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Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing
Accepted Manuscript Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing
Abhay Srivastava, Ye Tian, Xiushu Qie, Dongfang Wang, Zhuling Sun, Shanfeng Yuan, Wang Yu, Zhixiong Chen, Wenjing Xu, Hongbo Zhang, Rubin Jiang, Debin Su PII: DOI: Reference:
S0169-8095(17)30217-X doi: 10.1016/j.atmosres.2017.06.026 ATMOS 3990
To appear in:
Atmospheric Research
Received date: Revised date: Accepted date:
21 February 2017 21 June 2017 22 June 2017
Please cite this article as: Abhay Srivastava, Ye Tian, Xiushu Qie, Dongfang Wang, Zhuling Sun, Shanfeng Yuan, Wang Yu, Zhixiong Chen, Wenjing Xu, Hongbo Zhang, Rubin Jiang, Debin Su , Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing, Atmospheric Research (2017), doi: 10.1016/j.atmosres.2017.06.026
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ACCEPTED MANUSCRIPT
Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing Abhay Srivastava1, Ye Tian1, 4, Xiushu Qie1, 3, 4, Dongfang Wang1, 4, Zhuling Sun1, Shanfeng Yuan1, 4, Wang Yu5, Zhixiong Chen1, 4, Wenjing Xu2, Hongbo Zhang1, 4, Rubin Jiang1, 3,
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Debin Su2
1. Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO),
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Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, 100029
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2. Institute of Urban Meteorology, China Meteorological Administration, Beijing, China, 100089 3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,
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Nanjing University of information Science & Technology, Nanjing, China, 21004
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4. College of Earth Science, University of Chinese Academy of Sciences, Beijing, China
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5. Wuhan NARI Limited Company, State Grid Electric Power Research Institute, Wuhan, China
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Corresponding author: Xiushu Qie ([email protected])
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ACCEPTED MANUSCRIPT Abstract The performances of Beijing Lightning NETwork (BLNET) operated in Beijing-TianjinHebei urban cluster area have been evaluated in terms of detection efficiency and relative location accuracy. A self-reference method has been used to show the detection efficiency of BLNET, for which fast antenna waveforms have been manually examined. Based on the fast
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antenna verification, the average detection efficiency of BLNET is 97.4% for intracloud (IC)
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flashes, 73.9 % for cloud-to-ground (CG) flashes and 93.2% for the total flashes. Result suggests
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the CG detection of regional dense network is highly precise when the thunderstorm passes over the network; however it changes day to day when the thunderstorms are outside the network. Further, the CG stroke data from three different lightning location networks across Beijing are
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compared. The relative detection efficiency of World Wide Lightning Location Network (WWLLN) and Chinese Meteorology Administration - Lightning Detection Network (CMA-
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LDN, also known as ADTD) are approximately 12.4% (16.8%) and 36.5% (49.4%), respectively, comparing with fast antenna (BLNET). The location of BLNET is in middle, while WWLLN
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and CMA-LDN average locations are southeast and northwest, respectively. Finally, the IC pulses and CG return stroke pulses have been compared with the S-band Doppler radar. This
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type of study is useful to know the approximate situation in a region and improve the performance of lightning location networks in the absence of ground truth. Two lightning flashes
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and 250 m, respectively.
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occurred on tower in the coverage of BLNET show that the horizontal location error was 52.9 m
Key Words: Lightning location network, intracloud flashes, cloud-to-ground flashes, location
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accuracy, detection efficiency.
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ACCEPTED MANUSCRIPT 1. Introduction The importance of atmospheric electricity and lightning physics has been increasingly recognized year by year (Qie et al., 2015). Lightning is a kind of long-distance electrical discharge and usually occurs during thunderstorms. Lightning location technique aiming to locate the lightning striking point or mapping the lightning discharges is a fundamental way to
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study lightning physics. It is also important in the nowcasting and warning of severe convective
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weather and lightning. For this purpose, many regional and worldwide lightning location
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network (LLN) have been developed based upon lightning radiation signals in the band of very low frequency (VLF) / low frequency (LF) (e.g., Cummins et al., 1998; Shao et al., 2006; Betz et al., 2009; Nag et al., 2011; Novák et al., 2011; Bitzer et al., 2013; Taszarek et al., 2015; Y. Wang
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et al., 2016) and very high frequency (VHF) (e.g., Rison et al., 1999; Zhang et al., 2010). Different principal and mathematical algorithms, such as time of arrival (TOA) and magnetic
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direction finding (MDF) combined detection technologies (e.g., Cummins et al., 1998; Nag et al., 2011) and TOA technologies (e.g., Rison et al., 1999; Betz et al., 2009; Zhang et al., 2010), have
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been used in the lightning detection and location techniques. One of the key issues with all developed LLNs is to know the performance of the network, which is required to show in terms
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of detection efficiency (DE) and location accuracy (LA). The DE is defined as the ratio between truly occurred number of strokes (or flashes) and number of strokes (or flashes) located from
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LLN. LA is the location differences between originally occurred strokes and strokes located by the LLN (Idone et al., 1998a, 1998b; Nag et al., 2015). A good performance of a LLN can be
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considered as high DE, good LA, and low false detection.
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Ground truth, an optically observed lightning at a known place, is the best method used to obtain the performance of a LLN. However, very few ground truths can be recorded every year within the coverage of a given LLN. In the absence of a sufficient number of ground truths, performance assessment of a LLN using alternate methods can play a major role. However alternative methods have their own limitation and results can be inaccurate in some cases. Several alternate methods have been used to show DE and LA, such as relative performance with other LLN (e.g., Rodger et al., 2005; Jacobson et al., 2006; Abreu et al., 2010; Kuk et al., 2010; Pohjola and Mäkelä, 2013), self-reference performance from fast antenna data (e.g., Bitzer et al., 2013; Zhu et al., 2016), comparison with radar reflectivity or cloud images observed from space 3
ACCEPTED MANUSCRIPT (e.g., Shao et al., 2006; Liu et al., 2011; F. Wang et al., 2016) and statistical method by subgrouping of LLN (e.g. Chen et al, 2013) or comparing relative results between the human observation and LLN for the known DE and LA (Czernecki et al., 2016). Recently, a nonconventional method was used to know the performance of LLN in which lightning strikes on trees was considered as ground truth; unfortunately this method is labor intensive (Mäkelä et al.,
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2016).
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Several studies have been conducted in different region of the world between regional
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LLN and World Wide Lightning Location Network (WWLLN) and suggested relative detection efficiency (RDE) of WWLLN depend on return stroke (RS) peak current. It is noticed that the RDE of WWLLN with local LLN in Brazil, Kattron system in Australia, Los Alamos Sferic
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Array (LASA) in Florida, New Zealand Lightning Detection Network (NZLDN), National Lightning Detection Network (NLDN) in USA and Canadian Lightning Detection Network
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(CLDN) are 0.3%, ~13%, 1%-4%, 1%-50 %, 2%-35%, and 2%-75% , respectively and relative location accuracy (RLA) are ranging from 5-15 km (Lay et al., 2004; Rodger et al., 2004, 2005,
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2006; Jacobson et al., 2006; Abarca et al., 2010; Abreu et al., 2010). Similarly Global Lightning Dataset 360 (GLD360) and European Cooperation for Lightning Detection (EUCLID) have been
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compared in four regions of Europe and suggested the RDE of GLD360 depend on RS peak current from 10% and sharply increases to more than 100% above 15 kA and RLA of GLD360
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was shown a few kilometers from EUCLID (Pohjola and Mäkelä, 2013). The RDE of long range LLN are also dependent on ionospheric region that varies between day and night (Abreu et al.,
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2010; Poelman et al., 2013). Further, Korean Meteorology Administration Lightning Detection Network (KLDN) and the Korea Aerospace Research Institute Total Lightning Detection System
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(KARITLDS) have been compared in two regions of South Korea with the help of shared flashes and shown the performance using time range correlation method. RDE was ranging from 47%77% for KARITLDS and 27%-61% for KLDN. However, it was not clearly mentioned that why the relative performances of these two regional LLNs was different in day to day (Kuk et al., 2010). Beijing Lightning NETwork (BLNET), a regional multi-frequency-band lightning detection and location network, is developed for both research and operational purposes in Beijing-Tianjin-Hebei urban cluster area. Y. Wang et al. (2016) introduced the performance of 4
ACCEPTED MANUSCRIPT BLNET using the LF source locations and compared with the radar echo. They estimated LA using Monte Carlo simulation and suggested that the DE of BLNET is an important index in future studies. The limited number of camera observation has pushed to use an alternative method to evaluate the performance of BLNET. In this article, the DE of BLNET is obtained by comparing with fast antenna and LA is shown by comparing with radar and limited tower flashes. The events of fast antenna are taken from the entire sensor network as a reference and the flashes
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are checked manually from every fast antenna. In addition, the relative performance of other two
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lightning location networks across Beijing, WWLLN and Chinese Meteorology Administration
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Lightning Detection Network (CMA-LDN, also known as ADTD), are shown based on CG stroke location. Based on the performance of the fast antenna and BLNET, the relative
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evaluation between BLNET-WWLLN and BLNET-CMA-LDN has been assessed over Beijing. 2. Data and Method
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The BLNET has been developed for locating both intracloud (IC) and cloud-to-ground (CG) lightning, which have 16 stations covering East-West 110 km and North-South 120 km
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areas since 2015. Fig. 1 shows the sensor distribution of BLNET including the site name. DQS is the main observation station shown in Fig. 1. The Chan algorithm and Levenberg-Marquardt
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method is adopted jointly in the lightning location algorithm. Detailed descriptions of the
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network and location algorithm were presented by Y. Wang et al. (2016). CMA-LDN is operated by the Chinese Meteorological Administration (CMA) and is
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capable of detecting the CG lightning stroke throughout China. CMA-LDN works on the number of stations recorded the flash. If pulses are detected by two stations, location is obtained by the
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DF and if it is detected from three stations then location is obtained by the DF + TOA method, otherwise the location is obtained using the TOA method. This LLN claims 90% DE with 500 m LA of CG (Yao et al., 2013). The WWLLN locates real-time lightning stroke worldwide and is able to detect the CG and few strong IC flashes. WWLLN works on time of group arrival method and the DE and LA are changing with region. The aim of WWLLN is to achieve the DE ≥ 50% and the LA ≤ 10 km (Rodger et al., 2005), detailed descriptions are given by Abarca et al. (2010). Abreu et al. (2010) had shown a comparative performance of WWLLN in several regions. A detailed description on
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ACCEPTED MANUSCRIPT BLNET, CMA-LDN and WWLLN are shown in Table 1. The observation zone for this study is within 39° N - 41° N and 115.5° E - 117.5° E. Lightning flashes hitting to a 325-meter meteorological tower located about 900 m southwest of DQS station are used to the evaluation of the BLNET. A high-speed camera, Phantom Miro M310 is equipped at the DQS station with a very good view to the tower tip
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(Jiang et al., 2014; Z. Wang et al., 2016). Length of the record is 1 s with 200 ms pre-trigger time
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and 150,000 frames per second frame rate. The CMA S-band Doppler weather radar at Beijing
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observatory located in Nanyuan is also used in this study. 2.2 Methodology
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BLNET detects and locates both IC and CG radiation pulses in 2 dimensions (2D) in real time for operational severe thunderstorm warning and map IC and CG lightning discharge in 3D
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for post research (Y. Wang et al., 2016). Here we use 2D location results to evaluate the BLNET performances. Firstly, the location results for IC and CG pulses are grouped in flashes. Grouping
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criteria for this study are 10 km and 500 ms, which is based on the spatial and time difference of pulses. All pulses up to 500 ms before and after the first CG stroke are considered part of the CG
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flash. Therefore, in this evaluation preliminary breakdown (PB) pulses are integrated into CG flash.
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In the next step, we manually examined the waveform from all fast antenna sensors and extracted IC and CG flashes. To reduce noise errors, all the waveforms have been excluded
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which triggered only a sensor or continuously triggered a particular sensor. A sharp rise with a slow decay in waveforms is considered as a CG stroke and others which do not meet these
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criteria are considered as IC pulse. In Fig. 2, 3 IC pulses are shown. Here PB is shown as C1 and IC Pulses as C2 and C3, where C2 gets 252 ms and C3 at 331 ms. Fig. 3 shows PB and 4 CG strokes. PB on 200 ms and followed by 4 RS, R1, R2, R3 and R4, which are shown at 270.9 ms, 315.1 ms, 522.7 ms and 599.4 ms, respectively. First two RS, R1 and R2 are very near and R2 is stronger than R1. R3 have more IC pulses and R4 with a narrow time duration. IC and CG flashes from the fast antenna waveform have been identified manually using the above criteria. It would not be possible that all sensors are recording an identical flash. So, we classify these flashes using following criteria to compare with the location results. If two or more sensors
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ACCEPTED MANUSCRIPT receive waveform and two of them have CG, then we will accept it as a CG flash and all others are IC flash. Five different thunderstorm days have been used to show the DE. The selection of these days has been done on the basis of different direction and strength of the thunderstorm. The DE of BLNET is shown using following formulas. (1.a)
DECG = CG flashes located by BLNET/ CG flashes from fast antenna
(1.b)
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DEIC = IC flashes located by BLNET/ IC flashes from fast antenna
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The fast antenna and BLNET are considered as reference, separately, and performance of the WWLLN and the CMA-LDN, is evaluated in terms of RDE and RLA. To choose fast antenna as a reference in place of BLNET is to reduce overestimation or underestimation
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performance of other LLNs. In this discussion following formulas have been used: (2.a)
RDEFA = (LLNCG event /BLNET CG event) ∗ DECG
(2.b)
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RDE BLNET = LLNCG event /BLNET CG event
Where RDEBLNET is relative detection efficiency when BLNET as reference; RDEFA is relative
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detection efficiency when fast antenna as a reference; DECG is CG detection efficiency of BLNET.
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For this part, 19 thunderstorm days during summer 2015 and 2016 have been considered. The selection of these days has been done on the basis of available data from the three LLNs.
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Shared events among these three LLNs and then shared events between BLNET-CMA-LDN and BLNET-WWLLN have determined. To determine the shared events, time difference and spatial
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separation between LLNs strokes are 0.5 ms and 30 km respectively. Any CG stroke in this
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frame has been considered as shared events between LLNs. 3. Results and Discussion: 3.1 BLNET IC and CG detection efficiency using fast antenna CG strokes are identified from all the lightning flashes on the basis of pulses, which has the obvious wave characteristics of RS, as shown in Fig. 3. The DE for IC flash and CG flash have been calculated using equation 1a and 1b. In every thunderstorm, the performance has evaluated separately and the overall DE based on various days is shown in Table 2. The average DE of BLNET for total lightning, IC and CG are 93.2%, 97.4%, and 73.9%, respectively.
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ACCEPTED MANUSCRIPT The best performance was found on 17 Jul 2015. However, average performance during 2015 was worse as compared to that of 2016. The CG flash DE ranging from 60.6% to 87.9% depend on geographical condition and local conditions of the thunderstorm. It could be day to day limitation of dense LLN that depend on various reasons: (i) Y. Wang et al. (2016) suggested that the high sampling rate and the long record length in the acquisition system makes the buffer time relatively long. In addition, the background noise
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of Beijing varies day to day that depends on local circumstance. It makes impractical to reduce
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triggering threshold in the acquisition system.
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(ii) On 15 July 2015, a multi-cell thunderstorm was developed and most of the time it was outside of the network, which moved eastward from northwest of Beijing, then reached northeast
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of Beijing. On 7 August 2015, multi-cell, then a squall line has been shown and the size of the storm is bigger than the size of one side of the network that moved from northwest towards
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southeast. When storm was heading towards BLNET, another cell was built outside of BLNET as shown in Fig. 4 and many flashes were located in both cells. It is possible that at an instant
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time, some sensors were triggered due to flashes in cell A and few sensors triggered due to flashes in cell B. In this situation, the total number of triggered sensors may be sufficient but
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insufficient to locate the flash in cell A or cell B. On 11 May 2016 and 28 July 2016, the thunderstorm developed from the north of the network and entered inside the LLN. On 17 July
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2015, the high reflectivity echo moved inside the network and most of the time it was near to the network. It is obvious that the number of flashes located by LLN increases when the
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thunderstorm reaches near the LLN. The DE of this type of dense network would depend on the direction of day to day thunderstorm. In the days when a thunderstorm was over the BLNET, it
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has higher DE.
(iii) When the flashes occur far from the network, IC has less strength to trigger enough sensors but CG may trigger, because there is inequality between RS currents and weak IC discharge currents. However, when thunderstorm reaches near to the network, sufficient number of sensors are triggered and locates the pulses. These are the reasons that CG has a range of DE but IC has almost similar DE as shown in Table 2. The results are similar to LASA sferics, where CG event are detected and located in a relatively small number. It was also suggested that the number of detectable lightning event decreases with distance (Shao et al., 2006). It can be
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ACCEPTED MANUSCRIPT suggested that distinct phase and direction of the thunderstorm will affect the DE of dense network. 3.2 Relative performance using BLNET and fast antenna as reference The shared strokes have been compared among the three LLNs across Beijing region to show the RDE and RLA. To compare WWLLN, the criteria were temporal difference ≤ 3 ms and
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spatial separation ≤ 50 km (Lay et al., 2004; Rodger et al., 2005). Jacobson changed the criteria
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and took as temporal difference ≤ 1 ms and spatial separation ≤ 100 km (Jacobson et al., 2006). It was proposed that if the temporal difference reduce ≤ 0.5 ms then there is no need to consider
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the spatial separation (Abreu et al., 2010; Rodger et al., 2006). In a study, spatial separation ≤ 20 km was primary parameter for the shared event (Abarca et al., 2010). In a similar study between
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GLD360 and EUCLID, the temporal difference was 0.1 ms and it was suggested for the small region this temporal difference is enough (Pohjola and Mäkelä, 2013). Further, a comparison was
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performed between regional, subcontinental, and long range LLN over Benelux and France, and 1 s time window with ≤ 15 km spatial separation were chosen (Poelman et al., 2013). These
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conclusions suggested that consideration of spatial separation is not necessary for small area with a small time difference. It this study, analysis area is large enough and spatial separation has
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been considered.
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3.2.1 Relative detection efficiency (RDE) of CMA-LDN and WWLLN Fast antenna data and lightning location data from BLNET are considered as two
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references to determine the RDE of the two different networks WWLLN and CMA-LDN. BLNET, CMA-LDN and WWLLN located 27407, 13548 and 4623 strokes respectively in 19
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thunderstorms. The RDE shown in Table 3 is the outline of real DE by equation 2a and 2b. For every LLN, some flashes are located out of the observation range. These flashes would create 12 % error in entire results. The overall performance of CMA-LDN and WWLLN in Beijing area is approximately 12.4% (16.8%) and 36.5% (49.4%), respectively, with reference as fast antenna (as BLNET). BLNET is well performing and it detects a higher percentage of CG. CMA-LDN and WWLLN are detecting only strong RS and depend on RS peak current. They have few sensors in the observational area and unable to locate most of the CG that can easily located from BLNET.
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ACCEPTED MANUSCRIPT 3.2.2 Relative location accuracy (RLA) for three networks using shared events Here, the time window is chosen as ± 0.5 ms and space window as ± 30 km. Table 4 shows the shared strokes during the study period. It can be seen that the number of shared strokes between BLNET - WWLLN is significantly lower than BLNET - CMA-LDN. It is found that around 20% events have been shared from each network out of total events from that LLN. Fig. 5a demonstrates the comparison among these LLNs. The size of open and close
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circle is varying for different shared stroke. This shows that the same stroke is located at
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different places from CMA-LDN and WWLLN. Space distribution of WWLLN and CMA-LDN
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is found to the north (451 m) - east (61 m) and north (1319 m) - west (31 m) respectively from the BLNET. WWLLN has a higher number of shared events between 0-100 µs as compared to
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CMA-LDN shown in Fig. 5b. The shared events among these LLNs are fewer therefore studies between two LLNs have been included. BLNET is considered as reference and space distribution
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of WWLLN and CMA-LDN are demonstrated in Fig. 6a and b, and shared event is shown in Table 4. In a separate comparison, the average space differences from BLNET to WWLLN are
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south (377 m) - east (2744 m) and BLNET to CMA-LDN are north (3584 m) - west (74 m). Space differences for most of strokes are less than 10 km between WWLLN and BLNET. For
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CMA-LDN, the space differences with BLNET are scattered approximately uniformly up to 30 km. Time differences in separate matched strokes are shown in Fig. 7a and b. For WWLLN,
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higher numbers of matched strokes are found in 0-100 µs; whereas the number of matched strokes is approximately similar to 0-100 µs and 100-200 µs for CMA-LDN.
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Based on the results, it can be mentioned that the WWLLN performance is good as considering its global coverage and VLF working frequency band, and it locates southeast from
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CMA-LDN and BLNET. CMA-LDN performs northwest from BLNET. Even though the RLA of BLNET is found between CMA-LDN and WWLLN, the shared event is less than expected. The performances of shared stroke of WWLLN have better relation with BLNET than CMALDN. 3.3 Comparison of three LLNs with radar echo and tower lightning RLA and RDE can show only relative performance and cannot be believed with 100% confidence. So, a comparison has been shown between these three networks as BLNET, CMALDN and WWLLN with radar echo and tower lightning. 10
ACCEPTED MANUSCRIPT 3.3.1 Comparison of three LLNs with radar echo Fig. 4 shows radar reflectivity during six-minute intervals on 7 August 2015 from 09:30:00 to 09:36:00 UTC and all IC pulses and CG strokes in the same period are superimposed on it. In this figure, the IC pulses are 1019 for BLNET and CG strokes for BLNET, CMA-LDN and WWLLN are 16, 12 and 9 respectively. In Fig. 4, there were two cells as cell B being inside the network and cell A being outside. They were moving toward the network and become a
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squall line late on. Most of the CG strokes and IC pulses from the BLNET are located in the cell
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B. Few strokes from CMA-LDN and BLNET are located at northeast and missed from WWLLN
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or may be located in northwest. Out of these, six strokes have been shared among LLN shown in enlarged part of Fig. 4. In cell B, the shared stroke location from WWLLN is on weak
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reflectivity and location from CMA-LDN and BLNET is on reflectivity above 50 dBz. In cell A, all LLNs locate the shared strokes on around 30-35 dBz. Both cells demonstrate the BLNET
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location is at middle whereas CMA-LDN and WWLLN are located northwest and southeast respectively from BLNET. This clearly represents the overall detection of BLNET is high in
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comparison to other LLNs and there is a good relation between IC pulses and CG strokes with the radar reflectivity.
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3.3.2 Comparison of three LLNs with tower lightning Tower lightning provides good opportunity to evaluate the performances of lightning
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network with its known location and can be regarded as ground truth. However, for a high tower of 325 m in this study, most of the tower lightning flashes are upward lightning with only weak
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initial continuing current stage (Jiang et al., 2014), and can’t be detected by enough sensors working in the megacity region with high radio noise background. Two tower lightning flashes
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with RS were recorded by both the high-speed camera and BLNET in 2014 and 2016. Location accuracy of BLNET is estimated by using the two tower lightning flashes. Location error of BLNET by using one lightning stroke to the tower tip on 11 May 2016 was 52.9 m. Another lightning stroke on 6 June 2014 was 250 m, as discussed by Y. Wang et al. (2016). At that time the device had time synchronization errors due to different types of GPS clock and some sensors were still on installation and upgrading. The tower lightning on 11 May 2016 as discussed above was also located by CMA-LDN at 889.9 m from tower. It is found that the location difference and time difference between BLNET- CMA-LDN are 837 m and 22.7 µs respectively. The tower lightning on 6 June 2014 11
ACCEPTED MANUSCRIPT was located by WWLLN at 7.3 km from the tower. Location difference and time difference between BLNET-WWLLN are 7.1 km and 50 ms respectively. Two tower lightning flashes are not sufficient to give the actual performance of the three networks, and more ground truths are still needed. 4. Conclusion
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In this work, performance of BLNET has been evaluated on the basis of self-reference
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methods. Lightning location data of five different days of the thunderstorm have been analyzed
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to show its DE. Based on fast antenna verification, the average DE of BLNET is 97.4% for IC, 73.9% for CG and 93.2% for the total lightning. CG stroke data for 19 thunderstorm days from three different LLNs were compared, and RDE and RLA have been shown for the 3 LLNs across
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Beijing. The RDE of WWLLN and CMA-LDN have approximately 12.4% (16.8%) and 36.5% (49.4%) respectively, in the context of fast antenna (BLNET). The location of BLNET is in
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middle, while WWLLN and CMA-LDN average location are southeast and northwest, respectively when superimposing the lightning stroke locations on radar reflectivity, suggesting
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the performance of BLNET is better than WWLLN and CMA-LDN. BLNET have better DE and LA in the coverage of the network, and RDE and RLA are showing a confidence in favor to
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BLNET. This shows that BLNET has a high capability to detect IC flashes and can be used for thunderstorm tracking and warning with high confidence.
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Two tower lightning flashes occurred in the coverage of the BLNET shows that the horizontal location error is 52.9 m and 250 m, respectively. It is suggested that the location error
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from long range network could be up to few hundreds meters or few kilometers; however from dense network it can be achieved up to few tens meters. It is suggested that the DE of very dense
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network is accurate when the thunderstorm passes over the network, and it can be different day by day when the thunderstorms are outside the network. Acknowledgment The research was supported by National Key Basic Research Program of China (Grant No. 2014CB441401), Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-DQC007) and National Natural Science Foundation of China (Grant No. 41475002). The authors wish to thank the WWLLN (http://wwlln.net), collaboration among over 60 universities and institutions,
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ACCEPTED MANUSCRIPT for providing the lightning location data. The first author would like to thank Chinese Academy of Science for CAS-PIFI fellowship grant. References Abarca, S.F., Corbosiero, K.L., Galarneau, T.J., 2010. An evaluation of the Worldwide Lightning Location Network (WWLLN) using the National Lightning Detection Network
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ACCEPTED MANUSCRIPT characteristics and validation techniques. Earth Sp. Sci. 2 (4), 65–93. http://dx.doi.org/10.1002/2014EA000051. Novák, P., Kyznarová, H., 2011. Climatology of lightning in the Czech Republic. Atmos. Res. 100 (4), 318-333. https://doi.org/10.1016/j.atmosres.2010.08.022 Poelman, D.R., Honoré, F., Anderson, G., Pedeboy, S., Poelman, D.R., Honoré, F., Anderson, G., Pedeboy, S., 2013. Comparing a regional, subcontinental, and long-range lightning location
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ACCEPTED MANUSCRIPT Poland. Mon. Wea. Rev. 143(11), 4285-4304. https://doi.org/10.1175/MWR-D-15-0206.1. Wang, F., Qie, X., Liu, D., Shi, H., Srivastava, A., 2016. Lightning activity and its relationship with typhoon intensity and vertical wind shear for super typhoon Haiyan (1330). J. Meteorol. Res. 30 (1), 117–127. http://dx.doi.org/10.1007/s13351-016-4228-x. Wang, Y., X.Qie, D. Wang, , Liu, M., Su, D., Wang, Z., Liu, D., Wu, Z., Sun, Z., Tian, Y., 2016. Beijing Lightning Network (BLNET) and the observation on preliminary breakdown
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locating system based on time-of-arrival technique to study three-dimensional lightning discharge processes. Sci. China Earth Sci. 53 (4), 591–602.
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ACCEPTED MANUSCRIPT Figure Caption Fig. 1: Layout of BLNET sensors and corresponding sites name Fig. 2: IC waveforms from fast antenna sensor Fig. 3: CG waveforms from fast antenna sensor Fig. 4: The 2D lightning locations compared with the corresponding radar reflectivity including CG and IC pulses among 3 LLNs on 07 August 2015 during 09:30:00 to 09:36:00 UTC
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Fig. 5: Shared events with reference to BLNET. a. space difference, and b. time difference
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Fig. 6: Spatial distribution of shared events with reference to BLNET. a. WWLLN, and b. CMA-
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Fig. 7: Time difference of shared events with reference to BLNET. a. WWLLN, and b. CMA-
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WWLLN
CMA-LDN
BLNET
Range
World
China
Beijing
Agency
University of Washington & 50 Universities
Chinese Meteorology Administration
Institute of Atmospheric Physics, CAS
Put into operation
2004
2011
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Table 1: Comparative description of LLN in Beijing Area
Frequency
VLF
VLF/LF
Location
2D
2D
Technique
TOGA
TOA+DF
Detection
CG+IC without discrimination
Sensor
>60 approx
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2014
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VLF/LF, VHF 2D, 3D TOA
CG and IC with discrimination
>301 approx
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Fast Antenna
BLNET
DE of BLNET (%)
IC
CG
IC
IC
CG
Total
15 Jul 15
172
491
110
470
95.7
63.9
87.5
17 Jul 15
1713
3324
1507
3303
99.4
87.9
95.5
07 Aug 15
1767
12657
1071
12437
98.3
11 May 16
352
1706
275
1521
28 July 16
76
238
54
Total
4080
18416
3017
93.6
89.2
78.1
87.3
209
87.8
71.1
83.6
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73.9
93.2
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RDEBLNET
49.4%
16.8%
RDEFA
36.5%
12.4%
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CMA-LDN
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2754
BLNET -WWLLN
961
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ACCEPTED MANUSCRIPT Highlights (for review)
Self reference method to obtain DE of IC and CG based on waveform from the entire sensor used to locate lightning in BLNET. Comparison of regional, subcontinent and long range LLN with radar.
Performance of subcontinent and long range LLN using BLNET.
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Abhay Srivastava, Ye Tian, Xiushu Qie, Dongfang Wang, Zhuling Sun, Shanfeng Yuan, Wang Yu, Zhixiong Chen, Wenjing Xu, Hongbo Zhang, Rubin Jiang, Debin Su PII: DOI: Reference:
S0169-8095(17)30217-X doi: 10.1016/j.atmosres.2017.06.026 ATMOS 3990
To appear in:
Atmospheric Research
Received date: Revised date: Accepted date:
21 February 2017 21 June 2017 22 June 2017
Please cite this article as: Abhay Srivastava, Ye Tian, Xiushu Qie, Dongfang Wang, Zhuling Sun, Shanfeng Yuan, Wang Yu, Zhixiong Chen, Wenjing Xu, Hongbo Zhang, Rubin Jiang, Debin Su , Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing, Atmospheric Research (2017), doi: 10.1016/j.atmosres.2017.06.026
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Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing Abhay Srivastava1, Ye Tian1, 4, Xiushu Qie1, 3, 4, Dongfang Wang1, 4, Zhuling Sun1, Shanfeng Yuan1, 4, Wang Yu5, Zhixiong Chen1, 4, Wenjing Xu2, Hongbo Zhang1, 4, Rubin Jiang1, 3,
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Debin Su2
1. Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO),
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Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, 100029
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2. Institute of Urban Meteorology, China Meteorological Administration, Beijing, China, 100089 3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,
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Nanjing University of information Science & Technology, Nanjing, China, 21004
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4. College of Earth Science, University of Chinese Academy of Sciences, Beijing, China
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5. Wuhan NARI Limited Company, State Grid Electric Power Research Institute, Wuhan, China
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Corresponding author: Xiushu Qie ([email protected])
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ACCEPTED MANUSCRIPT Abstract The performances of Beijing Lightning NETwork (BLNET) operated in Beijing-TianjinHebei urban cluster area have been evaluated in terms of detection efficiency and relative location accuracy. A self-reference method has been used to show the detection efficiency of BLNET, for which fast antenna waveforms have been manually examined. Based on the fast
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antenna verification, the average detection efficiency of BLNET is 97.4% for intracloud (IC)
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flashes, 73.9 % for cloud-to-ground (CG) flashes and 93.2% for the total flashes. Result suggests
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the CG detection of regional dense network is highly precise when the thunderstorm passes over the network; however it changes day to day when the thunderstorms are outside the network. Further, the CG stroke data from three different lightning location networks across Beijing are
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compared. The relative detection efficiency of World Wide Lightning Location Network (WWLLN) and Chinese Meteorology Administration - Lightning Detection Network (CMA-
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LDN, also known as ADTD) are approximately 12.4% (16.8%) and 36.5% (49.4%), respectively, comparing with fast antenna (BLNET). The location of BLNET is in middle, while WWLLN
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and CMA-LDN average locations are southeast and northwest, respectively. Finally, the IC pulses and CG return stroke pulses have been compared with the S-band Doppler radar. This
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type of study is useful to know the approximate situation in a region and improve the performance of lightning location networks in the absence of ground truth. Two lightning flashes
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and 250 m, respectively.
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occurred on tower in the coverage of BLNET show that the horizontal location error was 52.9 m
Key Words: Lightning location network, intracloud flashes, cloud-to-ground flashes, location
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accuracy, detection efficiency.
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ACCEPTED MANUSCRIPT 1. Introduction The importance of atmospheric electricity and lightning physics has been increasingly recognized year by year (Qie et al., 2015). Lightning is a kind of long-distance electrical discharge and usually occurs during thunderstorms. Lightning location technique aiming to locate the lightning striking point or mapping the lightning discharges is a fundamental way to
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study lightning physics. It is also important in the nowcasting and warning of severe convective
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weather and lightning. For this purpose, many regional and worldwide lightning location
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network (LLN) have been developed based upon lightning radiation signals in the band of very low frequency (VLF) / low frequency (LF) (e.g., Cummins et al., 1998; Shao et al., 2006; Betz et al., 2009; Nag et al., 2011; Novák et al., 2011; Bitzer et al., 2013; Taszarek et al., 2015; Y. Wang
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et al., 2016) and very high frequency (VHF) (e.g., Rison et al., 1999; Zhang et al., 2010). Different principal and mathematical algorithms, such as time of arrival (TOA) and magnetic
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direction finding (MDF) combined detection technologies (e.g., Cummins et al., 1998; Nag et al., 2011) and TOA technologies (e.g., Rison et al., 1999; Betz et al., 2009; Zhang et al., 2010), have
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been used in the lightning detection and location techniques. One of the key issues with all developed LLNs is to know the performance of the network, which is required to show in terms
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of detection efficiency (DE) and location accuracy (LA). The DE is defined as the ratio between truly occurred number of strokes (or flashes) and number of strokes (or flashes) located from
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LLN. LA is the location differences between originally occurred strokes and strokes located by the LLN (Idone et al., 1998a, 1998b; Nag et al., 2015). A good performance of a LLN can be
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considered as high DE, good LA, and low false detection.
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Ground truth, an optically observed lightning at a known place, is the best method used to obtain the performance of a LLN. However, very few ground truths can be recorded every year within the coverage of a given LLN. In the absence of a sufficient number of ground truths, performance assessment of a LLN using alternate methods can play a major role. However alternative methods have their own limitation and results can be inaccurate in some cases. Several alternate methods have been used to show DE and LA, such as relative performance with other LLN (e.g., Rodger et al., 2005; Jacobson et al., 2006; Abreu et al., 2010; Kuk et al., 2010; Pohjola and Mäkelä, 2013), self-reference performance from fast antenna data (e.g., Bitzer et al., 2013; Zhu et al., 2016), comparison with radar reflectivity or cloud images observed from space 3
ACCEPTED MANUSCRIPT (e.g., Shao et al., 2006; Liu et al., 2011; F. Wang et al., 2016) and statistical method by subgrouping of LLN (e.g. Chen et al, 2013) or comparing relative results between the human observation and LLN for the known DE and LA (Czernecki et al., 2016). Recently, a nonconventional method was used to know the performance of LLN in which lightning strikes on trees was considered as ground truth; unfortunately this method is labor intensive (Mäkelä et al.,
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2016).
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Several studies have been conducted in different region of the world between regional
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LLN and World Wide Lightning Location Network (WWLLN) and suggested relative detection efficiency (RDE) of WWLLN depend on return stroke (RS) peak current. It is noticed that the RDE of WWLLN with local LLN in Brazil, Kattron system in Australia, Los Alamos Sferic
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Array (LASA) in Florida, New Zealand Lightning Detection Network (NZLDN), National Lightning Detection Network (NLDN) in USA and Canadian Lightning Detection Network
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(CLDN) are 0.3%, ~13%, 1%-4%, 1%-50 %, 2%-35%, and 2%-75% , respectively and relative location accuracy (RLA) are ranging from 5-15 km (Lay et al., 2004; Rodger et al., 2004, 2005,
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2006; Jacobson et al., 2006; Abarca et al., 2010; Abreu et al., 2010). Similarly Global Lightning Dataset 360 (GLD360) and European Cooperation for Lightning Detection (EUCLID) have been
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compared in four regions of Europe and suggested the RDE of GLD360 depend on RS peak current from 10% and sharply increases to more than 100% above 15 kA and RLA of GLD360
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was shown a few kilometers from EUCLID (Pohjola and Mäkelä, 2013). The RDE of long range LLN are also dependent on ionospheric region that varies between day and night (Abreu et al.,
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2010; Poelman et al., 2013). Further, Korean Meteorology Administration Lightning Detection Network (KLDN) and the Korea Aerospace Research Institute Total Lightning Detection System
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(KARITLDS) have been compared in two regions of South Korea with the help of shared flashes and shown the performance using time range correlation method. RDE was ranging from 47%77% for KARITLDS and 27%-61% for KLDN. However, it was not clearly mentioned that why the relative performances of these two regional LLNs was different in day to day (Kuk et al., 2010). Beijing Lightning NETwork (BLNET), a regional multi-frequency-band lightning detection and location network, is developed for both research and operational purposes in Beijing-Tianjin-Hebei urban cluster area. Y. Wang et al. (2016) introduced the performance of 4
ACCEPTED MANUSCRIPT BLNET using the LF source locations and compared with the radar echo. They estimated LA using Monte Carlo simulation and suggested that the DE of BLNET is an important index in future studies. The limited number of camera observation has pushed to use an alternative method to evaluate the performance of BLNET. In this article, the DE of BLNET is obtained by comparing with fast antenna and LA is shown by comparing with radar and limited tower flashes. The events of fast antenna are taken from the entire sensor network as a reference and the flashes
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are checked manually from every fast antenna. In addition, the relative performance of other two
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lightning location networks across Beijing, WWLLN and Chinese Meteorology Administration
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Lightning Detection Network (CMA-LDN, also known as ADTD), are shown based on CG stroke location. Based on the performance of the fast antenna and BLNET, the relative
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evaluation between BLNET-WWLLN and BLNET-CMA-LDN has been assessed over Beijing. 2. Data and Method
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The BLNET has been developed for locating both intracloud (IC) and cloud-to-ground (CG) lightning, which have 16 stations covering East-West 110 km and North-South 120 km
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areas since 2015. Fig. 1 shows the sensor distribution of BLNET including the site name. DQS is the main observation station shown in Fig. 1. The Chan algorithm and Levenberg-Marquardt
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method is adopted jointly in the lightning location algorithm. Detailed descriptions of the
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network and location algorithm were presented by Y. Wang et al. (2016). CMA-LDN is operated by the Chinese Meteorological Administration (CMA) and is
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capable of detecting the CG lightning stroke throughout China. CMA-LDN works on the number of stations recorded the flash. If pulses are detected by two stations, location is obtained by the
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DF and if it is detected from three stations then location is obtained by the DF + TOA method, otherwise the location is obtained using the TOA method. This LLN claims 90% DE with 500 m LA of CG (Yao et al., 2013). The WWLLN locates real-time lightning stroke worldwide and is able to detect the CG and few strong IC flashes. WWLLN works on time of group arrival method and the DE and LA are changing with region. The aim of WWLLN is to achieve the DE ≥ 50% and the LA ≤ 10 km (Rodger et al., 2005), detailed descriptions are given by Abarca et al. (2010). Abreu et al. (2010) had shown a comparative performance of WWLLN in several regions. A detailed description on
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ACCEPTED MANUSCRIPT BLNET, CMA-LDN and WWLLN are shown in Table 1. The observation zone for this study is within 39° N - 41° N and 115.5° E - 117.5° E. Lightning flashes hitting to a 325-meter meteorological tower located about 900 m southwest of DQS station are used to the evaluation of the BLNET. A high-speed camera, Phantom Miro M310 is equipped at the DQS station with a very good view to the tower tip
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(Jiang et al., 2014; Z. Wang et al., 2016). Length of the record is 1 s with 200 ms pre-trigger time
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and 150,000 frames per second frame rate. The CMA S-band Doppler weather radar at Beijing
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observatory located in Nanyuan is also used in this study. 2.2 Methodology
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BLNET detects and locates both IC and CG radiation pulses in 2 dimensions (2D) in real time for operational severe thunderstorm warning and map IC and CG lightning discharge in 3D
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for post research (Y. Wang et al., 2016). Here we use 2D location results to evaluate the BLNET performances. Firstly, the location results for IC and CG pulses are grouped in flashes. Grouping
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criteria for this study are 10 km and 500 ms, which is based on the spatial and time difference of pulses. All pulses up to 500 ms before and after the first CG stroke are considered part of the CG
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flash. Therefore, in this evaluation preliminary breakdown (PB) pulses are integrated into CG flash.
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In the next step, we manually examined the waveform from all fast antenna sensors and extracted IC and CG flashes. To reduce noise errors, all the waveforms have been excluded
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which triggered only a sensor or continuously triggered a particular sensor. A sharp rise with a slow decay in waveforms is considered as a CG stroke and others which do not meet these
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criteria are considered as IC pulse. In Fig. 2, 3 IC pulses are shown. Here PB is shown as C1 and IC Pulses as C2 and C3, where C2 gets 252 ms and C3 at 331 ms. Fig. 3 shows PB and 4 CG strokes. PB on 200 ms and followed by 4 RS, R1, R2, R3 and R4, which are shown at 270.9 ms, 315.1 ms, 522.7 ms and 599.4 ms, respectively. First two RS, R1 and R2 are very near and R2 is stronger than R1. R3 have more IC pulses and R4 with a narrow time duration. IC and CG flashes from the fast antenna waveform have been identified manually using the above criteria. It would not be possible that all sensors are recording an identical flash. So, we classify these flashes using following criteria to compare with the location results. If two or more sensors
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DECG = CG flashes located by BLNET/ CG flashes from fast antenna
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DEIC = IC flashes located by BLNET/ IC flashes from fast antenna
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The fast antenna and BLNET are considered as reference, separately, and performance of the WWLLN and the CMA-LDN, is evaluated in terms of RDE and RLA. To choose fast antenna as a reference in place of BLNET is to reduce overestimation or underestimation
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performance of other LLNs. In this discussion following formulas have been used: (2.a)
RDEFA = (LLNCG event /BLNET CG event) ∗ DECG
(2.b)
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RDE BLNET = LLNCG event /BLNET CG event
Where RDEBLNET is relative detection efficiency when BLNET as reference; RDEFA is relative
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detection efficiency when fast antenna as a reference; DECG is CG detection efficiency of BLNET.
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For this part, 19 thunderstorm days during summer 2015 and 2016 have been considered. The selection of these days has been done on the basis of available data from the three LLNs.
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Shared events among these three LLNs and then shared events between BLNET-CMA-LDN and BLNET-WWLLN have determined. To determine the shared events, time difference and spatial
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separation between LLNs strokes are 0.5 ms and 30 km respectively. Any CG stroke in this
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frame has been considered as shared events between LLNs. 3. Results and Discussion: 3.1 BLNET IC and CG detection efficiency using fast antenna CG strokes are identified from all the lightning flashes on the basis of pulses, which has the obvious wave characteristics of RS, as shown in Fig. 3. The DE for IC flash and CG flash have been calculated using equation 1a and 1b. In every thunderstorm, the performance has evaluated separately and the overall DE based on various days is shown in Table 2. The average DE of BLNET for total lightning, IC and CG are 93.2%, 97.4%, and 73.9%, respectively.
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ACCEPTED MANUSCRIPT The best performance was found on 17 Jul 2015. However, average performance during 2015 was worse as compared to that of 2016. The CG flash DE ranging from 60.6% to 87.9% depend on geographical condition and local conditions of the thunderstorm. It could be day to day limitation of dense LLN that depend on various reasons: (i) Y. Wang et al. (2016) suggested that the high sampling rate and the long record length in the acquisition system makes the buffer time relatively long. In addition, the background noise
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of Beijing varies day to day that depends on local circumstance. It makes impractical to reduce
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triggering threshold in the acquisition system.
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(ii) On 15 July 2015, a multi-cell thunderstorm was developed and most of the time it was outside of the network, which moved eastward from northwest of Beijing, then reached northeast
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of Beijing. On 7 August 2015, multi-cell, then a squall line has been shown and the size of the storm is bigger than the size of one side of the network that moved from northwest towards
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southeast. When storm was heading towards BLNET, another cell was built outside of BLNET as shown in Fig. 4 and many flashes were located in both cells. It is possible that at an instant
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time, some sensors were triggered due to flashes in cell A and few sensors triggered due to flashes in cell B. In this situation, the total number of triggered sensors may be sufficient but
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insufficient to locate the flash in cell A or cell B. On 11 May 2016 and 28 July 2016, the thunderstorm developed from the north of the network and entered inside the LLN. On 17 July
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2015, the high reflectivity echo moved inside the network and most of the time it was near to the network. It is obvious that the number of flashes located by LLN increases when the
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thunderstorm reaches near the LLN. The DE of this type of dense network would depend on the direction of day to day thunderstorm. In the days when a thunderstorm was over the BLNET, it
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has higher DE.
(iii) When the flashes occur far from the network, IC has less strength to trigger enough sensors but CG may trigger, because there is inequality between RS currents and weak IC discharge currents. However, when thunderstorm reaches near to the network, sufficient number of sensors are triggered and locates the pulses. These are the reasons that CG has a range of DE but IC has almost similar DE as shown in Table 2. The results are similar to LASA sferics, where CG event are detected and located in a relatively small number. It was also suggested that the number of detectable lightning event decreases with distance (Shao et al., 2006). It can be
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ACCEPTED MANUSCRIPT suggested that distinct phase and direction of the thunderstorm will affect the DE of dense network. 3.2 Relative performance using BLNET and fast antenna as reference The shared strokes have been compared among the three LLNs across Beijing region to show the RDE and RLA. To compare WWLLN, the criteria were temporal difference ≤ 3 ms and
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spatial separation ≤ 50 km (Lay et al., 2004; Rodger et al., 2005). Jacobson changed the criteria
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and took as temporal difference ≤ 1 ms and spatial separation ≤ 100 km (Jacobson et al., 2006). It was proposed that if the temporal difference reduce ≤ 0.5 ms then there is no need to consider
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the spatial separation (Abreu et al., 2010; Rodger et al., 2006). In a study, spatial separation ≤ 20 km was primary parameter for the shared event (Abarca et al., 2010). In a similar study between
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GLD360 and EUCLID, the temporal difference was 0.1 ms and it was suggested for the small region this temporal difference is enough (Pohjola and Mäkelä, 2013). Further, a comparison was
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performed between regional, subcontinental, and long range LLN over Benelux and France, and 1 s time window with ≤ 15 km spatial separation were chosen (Poelman et al., 2013). These
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conclusions suggested that consideration of spatial separation is not necessary for small area with a small time difference. It this study, analysis area is large enough and spatial separation has
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been considered.
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3.2.1 Relative detection efficiency (RDE) of CMA-LDN and WWLLN Fast antenna data and lightning location data from BLNET are considered as two
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references to determine the RDE of the two different networks WWLLN and CMA-LDN. BLNET, CMA-LDN and WWLLN located 27407, 13548 and 4623 strokes respectively in 19
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thunderstorms. The RDE shown in Table 3 is the outline of real DE by equation 2a and 2b. For every LLN, some flashes are located out of the observation range. These flashes would create 12 % error in entire results. The overall performance of CMA-LDN and WWLLN in Beijing area is approximately 12.4% (16.8%) and 36.5% (49.4%), respectively, with reference as fast antenna (as BLNET). BLNET is well performing and it detects a higher percentage of CG. CMA-LDN and WWLLN are detecting only strong RS and depend on RS peak current. They have few sensors in the observational area and unable to locate most of the CG that can easily located from BLNET.
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ACCEPTED MANUSCRIPT 3.2.2 Relative location accuracy (RLA) for three networks using shared events Here, the time window is chosen as ± 0.5 ms and space window as ± 30 km. Table 4 shows the shared strokes during the study period. It can be seen that the number of shared strokes between BLNET - WWLLN is significantly lower than BLNET - CMA-LDN. It is found that around 20% events have been shared from each network out of total events from that LLN. Fig. 5a demonstrates the comparison among these LLNs. The size of open and close
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circle is varying for different shared stroke. This shows that the same stroke is located at
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different places from CMA-LDN and WWLLN. Space distribution of WWLLN and CMA-LDN
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is found to the north (451 m) - east (61 m) and north (1319 m) - west (31 m) respectively from the BLNET. WWLLN has a higher number of shared events between 0-100 µs as compared to
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CMA-LDN shown in Fig. 5b. The shared events among these LLNs are fewer therefore studies between two LLNs have been included. BLNET is considered as reference and space distribution
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of WWLLN and CMA-LDN are demonstrated in Fig. 6a and b, and shared event is shown in Table 4. In a separate comparison, the average space differences from BLNET to WWLLN are
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south (377 m) - east (2744 m) and BLNET to CMA-LDN are north (3584 m) - west (74 m). Space differences for most of strokes are less than 10 km between WWLLN and BLNET. For
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CMA-LDN, the space differences with BLNET are scattered approximately uniformly up to 30 km. Time differences in separate matched strokes are shown in Fig. 7a and b. For WWLLN,
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higher numbers of matched strokes are found in 0-100 µs; whereas the number of matched strokes is approximately similar to 0-100 µs and 100-200 µs for CMA-LDN.
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Based on the results, it can be mentioned that the WWLLN performance is good as considering its global coverage and VLF working frequency band, and it locates southeast from
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CMA-LDN and BLNET. CMA-LDN performs northwest from BLNET. Even though the RLA of BLNET is found between CMA-LDN and WWLLN, the shared event is less than expected. The performances of shared stroke of WWLLN have better relation with BLNET than CMALDN. 3.3 Comparison of three LLNs with radar echo and tower lightning RLA and RDE can show only relative performance and cannot be believed with 100% confidence. So, a comparison has been shown between these three networks as BLNET, CMALDN and WWLLN with radar echo and tower lightning. 10
ACCEPTED MANUSCRIPT 3.3.1 Comparison of three LLNs with radar echo Fig. 4 shows radar reflectivity during six-minute intervals on 7 August 2015 from 09:30:00 to 09:36:00 UTC and all IC pulses and CG strokes in the same period are superimposed on it. In this figure, the IC pulses are 1019 for BLNET and CG strokes for BLNET, CMA-LDN and WWLLN are 16, 12 and 9 respectively. In Fig. 4, there were two cells as cell B being inside the network and cell A being outside. They were moving toward the network and become a
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squall line late on. Most of the CG strokes and IC pulses from the BLNET are located in the cell
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B. Few strokes from CMA-LDN and BLNET are located at northeast and missed from WWLLN
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or may be located in northwest. Out of these, six strokes have been shared among LLN shown in enlarged part of Fig. 4. In cell B, the shared stroke location from WWLLN is on weak
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reflectivity and location from CMA-LDN and BLNET is on reflectivity above 50 dBz. In cell A, all LLNs locate the shared strokes on around 30-35 dBz. Both cells demonstrate the BLNET
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location is at middle whereas CMA-LDN and WWLLN are located northwest and southeast respectively from BLNET. This clearly represents the overall detection of BLNET is high in
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comparison to other LLNs and there is a good relation between IC pulses and CG strokes with the radar reflectivity.
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3.3.2 Comparison of three LLNs with tower lightning Tower lightning provides good opportunity to evaluate the performances of lightning
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network with its known location and can be regarded as ground truth. However, for a high tower of 325 m in this study, most of the tower lightning flashes are upward lightning with only weak
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initial continuing current stage (Jiang et al., 2014), and can’t be detected by enough sensors working in the megacity region with high radio noise background. Two tower lightning flashes
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with RS were recorded by both the high-speed camera and BLNET in 2014 and 2016. Location accuracy of BLNET is estimated by using the two tower lightning flashes. Location error of BLNET by using one lightning stroke to the tower tip on 11 May 2016 was 52.9 m. Another lightning stroke on 6 June 2014 was 250 m, as discussed by Y. Wang et al. (2016). At that time the device had time synchronization errors due to different types of GPS clock and some sensors were still on installation and upgrading. The tower lightning on 11 May 2016 as discussed above was also located by CMA-LDN at 889.9 m from tower. It is found that the location difference and time difference between BLNET- CMA-LDN are 837 m and 22.7 µs respectively. The tower lightning on 6 June 2014 11
ACCEPTED MANUSCRIPT was located by WWLLN at 7.3 km from the tower. Location difference and time difference between BLNET-WWLLN are 7.1 km and 50 ms respectively. Two tower lightning flashes are not sufficient to give the actual performance of the three networks, and more ground truths are still needed. 4. Conclusion
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In this work, performance of BLNET has been evaluated on the basis of self-reference
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methods. Lightning location data of five different days of the thunderstorm have been analyzed
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to show its DE. Based on fast antenna verification, the average DE of BLNET is 97.4% for IC, 73.9% for CG and 93.2% for the total lightning. CG stroke data for 19 thunderstorm days from three different LLNs were compared, and RDE and RLA have been shown for the 3 LLNs across
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Beijing. The RDE of WWLLN and CMA-LDN have approximately 12.4% (16.8%) and 36.5% (49.4%) respectively, in the context of fast antenna (BLNET). The location of BLNET is in
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middle, while WWLLN and CMA-LDN average location are southeast and northwest, respectively when superimposing the lightning stroke locations on radar reflectivity, suggesting
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the performance of BLNET is better than WWLLN and CMA-LDN. BLNET have better DE and LA in the coverage of the network, and RDE and RLA are showing a confidence in favor to
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BLNET. This shows that BLNET has a high capability to detect IC flashes and can be used for thunderstorm tracking and warning with high confidence.
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Two tower lightning flashes occurred in the coverage of the BLNET shows that the horizontal location error is 52.9 m and 250 m, respectively. It is suggested that the location error
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from long range network could be up to few hundreds meters or few kilometers; however from dense network it can be achieved up to few tens meters. It is suggested that the DE of very dense
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network is accurate when the thunderstorm passes over the network, and it can be different day by day when the thunderstorms are outside the network. Acknowledgment The research was supported by National Key Basic Research Program of China (Grant No. 2014CB441401), Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-DQC007) and National Natural Science Foundation of China (Grant No. 41475002). The authors wish to thank the WWLLN (http://wwlln.net), collaboration among over 60 universities and institutions,
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ACCEPTED MANUSCRIPT for providing the lightning location data. The first author would like to thank Chinese Academy of Science for CAS-PIFI fellowship grant. References Abarca, S.F., Corbosiero, K.L., Galarneau, T.J., 2010. An evaluation of the Worldwide Lightning Location Network (WWLLN) using the National Lightning Detection Network
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ACCEPTED MANUSCRIPT Figure Caption Fig. 1: Layout of BLNET sensors and corresponding sites name Fig. 2: IC waveforms from fast antenna sensor Fig. 3: CG waveforms from fast antenna sensor Fig. 4: The 2D lightning locations compared with the corresponding radar reflectivity including CG and IC pulses among 3 LLNs on 07 August 2015 during 09:30:00 to 09:36:00 UTC
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Fig. 5: Shared events with reference to BLNET. a. space difference, and b. time difference
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Fig. 6: Spatial distribution of shared events with reference to BLNET. a. WWLLN, and b. CMA-
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Fig. 7: Time difference of shared events with reference to BLNET. a. WWLLN, and b. CMA-
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Table 4: Shared CG events in various LLNs
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Fig. 4. The 2D lightning locations compared with the corresponding radar reflectivity including
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Fig. 5. Shared events with reference to BLNET a. space difference b. time difference
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Fig. 6. Space distribution of shared events with reference to BLNET a. WWLLN b. CMA-LDN
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Fig. 7. Time difference of shared events with reference to BLNET a. WWLLN b. CMA-LDN
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WWLLN
CMA-LDN
BLNET
Range
World
China
Beijing
Agency
University of Washington & 50 Universities
Chinese Meteorology Administration
Institute of Atmospheric Physics, CAS
Put into operation
2004
2011
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Table 1: Comparative description of LLN in Beijing Area
Frequency
VLF
VLF/LF
Location
2D
2D
Technique
TOGA
TOA+DF
Detection
CG+IC without discrimination
Sensor
>60 approx
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2014
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VLF/LF, VHF 2D, 3D TOA
CG and IC with discrimination
>301 approx
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CG
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ACCEPTED MANUSCRIPT Table 2: IC and CG flashes identified from fast antenna and BLNET with DE Date
Fast Antenna
BLNET
DE of BLNET (%)
IC
CG
IC
IC
CG
Total
15 Jul 15
172
491
110
470
95.7
63.9
87.5
17 Jul 15
1713
3324
1507
3303
99.4
87.9
95.5
07 Aug 15
1767
12657
1071
12437
98.3
11 May 16
352
1706
275
1521
28 July 16
76
238
54
Total
4080
18416
3017
93.6
89.2
78.1
87.3
209
87.8
71.1
83.6
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97.4
73.9
93.2
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60.6
17940
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RDEBLNET
49.4%
16.8%
RDEFA
36.5%
12.4%
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ACCEPTED MANUSCRIPT Table 4: Shared CG events in the three LLNs Total number of CG shared strokes
LLN
786
BLNET-CMA-LDN
2754
BLNET -WWLLN
961
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BLNET - CMA-LDN - WWLLN
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ACCEPTED MANUSCRIPT Highlights (for review)
Self reference method to obtain DE of IC and CG based on waveform from the entire sensor used to locate lightning in BLNET. Comparison of regional, subcontinent and long range LLN with radar.
Performance of subcontinent and long range LLN using BLNET.
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