NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-16-595-2016The European lightning location system EUCLID – Part 1: Performance analysis and validationSchulzWolfgangw.schulz@ove.atDiendorferGerhardPedeboyStéphanePoelmanDieter RoelOVE-ALDIS, Vienna, AustriaMeteorage, Pau, FranceRoyal Meteorological Institute of Belgium, Brussels, BelgiumWolfgang Schulz (w.schulz@ove.at)2March201616259560527July20154September20158February201623February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/16/595/2016/nhess-16-595-2016.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/16/595/2016/nhess-16-595-2016.pdf
In this paper we present a performance analysis of the European lightning
location system EUCLID for cloud-to ground flashes/strokes in terms of
location accuracy (LA), detection efficiency (DE) and peak current
estimation. The performance analysis is based on ground truth data from
direct lightning current measurements at the Gaisberg Tower (GBT) and data
from E-field and video recordings. The E-field and video recordings were
collected in three different regions in Europe, namely in Austria, Belgium
and France. The analysis shows a significant improvement of the LA of the
EUCLID network over the past 7 years. Currently, the median LA is in the
range of 100 m in the center of the network and better than 500 m within the
majority of the network. The observed DE in Austria and Belgium is similar,
yet a slightly lower DE is determined in a particular region in France, due
to malfunctioning of a relevant lightning location sensor during the time of
observation. The overall accuracy of the lightning location system (LLS)
peak current estimation for subsequent strokes is reasonable keeping in mind
that the LLS-estimated peak currents are determined from the radiated
electromagnetic fields, assuming a constant return stroke speed.
The results presented in this paper can be used to estimate the performance
of the EUCLID network related to cloud-to-ground flashes/strokes for regions
with similar sensor baselines and sensor technology.
Introduction
Lightning location data have been used by power utilities, meteorological services
and other lightning sensitive operations for more than 20 years. In the case
of power utilities the data are important to support the network operator in
order to increase the power system availability and to provide warning
information for maintenance crews in case of approaching thunderstorms. For
all applications of lightning data, it is important to know the performance
of the employed lightning location system (LLS) related to cloud-to-ground (CG)
flashes/strokes in terms of location accuracy (LA) and detection
efficiency (DE). Often it is tried to determine the performance of an LLS by
network cross comparison with data from different LLS covering the same area
(Drüe et al., 2007; Poelman et al., 2013a) but such comparisons typically do not
provide any clear results as long as none of the two networks is of high and
validated performance. Ideally one of the networks should be a kind of
reference network for a certain performance parameter. Therefore, a direct
comparison of LLS data with ground truth data is the best way to validate
the performance of an LLS.
Different approaches to collect ground truth data of lightning discharges
related to CG flashes/strokes are used:
lightning to instrumented towers;
rocket-triggered lightning;
video and E-field records of lightning discharges.
Each of these methods has different advantages and limitations
(for more details see Nag et al., 2015). In order to
evaluate the performance of the EUCLID (European Cooperation for Lightning
Detection) LLS in terms of LA, DE and the accuracy of the peak current
estimate, we are using approaches (A) and (C) in this paper for the
collection of ground truth data by using data from the direct lightning
current measurement at the Gaisberg Tower (GBT) (Diendorfer
et al., 2009a), and video and E-field records of lightning data collected in
three different regions (Austria, Belgium and France) in Europe
(Poelman et al., 2013b), respectively. Those measurements
should be representative for all regions in Europe covered by the EUCLID
network with similar sensor baselines. We do not present any data on
polarity errors because by comparing LLS data with independent E-field
measurement data we have never observed such errors since we started the measurements.
In the past, several analyses were made to estimate the EUCLID performance in
the early stage of the network (before 2005, the beginning of the data
analysis in this paper), e.g., in Slovenia where LLS data were compared to
data from GPS synchronized flash counters installed on mobile phone towers
(Djurica and Kosmač, 2006; Djurica et al., 2009), in France where video surveys were used to determine the actual
network performance of the French lightning location system
(Berger and Pedeboy, 2003) and in Austria where a continuous
E-Field measurement system was developed to evaluate the network performance
(Schulz and Diendorfer, 2006), together with measurements at the
GBT (Diendorfer et al., 2002). Further data from a
VHF mapping system (Lightning Mapping Array) were used during the HyMeX
experiment (Ducrocy et al., 2013; Defer et al., 2015) in the south of France
to validate the EUCLID DE of intra-cloud discharges (Schulz et al., 2014b; Pédeboy et al., 2014).
The above mentioned network evaluation projects were performed during
different time periods in different regions of the EUCLID network. With this
paper we want to show the present status of the network performance and
provide a base for the companion paper by Poelman and coworkers (Poelman et al., 2016).
EUCLID network configuration for 2014. Sensor locations are shown as red dots.
EUCLID network
In 2001 several countries (Austria, France, Germany, Italy, Norway and
Slovenia) started a cooperation named EUCLID. The goal of this cooperation
is to provide to the end users “European-wide” lightning data of high and
nearly homogeneous quality. Since that time also Spain, Portugal, Finland,
Sweden and Belgium joined EUCLID. The EUCLID cooperation is special in the
sense that it is the merge of independent national networks, and the
individual partners are highly motivated to run their local networks with
state-of-the-art lightning detection sensors. All the partners employ
dedicated technicians to supervise and maintain the network and to react in
short time in case of sensor or communication problems. As of December 2014
the EUCLID network employs 149 sensors: 7 LPATS, 10 IMPACT, 31 IMPACT
ES/ESP and 101 LS700x sensors, when listed in order from the oldest to the
newest sensor version. All different sensor types, manufactured by Vaisala
Inc., are operating in the same frequency range with individually calibrated
sensor gains and sensitivities in order to account for any local sensor site
conditions. Figure 1 shows the EUCLID network configuration as of 2014. In
this figure also three areas labeled Region 1, Region 2 and Region 3,
respectively, are indicated. In these three areas video and E-field records
of lightning discharges were collected.
In addition to the processing carried out by each national LLS, data from
all 149 sensors are processed in real-time using a central processor in
Austria at ALDIS (Austrian Lightning Detection and Information System),
which also provides daily performance analyses reports for each of the
sensors. This assures that the resulting lightning data are as consistent as
possible throughout Europe. In fact, the EUCLID data are frequently of
higher quality than the data produced by individual national networks, being
sub-networks of EUCLID. This is due to the implicit higher redundancy in
EUCLID as a result of participation of additional sensors in the lightning
location, which are located outside the national borders in a neighboring
country. We note that there is a full backup EUCLID processing center in
Germany with independent and direct data connections to all sensors.
Further, the transnational EUCLID cooperation also acts as a platform for
knowledge exchange related to lightning location technology and LLS data
applications. Since the beginning of the cooperation, the performance of the
EUCLID network has been steadily improved. Improvements are the result of
employing more advanced location algorithms, of installing state of the art
sensor technology, and relocating sensor positions in case of poorly
performing sensor sites (e.g., local electromagnetic noise). Over the next
1–2 years, at least 10 of the remaining older type sensors are expected to
be upgraded to the newest sensor type (LS700x).
Prior to 2005 the EUCLID network consisted of IMPACT and LPATS sensors only.
More information about the setup of the original network in 2002 can be
found in Schulz and Diendorfer (2002) and Diendorfer (2002). The following important changes and upgrades
in the EUCLID network were made during the last 10 years.
Start of the sensor upgrade to LS700X technology (2006) – The ALDIS network
was the first network in Europe which was upgraded to the LS700x sensor
technology in the beginning of 2006. At this time, not all the new features,
described below, of the LS700x technology were used. Therefore the LS700x
sensor was basically performing like an IMPACT sensor. Successively, more
and more sensors were upgraded by other EUCLID members to the newest technology.
New location algorithm (July 2008) – At this time an updated location algorithm
was installed at the EUCLID central processor. This updated location
algorithm does a more sophisticated job of grouping received sensor data to
a given stroke and also performs iterations after rejection of originally
considered sensor messages because their time or angle measurements did not
fit with the estimated stroke location.
“Sensor-based” onset time calculation (July 2011) – Prior to 2011, the
so-called onset time of the lightning-radiated field arriving at the sensor
site was estimated at the central processor (Honma et al., 1998). The onset time
is the time information that is minimally altered by field propagation
effects over different distances, and it is the time used for the
location calculation based on the arrival time differences. Therefore, it is
important to determine the onset time as precisely as possible. In 2011 a
new feature at the LS700x sensor was put into operation, the so-called
sensor-based onset time calculation (Honma et al., 2011). In this case,
the onset time is derived as a linear extrapolation from the rising edge of
the return stroke wave-front. This type of onset time calculation is
significantly more accurate than the previously used estimation at the
central processor.
Propagation correction (December 2012) – In the complex terrain of the Alps, the
correction of timing errors is very important. Those timing errors are the
result of a combination of propagation effects due to finite ground
conductivity and of an elongation of the propagation path (Honma et al., 2011). As the alpine
region represents a large part of the area covered by the EUCLID network,
those timing errors are important. In order to correct the timing errors for
each sensor, the distance- and angle-dependent time-correction values have
been extracted from the historical sensor data and implemented as
corrections in the central analyzer. As an example for the time error
correction in the Alps, the angle- and distance-dependent time corrections of
sensor #2 (Schwaz) are shown in Fig. 2. This sensor #2 is located in
Austria in a mountain valley that stretches from west to east and is
surrounded by high mountains (up to 3000 m a.s.l.). The highest mountains are
in the south of the sensor site. Compared to sensors located in a more or
less flat region this sensor site shows a very complex structure for
timing correction. It shows large time errors in the west and in the
south-east of the sensor location. All the regions in blue are outside the
operational range (600 km) of this sensor and therefore not corrected for timing errors.
Example of a propagation correction determined and implemented for
Austrian sensor #2 (Schwaz). The sensor is located in the center of the
circular area.
InstrumentationGaisberg Tower (GBT)
Since 1998, direct lightning strikes to a radio tower have been measured on
the Gaisberg, a mountain next to the city of Salzburg in Austria (Diendorfer et al.,
2009b). This 100 m high tower is located on the top of the Gaisberg
(1287 m a.s.l.). Lightning flashes to the tower occur in summer as well as
during winter time. The overall current waveforms are measured at the base
of the air terminal installed on the top of the tower with a current-viewing
shunt resistor of 0.25 Ωm having a bandwidth of 0 Hz to 3.2 MHz. A
fiber optic link is used for transmission of the shunt output signal to a
digital recorder installed in the building next to the tower. The signals
were recorded by an 8 bit digitizing board installed in a personal computer.
The trigger threshold of the recording system was set to 200 A with a
pre-trigger recording time of 15 ms. The lower measurement limit given by
the 8 bit digitizer resolution was about 15 A. For noise reduction the
current records acquired at the GBT are filtered using a digital low pass
filter (Butterworth, 2nd order) with a cut-off frequency of 250 kHz.
The effects of this filtering on the correlation of measured and LLS
inferred peak current is assumed to be insignificant as the sensor bandwidth
with an upper frequency of 350 kHz is in the same range. More details about
the Gaisberg measurement system can be found in Diendorfer et al. (2009b).
Measurement locations for (a) Austria, (b) Belgium
and (c) France.
Video and field recording system (VFRS)
To collect video and E-field data of individual lightning discharges, we are
employing a mobile video and field recording system (VFRS) consisting of a
flat plate antenna, an integrator, a fiber optic link and a camera. For the
E-field measurements a 12 bit digitizer with 5 MS s-1 sample rate and an
integrator with a decay time constant of 0.46 ms was used. The complete
system has an upper frequency cutoff of 1.3 MHz. The camera used had a
frame rate of 200 fps and an angle-of-view of about 50∘. The
complete recording system is described in detail in Schulz et al. (2005),
Schulz and Saba (2009) and Schulz and Diendorfer (2006).
Data
The lightning data used in this analysis were collected in three different
regions covered by the EUCLID LLS (see Fig. 1).
Region 1 (Fig. 3a) – During summer periods from 2009 to 2012, measurements
with the VFRS were carried out at various locations in Austria. In addition,
direct lightning current measurements have been performed at the instrumented GBT,
close to the city of Salzburg, since 1998.
Region 2 (Fig. 3b) – In August 2011 ground truth data were collected with the
VFRS in Belgium.
Region 3 (Fig. 3c) – In 2012, during the HyMeX project
(Ducrocy et al., 2013; Defer et al., 2015) ground truth data were collected with the VFRS in southern France and
in 2013 a separate measurement campaign was organized in the north of France.
The measurement locations for those regions are given in Fig. 3. In Fig. 3a
the location of the GBT in Austria is especially indicated. At the GBT a
total of 513 flashes (498 negative and 15 bipolar) were recorded from 2005
to 2014, the vast majority of them being upward-initiated discharges. A total
of 161 out of the 513 flashes contain 675 return strokes which are used as ground
truth reference in this paper. The remaining 352 flashes to the tower
exhibit either an initial continuous current (ICC) only or an ICC with
superimposed pulses (ICC pulses). Those types of flashes occur solely in
upward-initiated lightning and are not representative of natural downward lightning.
With the VFRS we recorded 587 negative flashes in the three distinct regions
during 38 days and 156 positive flashes during 21 days (see Table 1). All
these recordings were carried out at 47 different locations shown in Fig. 3.
Unfortunately, no positive flashes were recorded in Belgium. Also no positive
flashes were recorded in France in 2013.
LA in Austria (Region 1), Belgium (Region 2) and in France
(Region 3) obtained from VFRS verified strokes which followed the same
channel as a previous stroke in the flash.
Austria BelgiumFrance 2009–20102012All201120122013AllN1191082272514143157Median LA (m)326157259600256909095 % LA (m)162915621660331573216651665Mean LA (m)5634305001207330375372ResultsLocation accuracy
For the analysis of the LLS, LA based on data from the GBT measurements
(Region 1) only data from flashes with negative return strokes (including
bipolar flashes) were used. For the period 2005–2014 the LLS located 469 out
of the 675 return strokes with a median and mean LA of 201 and 343 m
(95 % value 1258 m), respectively.
Figure 4 plots the moving median of the location error over the last
100 return strokes directly measured at the GBT. The time axis starts at
21 January 2005 because on that day the first strike to the GBT during the period
of investigation was recorded. The graph starts on the 20 June 2007 because in
the period from 1 January 2005 to 20 June 2007, the 100 strokes occurred, which are
needed to start the moving median calculation. The last recorded stroke
during the period of interest occurred on 21 October 2014. The improvements in LA
due to all the changes in the network described in Sect. 2 are clearly
visible in Fig. 4. The moving median location error decreased from 317 m in
2007 to 89 m at the end of 2014. In Fig. 4 also the three major changes in
the network, the introduction of the new location algorithm (A), the
introduction of the sensor-based onset time calculation (B) and the
application of propagation correction (C) are indicated. After each change, a
notable increase of LA can be seen.
In order to determine the LA of the LLS with the VFRS data we have to search
for strokes that occurred in the same return stroke channel. Due to the
reason that almost no positive flashes with multiple strokes in the same
channel exist, the LA is determined with negative flashes only. The method
to estimate the LLS LA, based on multi-stroke flashes, is described in
Schulz et al. (2012) and Biagi et al. (2007). As mentioned in Schulz et al. (2012) this
method does not show any systematical location error. Table 2 shows the
resulting LA for Austria, Belgium and France.
Median location error over time calculated as moving median over the
last 100 return strokes measured at the GBT. The vertical lines show the
time when the new location algorithm was introduced (A), the introduction of
the sensor-based onset time calculation (B) and the application of the
propagation correction (C).
The LA obtained from all data in Table 2 is better for France compared to
Austria because the 2009–2010 measurements in Austria were performed before
the implementation of sensor-based onset time calculation, and for
measurements in 2012 the LLS locations were calculated without propagation corrections.
Median semi major axis for the whole EUCLID network for (a) period
2005–2014 and (b) for 2014 only. The black polygon shows the region for the
data analysis in Poelman et al. (2016). The red line represents the 0.5 km
contour line of median semi major axis.
(a) Flash DE and (b) stroke DE determined for negative
return strokes at the GBT (2005–2014).
Figure 5 shows the LLS provided median length of the semi major axis of the
confidence ellipse on a 50 km × 50 km grid for the period 2005–2014
(Fig. 5a) and for the year 2014 only (Fig. 5b). The semi major axis of the
confidence ellipse is a measure closely related to the actual location
accuracy (Diendorfer et al., 2014). The calculation of the
error ellipse is based on the assumption that the time error and angle
errors are following a Gaussian distribution. The parameters used for the
calculation, namely the standard deviations of the time, and angle error,
are determined regularly for each individual sensor. In this figure the
contour line for 0.5 km semi major axis (red line) and the region for the
data analyses (black polygon) in the paper Poelman et
al. (2016) are displayed. The color coding in Fig. 5 is not discrete but
linearly interpolated from blue (0.1 km) till red (2.0 km or greater).
Figure 5a shows the median LA over the 10 year period and should give the reader a
possibility to assess the LA of the data used in the paper
Poelman et al. (2016). Of course the actual performance
of the network regarding LA is much better in 2014 and this can be seen in
Fig. 5b. A significant improvement can be seen for the 2014 median semi
major axis except for a small region in the east of the network (east
Slovakia and south-east Poland). This degrade of the network is related to
temporary outage of one sensor in Poland and one in Hungary in 2014. In the
three regions of the video and E-field measurements, the overall median semi
major axis is 0.1 km and fits well to the latest measured location
accuracies at the GBT (see Fig. 4) and in France (see Table 2). Due to the
reason that the discretization of the error ellipse given by the location
algorithm was 100 m up to the end of 2014, a median value of 0.1 km is the
lowest possible value we can obtain. Since beginning of 2015 the semi major
axis is given by the central analyzer in 10 m steps. It can be seen in
Fig. 5b that the estimated location accuracy is better than 500 m within the
majority of the network covered area in 2014.
Detection efficiency
In general it can be shown that the flash/stroke DE increases with
increasing peak current (see also Fig. 6a and b). Typically the stroke
DE is always lower than the corresponding flash DE because in order to
detect a flash it is sufficient to detect one out of several strokes in a
multi-stroke flash. The main focus of this paper is on the performance
validation of an LLS based on ground truth data. We do not present any
spatial flash/stroke DE maps because for this task we would have to use a so
called DE model. Model-based DE is still idealized as it does not consider,
e.g., any temporary sensor outages due to communication problems.
Nevertheless the stroke/flash DE of any network has some spatial
variability. The analyses in the companion paper Poelman
et al. (2016) are based on flashes because flash DE is less sensitive to
network performance and especially larger sensor baselines than stroke DE.
Using a reasonable multiplicity distribution it was shown by
Schulz et al. (2014a) that in a region of high flash DE a
change of stroke DE of ∼ 30 % results in a change of flash
DE by only ∼ 10 %.
We do not take intra-cloud/cloud-to-ground (IC/CG) classification into
account for the Gaisberg data validation because it is documented that the
measured field widths related to tower strikes are shorter than for strokes
to ground (Diendorfer et al., 2010). This may result in
additional misclassification compared to natural lightning and in an
underestimation of DE. However we take IC/CG classification into account for
video and E-field studies because in this case the data should not be biased
by any tower effect.
DE determined from GBT measurements
EUCLID flash DE based on the GBT measurements is shown to be greater than
96 % if one of the return strokes in a flash had a peak current greater
than 2 kA (Fig. 6a). Flash peak current in Fig. 6a is the peak current of
the largest stroke in the flash. All flashes containing at least one stroke
with a peak current greater than 10 kA were detected (DE = 100 %). For
strokes to the GBT with peak currents greater than 2 kA, the stroke DE is
70 % (Fig. 6b).
One has to keep in mind that the analysis of the GBT measurements is made
for negative subsequent strokes in upward-initiated flashes only (no first
stroke data are available from tower measurements), and therefore the stroke
DE mentioned above is a DE for subsequent strokes. A higher average
multiplicity in tower-initiated lightning than in natural downward lightning
would result in a bias of the DE to higher values, because more strokes in a
flash increase the probability of detection, as a flash is detected if at
least one of all the strokes is detected. From the current records at the
GBT we have determined an average multiplicity of 4.3 return strokes per
flash for the period 2005–2014. Figure 7 shows the histogram of the number
of return strokes for negative flashes at the GBT. This value is similar to
the average multiplicity of three to five strokes per flash observed in natural
lightning (CIGRE Report 549, 2013), and therefore we do not expect
any bias of the flash DE related to multiplicity. Nevertheless, taking into
account that first strokes in natural downward lightning normally have
greater peak currents than subsequent strokes, the determined overall flash
DE of 96 % (in Fig. 6a) should be interpreted as a lower limit for the DE
of EUCLID for natural downward lightning in the same area.
Flash and stroke DEs determined from VFRS data. The number of
flashes/strokes recorded during each of the campaigns is given in the
parenthesis. Median peak current is given for strokes used in this analysis.
Median stroke peak Flash DE Stroke DE current PositiveNegativePositiveNegativePositiveNegativeAustria97 %98 %92 %84 %34 kA-12 kA(Region 1)(109)(271)(119)(928)Belgium–100 %–84 %–-18 kA(Region 2)(57)(210)France87 %93 %84 %89 %46 kA-16 kA(Region 3)(47)(259)(56)(833)
Histogram of number of return strokes in negative flashes to the GBT
(2005–2014), sample size N= 150.
DE determined from VFRS records
Due to the reason that the DE did not vary considerably in Austria from 2009
to 2012 and in France in 2012 and in 2013, we present only the average DEs
for all the years where data were recorded with the VFRS. The criteria used
in Table 3 to determine whether a stroke was detected by the LLS or not are
quite strict because not only the stroke location has to be provided with
certain quality criteria (χ2< 10 and major axis of the
confidence ellipse smaller than 5 km) but also the stroke classification
either as cloud-to-ground (CG) or as intra-cloud (IC) has to be correct.
As we have discussed before, the DE depends on peak current, and therefore we
present in Table 3 also the median peak current for negative and positive
strokes analyzed in the three regions. The median values are based on LLS-estimated peak currents.
Peak current estimates
The EUCLID peak current estimates are compared with the direct current
measurements of strokes to the GBT only because no information regarding
peak current is available from the VFRS data records. In Fig. 8 we have
plotted the EUCLID-estimated peak current IEUCLID vs. the directly
measured peak current IGB at the GBT. Ideally all data points should
line up on the diagonal (solid black line). Based on the transmission line
model (TLM) and the conversion of peak field to peak current used in the
EUCLID network, this line corresponds to a return stroke speed of
vLLS= 1.2 × 108 m s-1. Due to different return stroke speeds and
propagation paths from the tower to the LLS sensors, the resulting EUCLID
peak current estimates scatter around this line. Nevertheless, the overall
accuracy seems to be reasonable. It is commonly agreed that most of the
observed scatter around a return stroke speed used for the estimation of
stroke peak currents (black line in Fig. 8 corresponding to v= 1.2 × 108 m s-1)
is caused by the stroke by stroke variation of the return stroke speed
(Rakov, 2007). In Fig. 8 the blue, green and red lines represent
the peak current what EUCLID would report when we assume different return
stroke speeds vTLM for the TLM based peak field calculations.
It is interesting to see that basically all strokes are below the green line
for a return stroke speed of 1.5 ×vLLS equal to about 2/3 of speed of
light and above the red line representing a return stroke speed of 0.5 ×vLLS.
Figure 9a shows the histogram of the signed EUCLID peak current estimation
error as a percentage of the measured peak current at the GBT for 464 return
strokes. The arithmetic mean (AM) and the median are 3 and 4 %,
respectively. When we calculate the absolute values of the EUCLID peak
current estimation as percentage, we determine an AM and a median value of
|ΔI%| of 19 and 18 %, respectively (Fig. 9b).
EUCLID peak current estimates plotted vs. directly measured
stroke peak currents at the GBT during the time period 2005–2014. Blue,
green, black and yellow lines represent the EUCLID peak currents for
different return stroke speed and assuming TLM.
Histograms of (a) signed and (b) absolute EUCLID peak current
estimation errors, given as a percentage of the directly measured GBT
current (ΔI% = ((IEUCLID-IGB)/IGB) × 100
for 464 return strokes in 2005–2014.
Discussion
LA of the EUCLID network was determined from GBT measurements for negative
subsequent strokes only. Furthermore we cannot obtain any information
regarding the LA of positive flashes from VFRS measurements because positive
flashes with subsequent strokes in the same channel are rare. Nevertheless,
we do not see any reason why the LA for negative first strokes and positive
flashes should be different from the validated LA of negative subsequent strokes.
In case of a tower strike the injected current pulses propagating along the
tower to ground contribute to the total electromagnetic field radiated by
the lightning strokes. Compared to natural lightning strikes to ground, when
the lightning channel is often tortuous and branched, the tower is
completely straight, and therefore the resulting electromagnetic fields
radiated from the tower are probably more suitable to be detected by LLS
sensors. As a result, the estimated LA of an LLS based on lightning strikes
to towers is expected to be somewhat better than that for natural lightning.
On the other hand, the LLS location error determined from video data of
strokes in the same channel is an upper limit because the return stroke
channel is not always seen all the way down to the ground strike point of
each return stroke (Biagi et al., 2007). Keeping in mind those specific limitations of tower
measurements and VFRS recordings, the agreement between LA determined from
GBT data and VFRS data in Austria is almost perfect.
Further, we observed a continuous improvement of the LA of the EUCLID
network in recent years due to the implementation of several
technology improvements (soft- and hardware) summarized in Sect. 2. All
these improvements result in an actual median LA of 89 m for the last
100 strokes recorded at the GBT.
The estimated LA in Belgium was relatively poor in 2011 because during this
time there were still several sensors of outdated technology operated around
Belgium which had some negative effects on the LA. Since that time, these
old sensors installed around Belgium have been upgraded to the newest sensor technology.
The spatial LA estimation by use of the semi major axis of the 50 %
confidence ellipses is an appropriate tool to estimate the LA for larger
areas. This LA estimation based on real LLS data depends only on the
configured standard deviations of the time and angle measurements of each
sensor. If those parameters are derived and configured correctly the
resulting median LA for certain regions is a real measure of the LA in this
region. This was also confirmed with data from the GBT measurements in
Diendorfer et al. (2014). The analysis in this paper also
shows that the LA determined from ground truth data is in the same range as
the LA given by the major axis of the 50 % confidence ellipses for the
same region. This gives us the confidence to claim that the LA determined
from the distribution of the 50 % confidence ellipses (see Fig. 5b) is
representative for the real LA and hence the LA for the EUCLID network is
better than 500 m in the majority of the network.
The DE of 96 and 70 % for negative flashes and strokes,
respectively, determined from data to the GBT is in good agreement with the
DE determined from VFRS data in Austria (98 and 84 % for flashes and
strokes, respectively), considering that the DE from GBT data is based on
subsequent strokes only, and first strokes normally exhibit peak currents
greater than subsequent strokes. Negative flashes at the GBT exhibit a mean
multiplicity of 4.3 strokes per flash which is in the range of
multiplicities reported for natural negative downward flashes of 3 to 5
(CIGRE Report 549, 2013). This is important because the
multiplicity has a strong influence on the flash DE.
The lower DE for negative flashes in France compared to Austria is a result
of a temporary outage of a nearby sensor during the September 2012
measurements campaign. During this time period, nine single-stroke flashes
were missed. The low DE for positive flashes is caused by the very strict
criteria applied for the analysis, when we rate misclassified strokes as not
being detected by the LLS. Eight positive CG strokes were actually located
by EUCLID but misclassified as IC (five single-stroke flashes). In fact only
one positive flash was not detected at all.
LLS tend to overestimate the peak current of strokes to so-called
electrically tall towers (Pavanello et al., 2009). A
tower is called electrically tall when the rise time of the lightning
current is smaller than the current wave propagation time along the tower,
and therefore, the current injected into the tower top reaches its peak
before the arrival of any ground reflections. Correction factors have been
derived based on model calculations taking into account multiple reflections
of the lightning current pulse at ground level and at the top of the tower
(Baba and Rakov, 2007; Bermúdez et al., 2005). Based on
triggered lightning data for the US, NLDN peak current errors with an AM and
median of -5.6 and -5 %, respectively, are reported
(Mallick et al., 2014). Contrary to the
NLDN, peak current errors in this paper are positive (AM and the median
3 and 4 % respectively) which means that the EUCLID LLS
overestimates the peak current compared to the NLDN. This slight overestimation of lightning to the GBT compared to the triggered lightning
current measurements at Camp Blanding could possibly be related to some
tower enhancement described above but this enhancement is still much smaller
than observed at electrically tall towers, e.g., the 553 m tall CN Tower.
The results presented in this paper are assumed to be representative for the
performance of the EUCLID network in other regions with similar sensor
baseline and sensor technology.
Acknowledgements
The authors are grateful to the support from all the EUCLID members. The
authors further thank Christian Vergeiner who recorded most of the video and
E-field data in Austria and Hannes Pichler who is the master behind the
Gaisberg measurements. We also thank the anonymous reviewers for their very
helpful comments.
Edited by: A. Mugnai
Reviewed by: K. Cummins and one anonymous referee
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