Huntsville Alabama Marx Meter Array 2: Upgrade and Capability

Zhu, Yanan; Bitzer, Phillip M.; Stewart, M. F.; Podgorny, Scott; Corredor, David; Burchfield, J. C.; Carey, Lawrence D.; Medina, Bruno L.; Stock, Michael

doi:10.1029/2020ea001111

Cited by 26 publications

(23 citation statements)

References 29 publications

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“…Similar fluctuating behavior can also be recognized in the negative leaders mapped by other authors who used low-frequency lightning mapping systems (e.g., Lyu et al, 2016;Zhu et al, 2020).…”

Section: Journal Of Geophysical Research: Atmospheressupporting

confidence: 75%

A Comparison on the E‐Change Pulses Occurring in the Bi‐Level Polarity‐Opposite Charge Regions of the Intracloud Lightning Flashes

Shi

Wang

et al. 2020

JGR Atmospheres

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We have compared the E‐change pulses occurring in the upper positive charge region (UPCR) and the middle negative charge region (MNCR) of 41 normal intracloud (IC) flashes. E‐change pulses can be roughly grouped into isolated pulses and pulse trains with both positive and negative polarities. Although isolated pulses occurred about equally in UPCR and MNCR, there were much more pulse trains in MNCR than UPCR. Overall, the average amplitude, rise time, half‐peak width, and fall time of isolated pulses (pulse trains) are 56% larger (115% larger), 31% faster (43% slower), 25% narrower (46% wider), and 26% shorter (62% longer) in UPCR than in MNCR. Interestingly, in the same charge region, positive and negative pulses share similar characteristics. Based on a typical well‐structured IC, it was found that the majority of isolated pulses in both UPCR and MNCR occurred at or close to the leader tips. In contrast, about 30% UPCR pulse trains occurred at or close to the tip, while the remaining 70% pulse trains occurred in the leader channel retracing along the leader channel away from the flash origin; for MNCR pulse trains, about 60% occurred close to the tip, and 40% occurred in the channel with a backward extension to the flash origin. Based on these characteristics, we have discussed the physical nature of various types of pulses and pointed out that a leader is likely to progress with a fluctuating behavior when it is neutralizing space charge distributed in a region with sufficient thickness and width.

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Section: Journal Of Geophysical Research: Atmospheressupporting

confidence: 75%

A Comparison on the E‐Change Pulses Occurring in the Bi‐Level Polarity‐Opposite Charge Regions of the Intracloud Lightning Flashes

Shi

Wang

et al. 2020

JGR Atmospheres

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“…In addition, a recent study showed that a VHF lightning mapping system located a smaller number of sources than an LF/MF lightning mapping system, especially for those sources occurring in the negative charge layer at a low altitude (Zhu et al., 2020). A longer time window length by using the VHF lightning mapping system (80 μs) than that of the LF/MF lightning mapping system (30 μs) was speculated to be one of the factors that caused the difference.…”

Section: Methodsmentioning

confidence: 99%

“…This means that the VHF radiation sources located by our network in the negative charge layer may be slightly less than that which could be located by the VHF lightning mapping system mentioned above. However, the short baseline of our network (Figure 1 in Wang et al., 2018) is advantageous for the capability of detecting more VHF radiation sources (Zhu et al., 2020). On the whole, the comprehensive effect of the network layout and parameter settings on the existing detection efficiency may not be significant.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of Negative Leader Propagation Area of Lightning Flashes Initiated in the Stratiform Regions of Mesoscale Convective Systems

et al. 2021

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To investigate the characteristics of extension areas (mainly the propagation areas of negative leaders in this study), the lightning location data of 254 lightning flashes initiated in the stratiform regions (stratiform lightning flashes) of 14 mesoscale convective systems (MCSs) are analyzed. The results show that most of the flashes have a relatively small lightning area (LA) (≤100 km2), although they are initiated in the stratiform regions. In small or developing MCSs, most negative leaders of stratiform lightning flashes concentrate within the 9–12‐km altitude range. In other MCSs with a large‐sized and developed stratiform region, besides being in this high‐altitude range, the negative leaders are also found to propagate more frequently in a low‐altitude range of 5–7 km. Further analysis indicates that most of the stratiform lightning flashes with a large LA (>100 km2) propagate their negative leaders within the high‐altitude range, no matter where they are initiated. Moreover, the stratiform lightning flashes with or near the largest LA tend to be initiated 4–6 km below their negative leaders, while most of the stratiform lightning flashes usually propagate their negative leaders horizontally within ±1 km of the first detected very‐high‐frequency (VHF) radiation source. It is inferred that some in‐situ electrifications occurring before and during the formation of the high reflectivity layers in the low‐altitude range contribute to these flashes, although the influence of the advection charges from the convective regions still cannot be totally ruled out.

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“…The CAMMA consists of 10 electric field change sensors deployed in an array with 30-60 km baselines near Córdoba in central Argentina for the Remote sensing of Electrification, Lightning, And Mesoscale/ microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. Each sensor has two channels, a fast channel with a decay time constant of 100 µs, which is used for lightning mapping, and a slow channel with a decay time constant of 100 ms for charge retrieval analysis (Zhu et al, 2020b). Only the fast-channel waveforms were used for training the model in this study because the fast channel can detect small-amplitude pulses, and thus can provide pulses with a full range of amplitudes for model training.…”

Section: Cordoba Marx Meter Arraymentioning

confidence: 99%

“…Only the largest peak in each 800-µs window was used for locating. Note that the 800-µs window is much longer than the 30-µs window used in other techniques that can depict lightning development in detail (Zhu et al, 2020a(Zhu et al, , 2020b, but it allows fast processing of a large amount of data while also provide enough data for model training. The CAMMA requires a minimum of six stations to locate a lightning source.…”

Section: Cordoba Marx Meter Arraymentioning

confidence: 99%

A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning

Zhu

Bitzer

Rakov

et al. 2021

Geophysical Research Letters

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Lightning can be classified into two types according to whether it reaches the ground or not. About three-quarters of lightning discharges do not involve ground, most of which belong to the intracloud (IC) lightning category (Rakov, 2016). Lightning discharges that reach the ground are referred to as cloud-toground (CG) lightning. Whether a lightning discharge strikes the ground or remains inside the cloud can be determined from their low-frequency (LF) electromagnetic field waveforms. Pulses generated by lightning processes inside the cloud differ from those generated when lightning strikes the ground (the process referred to as return stroke). Typical return-stroke field waveforms measured at a distance beyond several tens of kilometers or so are characterized by a short rise time of several microseconds and a relatively slow decay time of several tens of microseconds (Haddad et al., 2012; Lin et al., 1979). Compared to return strokes, field pulses of most intracloud lightning processes are generally narrower. Also, due to the greater diversity of lightning processes inside the cloud, pulse waveshapes tend to have larger variation from one event to another. The differences in field waveforms produced by in-cloud lightning processes and return strokes are used in the multiparameter waveform classification methods implemented in many regional and continental scale ground-based lightning detection networks (e.g., U.S. National Detection Network (NLDN), Earth Networks Total Lightning Network (ENTLN), and European Cooperation for Lightning Detection (EUCLID) network) to classify the detected lightning field pulses into two categories: cloud pulses (referred to as ICs) and return strokes (referred to as CGs). In recent validation studies for the abovementioned networks, estimated classification accuracy for return strokes ranges from 70% to 92% (

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Huntsville Alabama Marx Meter Array 2: Upgrade and Capability

Cited by 26 publications

References 29 publications

A Comparison on the E‐Change Pulses Occurring in the Bi‐Level Polarity‐Opposite Charge Regions of the Intracloud Lightning Flashes

A Comparison on the E‐Change Pulses Occurring in the Bi‐Level Polarity‐Opposite Charge Regions of the Intracloud Lightning Flashes

Characteristics of Negative Leader Propagation Area of Lightning Flashes Initiated in the Stratiform Regions of Mesoscale Convective Systems

A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning

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