensions. In some cases, winter storms are not detected by the radars at long range. In that case, lightning events produced by such undetected winter storms are wrongly classified as outliers. In contrast, an incorrect classification may also occur when a wrong detection appears by chance in a precipitation area detected by the radar. In this case, a wrong lightning detection is classified as a correct detection. Since radars generally detect less precipitation in winter than in summer (e.g., Hazenberg et al., 2011), such misclassification occurs less in winter than in summer, which means that the classification method will produce more outliers in winter. Thus, the reduced efficiency of precipitation detected by the weather radars in winter is an additional possible source of the observed increase of outlier classifications in winter. Note that Poelman et al. (2016) showed that on average peak current estimates of winter lightning are higher than those in summer. One would therefore expect that on average in winter more sensors participate in a lightning event compared to summer, resulting in a good location accuracy. Nevertheless, the absolute number of outliers during winter is much smaller compared to summer, as can be deduced from Fig. 3b. Thus, the increase in the percentage of outliers may not be too important for the majority of applications.
Monthly distribution of the total (CG
Percentage of outliers versus event type in
Percentage of outliers as a function of peak current in
Figure 7 plots the outlier percentages related to each individual group; e.g., the percentage of negative IC outliers is related to the total number of negative IC pulses. Proportionally, the degree of occurrence of positive and negative outliers is of the same level, except for 2011, and follows the annual variation as in Fig. 4. Positive CG strokes exhibit the highest percentage of outliers in Belgium and Austria. This could be related to the fact that positive CG strokes are often accompanied by significant IC activity complicating the transmitted electromagnetic fields (Fuquay, 1982; Saba et al., 2009). It is therefore harder to detect and correctly locate such strokes, resulting in a higher percentage of outliers. Furthermore, the percentage of negative CG outliers is roughly half of that of the positive CG outliers for the years 2011–2014. The opposite is found in the case of IC pulses, where the percentage of negative IC outliers is higher compared to the positive counterpart. However, the difference between positive and negative CG outliers and/or IC pulses decreases in 2015 and 2016. Thus, the percentage of outliers is more or less unrelated to the polarity of the event. In 2016, it is obvious that the outlier percentages of the individual types are more or less in line with each other. This could also be a result of the improved performance of the latest adopted location algorithm.
In Fig. 8, the percentage of outliers for peak current intervals up to
Cumulative distribution of the number of sensors participating in a solution for CG and IC outliers (“out”) and correctly located (“ok”) events.
Distribution of the semi-major axis (SMA) of the outliers and “ok” events in Belgium and Austria for a search radius of 2 km. In addition, the total amount of events per SMA interval is indicated as grey triangles (Belgium) and circles (Austria).
Distribution of the time difference between the outliers and their
closest (in time) correctly located event in
Figure 9 reveals the cumulative distribution of the number of sensors participating in a solution as a function of event type. First of all, one notices that in the case of CG strokes more sensors participate in a solution compared to IC events. This is attributed to the fact that in the LF range the amplitude of even the largest IC pulses is significantly lower compared to that of the CG return strokes (Weidman et al., 1981). The amplitude difference between CG strokes and IC pulses increases even further with increasing propagation distance between the source and the lightning sensor (Cooray et al., 2000). Hence, more sensors will detect the radiation from a single CG discharge compared to an IC pulse. The resemblance in distribution between Belgium and Austria is not surprising since the lightning sensors in EUCLID are quite homogeneously distributed across the network. In addition, more sensors participate in the location of discharges that are correctly located than is the case of CG and IC outlier events. For instance, 85 % of the IC outliers are located by two or three sensors, whereas this drops to 50 % for correctly located IC pulses. For CG strokes on the other hand, only 20 % of the outliers are located with more than six participating sensors, whereas this is the case for more than 60 % for the CG strokes within 2 km of the nearest precipitation. We find that the median number of sensors participating in a solution for correctly located CG strokes and IC pulses is eight and three, respectively, and this drops to three and two participating sensors in the case of CG and IC outliers.
The central processor assigns a value of the
semi-major axis of the 50 % confidence ellipse to each lightning event.
This value can be
used as a quality indicator of the location accuracy, with smaller values
indicating a larger confidence in the assigned location of the event. The
distribution of SMA for all the events (CG
Looking at Figs. 8 to 10 one could wonder whether the CG outliers could be simply considered as IC discharges misclassified by the network, since IC discharges have on average lower peak currents and hence a lower number of contributing sensors and therefore smaller SMA. Although this can be partly true, a considerable fraction of the CG outliers are found to have large peak currents. It is therefore unlikely that all the CG outliers are in fact misclassified IC discharges.
Up to now, lightning discharges have been classified into either correctly located strokes or outliers based on their distance to the nearest precipitation. However, using an additional time criterion it is possible to further dissociate the outliers into isolated outliers in space and time from those that are just wrongly located from a group of correctly located events. Figure 11 plots the distribution of the time difference between the outliers and their closest (in time) correctly located event. Once more, the distribution is found to be similar in Belgium and Austria. The majority of the outliers occur within 1 s of a correctly located event. One could argue that these are simply bad located lightning events, whereas those that take place after 1 s are so-called ghost outliers, i.e., outliers in time and space. Furthermore, from this plot it is found that the outliers behave quite independently of polarity and classification in Belgium and Austria.
In this study all lightning events detected by the EUCLID network during 2011
and 2016 that fall within selected areas in and around Belgium and Austria
are classified as outliers or correctly located events based on their
distance
The applied methodology makes use of radar data with an adopted lower reflectivity threshold of 12 dBZ. Hence, precipitation is required to discriminate between the outliers and well-located lightning events. Therefore lightning produced by “dry” thunderstorms or bolts from the blue will be misclassified as outliers. However, these particular phenomena are extremely limited and do not influence the results presented in this study to a large extent. We believe that a methodology based on satellite cloudiness products would not allow a proper identification of outliers since cloudiness in Belgium and Austria is mostly not associated with thunderstorms.
Categorizing the lightning events based on radar reflectivity data and
comparing the results from different geographical regions is not a
straightforward task. The reason for this is the potential calibration issues in the
different radar networks with possibly different technology and local beam
blockage problems, especially in the mountainous regions in Austria. A
workaround, at least for the last problem, is the use of composite radar data.
Despite these difficulties the overall results in both regions agree quite
well. The overall percentage of outliers for both regions varies annually
between 0.8 and 1.7 % for a distance
All data used in this work are available from the authors upon request (dieter.poelman@meteo.be).
The authors declare that they have no conflict of interest.
The authors would like to thank the reviewers for their constructive remarks and suggestions, which have improved the paper. We further thank the EUCLID consortium for providing the EUCLID data in this study. Edited by: Andrew Sayer Reviewed by: Kleber Naccarato and two anonymous referees