Total Lightning Observations with the New and Improved Los Alamos ...

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JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY

OCTOBER 2006

Total Lightning Observations with the New and Improved Los Alamos Sferic Array (LASA) XUAN-MIN SHAO, MARK STANLEY, AMY REGAN, JEREMIAH HARLIN, MORRIE PONGRATZ, AND MICHAEL STOCK Space and Remote Sensing Sciences Group, Los Alamos National Laboratory, Los Alamos, New Mexico (Manuscript received 3 October 2005, in final form 13 January 2006) ABSTRACT Since 1998, Los Alamos National Laboratory (LANL) has deployed an array of fast electric field change sensors in New Mexico and Florida in support of LANL’s satellite lightning observations. In April 2004, all the sensors were significantly upgraded and improved, and a new array was deployed in north-central Florida. This paper describes the operations of the new array and reports the first 12 months of lightning observations. The new array is about 10 times more sensitive than the previous one and can capture millions of discharge events during a stormy day in Florida. In this paper, the array’s lightning location accuracy, minimum detectable peak current, and ratio of intracloud-to-cloud-to-ground flashes are analyzed. Some case studies that illustrate the storm evolution, lightning classification, and radar comparisons are presented. A new three-dimensional capability of the array is demonstrated.

1. Introduction Since 1998, Los Alamos National Laboratory (LANL) has deployed an array of fast electric field change sensors in New Mexico and Florida in support of LANL’s Fast Onboard Recording of Transient Events (FORTE) and GPS satellite observations of lightning discharges (Smith et al. 2002, 2004; Suszcynsky et al. 2005). The sensors operated at a radio frequency range from 200 Hz to 500 kHz and detected transient electric field changes produced by lightning discharges. The transient field change was referred as “atmospheric,” or colloquially “sferic,” and the array is referred as the Los Alamos Sferic Array (LASA). The purpose of LASA has been to provide groundtruth observations on lightning location, type, and discharge current for the FORTE and GPS satellites that operate at very high frequency (VHF) and have either no or limited capability of lightning identification and geolocation (Jacobson et al. 1999; Suszcynsky et al. 2001; Jacobson and Shao 2001; Shao and Jacobson 2001). For this purpose, LASA was designed to detect

Corresponding author address: Xuan-Min Shao, Los Alamos National Laboratory, ISR-2, MS D436, Los Alamos, NM 87545. E-mail: [email protected]

© 2006 American Meteorological Society

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both cloud-to-ground (CG) and intracloud (IC) discharges and was required to record the raw time waveforms of the corresponding field changes. Each station was time synchronized by a GPS receiver at a timing accuracy of ⬍2 ␮s and the source locations were determined by using differential time of arrival (DTOA) techniques. Raw waveforms were transferred daily via the Internet to a central station in Los Alamos in which the signals were characterized, geolocated, classified, and archived. Detailed description of the early LASA operation can be found in Smith et al. (2002). The commercially available U.S. National Lightning Detection Network (NLDN) operates at the same very low frequency (VLF)/low-frequency (LF) range and has a complete coverage of the continental United States (Cummins et al. 1998), but it is configured to predominantly detect the return stroke of CG flashes and does not record the field change waveforms. Therefore, NLDN can provide useful but limited support for the satellite observations. FORTE lightning studies that exploited NLDN data have been reported by Jacobson et al. (2000), Jacobson and Shao (2002), and Shao et al. (2005a). In addition to the supportive role to the satellites, LASA alone has proved a useful research tool for lightning and thunderstorm studies. Prior to LASA, a threestation array operated collaboratively by New Mexico

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Institute of Mining and Technology (hereafter New Mexico Tech) and LANL revealed new physical insights on the “narrow bipolar events” (“NBEs”) that had been discovered earlier by other researchers (e.g., Le Vine 1980; Willett et al. 1989; Medelius et al. 1991), which were first reported briefly by Shao et al. (1996) and Smith et al. (1996) and later by Smith et al. (1999) in a more thorough analysis. Based on the success of the three-station array, LANL deployed five stations across New Mexico in 1998, and expanded the array with five stations in Florida and one station each in Texas and Nebraska in 1999 (Smith et al. 2002). During several years of observations, LASA demonstrated that it could effectively monitor thunderstorm development and movement, with a regional coverage and limited sensitivity resulting from the limited number of stations. NLDN, on the other hand, covers the entire continental United States with 90% of CG return-strokedetection efficiency (Cummins et al. 1998). In addition to CG return strokes, LASA recorded a large number of IC pulses, especially the distinct NBEs that are often temporally isolated from other “normal” CG and IC lightning discharges and are commonly associated with powerful radio frequency radiation. With over 10 000 NBEs observed, their statistical signatures were studied and compared to the normal lightning field changes (Smith et al. 2002). Owing to the narrow pulse width and the large pulse amplitude of NBEs, their ionospheric reflections can often be readily identified in the data record. Based on the geometry of the reflection, the source height as well as the ionosphere reflection height can be accurately determined for sources that were hundreds of kilometers away from the stations (Smith et al. 2004). It has been found that oppositepolarity NBEs appear to occur at two distinctive heights with positive NBEs below and negative NBEs above ⬃15 km (Smith et al. 2004). In the context of storm development, LASA data have also been used to compare with radar reflectivity and satellite infrared images, and it was found that NBEs could be treated as indicators of a storm’s convective strength (Suszcynsky and Heavner 2003; Jacobson and Heavner 2005). In April of 2004, LANL substantially upgraded the field change sensors and redeployed the array to northern-central Florida. The improved sensors are broader in frequency response, lower in system noise, and more stable in operation. The trigger system is now entirely software based, thereby enabling flexible and sophisticated triggering algorithms. The data acquisition system exploits the current state-of-the-art digitizing and computer technologies and is capable of capturing every field change pulse without any dead time. The time tagging of the signals is upgraded with a new GPS re-

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ceiver, which has greater accuracy. Instead of the previous long-baseline configuration with 200–500-km station separations, the redeployed array consists of six stations within an area of ⬃100 km in diameter and two remote stations ⬃200 km away from the center of the array. The new array configuration allows us to study lightning discharges in much more detail over the dense array, and at the same time to monitor more distant lightning flashes. The geolocation algorithm has also been improved and is now capable of locating each of multiple pulses within a single data record instead of determining a single location for an entire 8-ms record. In this report, we describe the improved sensors, the new and improved capabilities of the redeployed array, and the initial results during the first 12 months of observation.

2. Sensor description a. Fast electric field change sensor The principle of the upgraded fast field change sensors is the same as that of the earlier LASA sensors (Smith et al. 2002), except that the new sensor was redesigned with new and advanced electronics and refabricated with more careful component layout. As described by Smith et al. (2002, their Fig. 3), the LASA’s sensor design was adopted from an original design by M. Brook of New Mexico Tech. The sensor head is suspended ⬃1 m above the ground and the sensing plate and charge-amplification circuit are shielded from above with a stainless steel “salad bowl.” Through 6 yr of LASA operation, this type of design has proved its structural ruggedness and is unsusceptible to spurious signals introduced by the impact of raindrops on the sensing plate. It is well known that the output of the sensor is proportional to the electric field change ⌬E of lightning discharge with the following relation (e.g., Krehbiel et al. 1979): ⌬V ⫽

␧0Aeff ⌬E. C

Here, ⌬V is the output of the sensor; ␧0 is the permittivity of vacuum; Aeff is the effective area of the sensing plate that could be greater or less than the plate’s physical area, depending on the sensor setup and the surrounding environment; and C is the capacitance of the RC feedback of the charge amplification circuit. The RC decay time constant ␶ for LASA sensors was chosen to be 1 ms, which yields a cutoff frequency at about 1/2␲␶ ⬇ 160 Hz at the low end. The cutoff frequency at the high end is increased significantly from the previous

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⬃300 kHz to the present ⬃20 MHz as a result of the upgraded design. Nevertheless, for the study presented in this report, signals at VLF/LF range are of interest and the output of the sensor is filtered by a 500-kHz low-pass filter before it is digitized and recorded. All LASA sensors are situated on rooftops of buildings to maximize the sensitivity [to increase the effective area Aeff in Eq. (1)] and to minimize possible signal interference from surrounding structures. Analog signals from each sensor are sent through a 30-m coaxial cable via a 50-⍀ line driver to the interior of the building, where the data acquisition system resides.

b. Data acquisition and triggering systems The new LASA sensors exploit a peripheral component interconnect (PCI)-based, high-speed [up to 20 mega samples per second (MSPS)] digitizer and a Linux personal computer (PC) for the signal digitization and acquisition. The 500-kHz low-passed signal is digitized continuously at a rate of 20 MSPS with a 12-bit resolution, and the digitized signal is continuously transferred to the PC’s memory from the digitizer card via the PCI bus. The signal is then summed among 10 consecutive samples that effectively reduces the sample rate from 20 to 2 MSPS, but at the same time raises the digital resolution from 12 to ⬃15 bits because each factor-of-2 summation increases the digital resolution by 1 bit. After this, the signal is sequentially compared against a preconfigured numerical trigger threshold. When a sample is detected above the threshold, a data record with a certain fraction of pretrigger and posttrigger is stored in a 32-MB circular buffer in the PC’s memory space. The corresponding time information that marks the start time of the triggered record is stored in a separate 8-MB circular buffer. New data and a header files are generated every 5 min, with the former containing the consecutively triggered data streams and latter containing the corresponding event times. At every hour mark, files generated in the previous hour are compressed and are ready for retrieval from a processing computer in Los Alamos via the Internet. To use the data storage effectively and at the same time capture all of the interested sferics features, different triggered data lengths were tested during the first 12-month operation. The data length was set to 1 ms from April to 16 August 2004, with the trigger point normally at the center. From 16 August to 26 September 2004, the trigger length was set to 400-␮s with an 80-/320-␮s pre-/posttrigger. From 26 September onward, the trigger length was set to 500 ␮s with a 150-/350-␮s pre-/posttrigger. The previous LASA sensors employed a hardwarebased triggering system that compared the sensor’s out-

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put voltage to a prespecified trigger level. The shortcoming of this was that to avoid false triggering on slow floating background interferences, the trigger threshold had to be set relatively high and the sensor’s sensitivity was sacrificed. The interferences were commonly due to the 60-Hz background of the power grid, the static field of nearby clouds or storms, and sometimes the sensor’s electronic offset. The new trigger system is entirely software based and is flexible in implementing different triggering algorithms and can readily overcome the previous problems. For the study presented here, the trigger algorithm continuously monitors the low-frequency nonlightning interference with a 2-kHz averaging of the data stream and sets a bipolar trigger level riding on the floating background. Figure 1 compares the floating trigger algorithm against the previous fixed-level trigger system. The floating trigger threshold is illustrated by the pair of red dotted lines following the 60-Hz and the static background fluctuation (solid black line). Two lightning pulses were detected above the floating threshold in the figure. For a fixedlevel threshold, the system would either miss the two lightning pulses with a high, insensitive threshold (the pair of blue dotted lines away from the middle), or would likely be falsely triggered by the interference with a lower threshold (the pair of blue dashed lines near the middle). The other shortcoming of the previous LASA system was that it suffered a considerable dead time (⬃30 ms) after each trigger, because of the PC and the PC-based digitizer technologies at the time the system was developed. With the capability of continuous data transfer from the digitizer to the PC memory and the softwarebased floating triggering algorithm, the upgraded LASA sensor eliminates the dead times between successive triggers and is much less constrained by the background interference.

3. Array configuration and operation a. Array configuration In April of 2004, we deployed eight upgraded LASA stations in northern-central Florida. Figure 2 shows the array configuration together with 5 min of lightning observations. Six stations formed a relative dense array over an area of ⬃100 km in diameter, and two remote stations at Tallahassee (TLH) and Tampa (TPA) formed longer baselines (⬃200 km) from the central array. The purpose of this array configuration is to study lightning flashes in more detail for storms over or near the dense array, and at the same time to maintain the previous capability of detecting and locating lightning discharges distant from the array. All of the

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FIG. 1. Comparison between floating and fixed-level trigger thresholds. Solid black curve shows two lightning field change pulses overriding background interference. The pair of red dotted lines indicates the floating trigger threshold. The pair of blue dashed lines indicates the fixed-level threshold is too sensitive, whereas the pair of blue dotted lines indicates the fixed-level threshold is too insensitive.

Florida stations are located on local university/college campuses, as listed in Table 1, and the data acquisition system at each station is connected to the local Internet for remote operation and data retrieval. In addition to the Florida stations, we also set up a station in Los Alamos for the purpose of software and hardware development. The Los Alamos station is ⬃1600 km away from the center of the Florida array, and therefore it runs mostly independently from the Florida array. Nevertheless, stations at both places occasionally detected common lightning events with large peak current.

b. Array operation Lightning geolocation and classification from the new array first became available on 3 April 2004 with four stations operational, as listed in Table 1. As with the previous LASA operation, the current array operates automatically with little operator intervention. Each station is configured to start up automatically after a power outage. Since the deployment of the array in April 2004, only one site visit has been made. The array’s data retrieval, state-of-health monitoring, system configuration and software upgrades are all done remotely from Los Alamos through the Internet.

In the normal operational mode, data are retrieved on a nightly basis from the individual stations to Los Alamos by automatic scripts. If a large quantity of data is collected by the individual stations, the scripts can transfer multiple files simultaneously from each station via multiple secure shell (SSH) pipes. At each station, the local system checks once an hour if the file system is 90% full. If it is, the oldest retrieved data are then deleted from the local station. Unlike the previous LASA operation that only retrieved the data if more than three stations were triggered on the same event, we now retrieve and archive all of the data from each station in a massive storage system in Los Alamos. After the raw data are retrieved from all available stations, a series of data analyses is conducted by the processing computer. First, data among all of the stations are sorted and compared to find the common discharge events. Based on the event time differences, two- or three-dimensional geolocation is computed for each event. The geolocated events then undergo a classification process that determines the discharge type (e.g., ⫺CG, ⫹CG, IC, ⫺NBE, ⫹NBE). For the NBEs, the height of the source and the effective ionospheric height are also determined, based on the ionospheric reflections of the original pulse (Smith et al. 1999,

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FIG. 2. LASA lightning observations compared to NEXRAD radar reflectivity during a 5-min interval. (a) Black squares mark LASA stations and red dots represent located lightning sources. Six stations form a dense array centered at PLK and two remote stations form longer baselines. (b) The NEXRAD plan position indicator (PPI) scan at the same time interval. Notice that radar coverage is smaller than the expansion of the storm/lightning activity.

2004). And, finally, the peak current for each event is estimated based on the peak field and the source– sensor distance. After the initial process, the raw data and the processed results are stored in a massive storage device for later analysis.

4. Performance and capability a. Lightning location and characterization LASA uses the DTOA technique to geolocate lightning events that are detected by four or more stations. The first step in the geolocation process is to find the common events among the different stations and to sort the corresponding time differences. In the previous LASA analysis, a technique of cross-correlation between pairs of raw waveforms was implemented. It is

clear that each record (8 ms for the previous system and 1, 0.4, or 0.5 ms for the current system) can contain multiple field change pulses, as shown in Fig. 3a, and the cross correlation between a pair would yield a single averaged time difference across the pulse series. Unlike the cross-correlation technique, the new process attempts to match each of the individual pulses among the different stations and to produce a sequence of time differences for all of the pulses. This enables us to locate discharge events down to the pulse level instead of the previous record level, and to provide more detailed and more accurate locations. To do this, we first convert the original field change waveform (Fig. 3a) to power waveform (Fig. 3b) by summing the squares of the original field change and the Hilbert transformation of the field change. The two parts of the

TABLE 1. Station information of LASA in Florida. Station ID

Initial date of operation

Relative sensitivity*

Location

Host facility

DAB GNV JAX OCF PLK TLH TPA UST

2 Apr 2004 3 Apr 2004 17 Jul 2004 5 Apr 2004 1 Apr 2004 7 Apr 2004 30 Apr 2004 2 Apr 2004

2.73 0.58 1.48 1.00 1.00* 1.47 1.43 0.94

Daytona Beach, FL Gainesville, FL Jacksonville, FL Ocala, FL Palatka, FL Tallahassee, FL Tampa, FL St. Augustine, FL

Daytona Beach Community College University of Florida University of North Florida Central Florida Community College St. Johns River Community College Florida State University University of South Florida St. Johns River Community College

* PLK used as reference station. Higher values correspond to higher sensitivity.

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FIG. 3. (a) Original field change waveform. (b) Power waveform converted from the field change.

summation correspond to the real and imaginary parts of a complex signal. The Hilbert transform of the original waveform provides the necessary imaginary copy of the original waveform for the power computation (Stearns and Hush 1990; Shao et al. 2005a). A peak detection algorithm is then applied to find the individual power peaks. At a specific station, if two or more successive peaks are closer than 10 ␮s, the largest one is registered as the sole peak in the interval. Therefore, the time separation between two locatable sources is 10 ␮s or greater. The selected peaks are tagged with their amplitudes and exact arrival times (⬍1 ␮s in accuracy). To pick the common peaks between a pair of stations, we slide the two peak sequences to find the best match in terms of both the amplitude and time sequence. The overlapped peaks (within a 10-␮s tolerance window) are then assumed to be common peaks between the two stations. This type of peak matching process is then applied among all of the available stations. If a peak can be commonly found in four or more stations, its absolute arrival times at the stations are then used in the DTOA technique to retrieve its source location. Figure 4 illustrates that a total of five peaks are matched

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among four or five stations by comparing a 1-ms data record at five stations. In the figure, matched peaks are marked with stars that have the same color. For instance, the first peak (blue) is matched by all the five stations, whereas the third peak (red) is only picked out by four stations. For the purpose of demonstration, Figs. 3 and 4 show only 1-ms records. The upgraded LASA is capable of triggering continuously without any dead time. In the case of multiple triggers being captured within a time interval that is shorter than the speed of light propagation time between stations, the multiple triggers are “stitched” together sequentially, and the peak match is processed over the elongated data streams. For the purpose of comparison, the previous LASA was only capable of producing a single set of time differences for a group of 8-ms records, and each trigger was followed by a dead time of 30 ms or longer. After a set of times is generated for a peak, a twodimensional location for the source is computed. First, an initial guess is estimated by solving the linearized DOTA equations from four stations. Then, the guessed solution is used in an iterative Levenberg–Marquardt least ␹2 fit to find a more accurate solution, following a similar technique used by New Mexico Tech’s Lightning Map Array (Thomas et al. 2004). The least ␹2 fit is conducted over an oblate spheroidal earth to accommodate the curved ground wave paths for signals at VLF/LF. LMA operates at 60–66 MHz and considers only the straight line-of-sight propagations. Together with the array configuration, Fig. 2a shows 5-min observations of a frontal storm system approaching LASA on 7 April 2005. With the upgraded sensors and the new processing techniques LASA was able to locate over 17 000 events in the 5-min interval, or at a rate ⬃60 events per second from this storm. As compared to the simultaneous Next Generation Weather Radar (NEXRAD) observations (Fig. 2b), it is clear that the lightning discharges occurred in the regions of high radar reflectivity, showing that LASA can be well suited for active storm detection and monitoring. The spatial coverage of this specific radar is smaller that the expansion of the storm/lightning activity, as indicated by the lightning sources at the northeastern tip of the front line. Once the lightning events are geolocated, the raw waveforms are used to classify the types of lightning. The waveforms are examined for the rise time, the fall time, and the pulse width, and these parameters are compared with the source distance to determine the lightning types (⫺CG, ⫹CG, IC, ⫺NBE, or ⫹NBE), as previously discussed by Heavner et al. (2003). Figure 5 illustrates the results of the classified lightning types

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FIG. 4. Matched power peaks among four or more stations. Each matched peak is marked by stars with the same color.

together with the temporal evolution of another frontal storm system that moved across northern-central Florida on 8 April 2004. As shown in Fig. 5a, this frontal system lasted about 14 h and moved steadily from the Gulf of Mexico to the Atlantic Ocean, as indicated by the color-coded time sequence. Unlike the frontal system shown in Fig. 2, which produced lightning discharges across almost the entire front, this system spawned a series of discrete cells across a north–south front, as indicated by the discrete but continuously progressing tracks. The entire system became lightning active at about 0800 UTC (0400 A.M. local time) and progressed eastward at a relatively constant speed. A total of ⬃126 000 sources were located for this storm system and the sources were grouped into ⬃34 000 flashes (Fig. 5b). In this study, a flash is composed of a sequence of discharge events in which 1) no event can be more than 0.3 s apart from its neighbors, 2) all of the source locations are within a common area of 10 km in diameter, and 3) the grand time length of the sequence cannot exceed 2 s. In comparison, NLDN defines a flash with the respective parameters of 0.5 s, 10 km, and 1 s (Cummins et al. 1998). It should be noted that NLDN events are limited to return strokes, and its flashes refer only to CGs. On the other hand, NASA’s Optical Transient Detector (OTD) assembled events

into a flash if the events are separated no more than 0.333 s and 25 km (Boccippio et al. 2001). Apparently, it is difficult to find a universal set of parameters for the flash definition, given that the storm size can vary significantly from region to region. The flash definition in this paper attempts to reconcile NLDN and OTD’s definitions. The location for a flash in Fig. 5b is represented by the averaged location of the events that are grouped into the same flash. Among the ⬃34 000 flashes, 27 361 were IC flashes, 1373 were ⫹CGs, 5268 were ⫺CGs, and 398 were NBEs. Because of the limited choices of distinctive colors, ⫹NBEs and ⫺NBEs are grouped into a single NBE class. The total IC/CG flash ratio for this storm is about 4. For lightning discharges over or near the dense array, several to several tens of sources are typically located from a single flash. It is interesting to note that most of the NBEs, as indicated by the yellow dots in Fig. 5b, were produced by two or three storms cells during a several-hour time interval at the middle of the storm life cycle. The figure appears to suggest that NBEs prefer larger storm cells, as indicated by the width of cell tracks, and coincide with ⫹CGs. Positive CGs are known often to be associated with mature and severe storms, and the coincidence suggests that NBEs can as well be considered

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FIG. 5. LASA observation of a frontal storm on 8 Apr 2004. (a) Temporal and spatial evolution of the storm. Colors and source locations indicate storm became active at 0800 UTC (0400 local time) west of the Florida Peninsula and moved eastward to the Atlantic Ocean during a period of about 14 h. (b) Flashes classified to four types and are marked by different colors. ICs were plotted first and were subsequently overlaid by ⫺CGs, NBEs, and ⫹CGs.

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FIG. 6. Flash classification of airmass storms on 6 Aug 2004. Most activity occurred between 2000 and 2400 UTC (1600–2000 local time). Notice that NBEs were detected from all storm cells.

indications of mature and severe storms. The cell that followed the southernmost track was small in size and produced few if any ⫹CGs and NBEs. The exact reason behind the uneven preferences is beyond the scope of this paper and further studies exploiting the simultaneous radar observation are needed. In contrast to the frontal storm, observations of airmass storms on 6 August 2004 indicated that all welldeveloped storm cells produced a similar rate of NBEs, as shown in Fig. 6. The storms in this figure occurred mostly between 1600 and 2000 local time and each cell lasted about 1–2 h, which is typical of summer afternoon storms in Florida. In this case, a total of 26 000 flashes were detected, and the average IC/CG ratio is about 3.1. It is clear that the total number of detectable lightning events decreases with distance, as will be discussed in section 4c. However, this is less likely to affect the relative ratio of NBEs over the total lightning, because NBEs were found to have similar peak field changes as that of typical return strokes (Smith et al. 1999). In Fig. 5b, the cells following a track that was immediately south of the NBE-rich cells were detected with no or little NPEs even though the two tracks are not much different in terms of distance from the array center. This further shows that the relative ratio of NBEs is not affected by the distance-dependent detection efficiency, but rather reflects the actual ratio.

b. Location accuracy As having been shown in Fig. 2, LASA-located lighting activity was tightly associated with the active regions of thunderstorms. To better understand the LASA’s location accuracy, a simple modeling study was carried out. It is evident that the location accuracy for a DTOA system first depends on the timing accuracy of the signals. Although the new GPS receivers have an absolute timing accuracy of 50 ns, the effective accuracy also depends on the frequency bandwidth of the recorded signal. As described earlier, signals from the sensors are low-pass filtered at 500 kHz. Our calibration showed that the system has a flat response from 200 Hz to 500 kHz. The time uncertainty ⌬t and the frequency bandwidth ⌬f roughly follow the relation of ⌬t⌬f ⱖ 1/4␲ (Jackson 1975, p. 301). Given the system’s bandwidth, it has a timing uncertainty of about 0.2 ␮s, if an ideal impulse is considered. Lightning signals are certainly not ideal impulses, their amplitude rolls off with increasing frequencies, either inherited in the signals themselves or by propagation attenuation. The timing uncertainty therefore will increase further from 0.2 ␮s for actual lightning signals. The new data acquisition system samples the signal at 2 MSPS and each point has a time resolution of 0.5 ␮s, comparable to the sensor response.

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ger site is at the midpoint between Jacksonville (JAX) and Gainesville (GNV), Florida, and is at the northern edge of the dense LASA array (Fig. 2). The peak current for the 13 return strokes ranged from 3.6 to 17.8 kA, with 9 of them below 10 kA. These strokes are commonly weaker than natural return strokes. All of the strokes were detected by one or more LASA stations, but only eight of them were detected by four or more stations. A minimum of four stations is required to generate a valid location by the least ␹2 algorithm. The location errors for the located return strokes are in the range of 20–586 m, with a median value of 131 m. These limited experimental observations are more accurate than the modeled result, apparently because of the conservative overestimated timing errors assumed in the simulation.

c. Sensitivity FIG. 7. Simulated location accuracy for LASA in Florida. All eight stations are assumed to contribute to the solution.

In the following simulation, an rms value of 1 ␮s is assumed for the timing accuracy, which is overestimated for sources near the array and is underestimated for sources at distance resulting from propagation attenuation. In the simulation, the exact arrival time at each station is computed for a given source location, and a random time error that follows a Gaussian distribution of 1-␮s rms is added to the exact time. With the set of simulated arrival time measurements, a new location is computed. The distance between the true location and the computed location is considered the location error. This type of exercises is done 1000 times for each location and the averaged location error is registered as the error/accuracy for the specific source location. The simulation was conducted over an area of 20° (latitude) ⫻ 20° (longitude) centered near the center of the array [Palatka (PLK)], and the examined source locations are separated by 0.1° from each other. Figure 7 shows the simulated results, assuming all eight stations contribute to the measurement. It can be seen that over the dense array, the location error is less then 500 m, and the error increases steadily for more distant sources. It should be noted that for sources several hundred kilometers away, the location errors are mainly along the radial direction but not across the azimuthal direction, common to all DTOA systems (Thomas et al. 2004). On 23 June and 24 July 2004, the lightning group at the University of Florida (led by V. Rakov) triggered five lightning flashes that produced a total of 13 negative return strokes at Camp Blanding, Florida. The trig-

As mentioned earlier, all LASA sensors were situated on top of buildings in the eight campuses. The height and dimension of the buildings, as well as the surrounding environment, vary from site to site, so that the sensitivity [Aeff in Eq. (1)] differs from station to station, even though the sensors are identical to each other. The relative sensitivities among the eight stations are listed in Table 1, and were derived from distant lightning observations. Several tens of lightning events that occurred beyond a 2000-km range were used for the relative calibration. Because the lightning distance is much greater than the station separations, the lightning events can be treated as common sources for all the stations. As expected, the sensor that is situated on the tallest building at Daytona Beach (DAB) has the greatest sensitivity, whereas the sensor that is situated on a horizontally extensive building in GNV has the least sensitivity. The relative calibration was referenced to the central station at PLK. An absolute calibration was then conducted for PLK by comparing common lightning signals detected by the array sensor on the rooftop and a calibrating sensor on the ground. The sensing plate of the calibration sensor was level with the surface of the ground in an open area and was about 50 m from the ⬃10 m tall building. Absolute calibrations for the rest of the stations were then determined through the relative comparisons to the Palatka station. After the relative and absolute calibrations, the trigger thresholds among the eight stations can be coordinated into a common range. During the period from April to September 2004, the trigger thresholds were set to values between 0.8 and 1.5 V m⫺1 over the floating background interference. In late September, the trigger threshold was lowered to about ⫾0.5 V m⫺1, 1 month after the trigger length was shortened from 1 to

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FIG. 8. LASA daily trigger rate among all eight Florida stations from May 2004 to May 2005.

0.4 ms. For the purpose of comparison, the trigger threshold for previous LASA sensors was set at ⫾6 V m⫺1. Figure 8 shows the daily trigger rate of the upgraded Florida LASA during a 12-month period starting on 13 May 2004. Among the eight stations, from millions to tens of millions of data records can be obtained within a stormy day during the spring–summer seasons. The significant rate increase after March 2005, as compared to the same time period in 2004, was due to the lowered trigger threshold from around ⫾1 to ⫾0.4 V m⫺1, in spite of the shortened record length from 1 to 0.5 ms. On average, the trigger rate of the present system is about one to two orders of magnitude greater than that of the previous LASA. Electric field changes produced by either CGs or ICs are indiscriminately captured and processed by the LASA system. Figure 9 shows distributions of the geolocated flashes of the two types as a function of distance, based on 1 month of observations in August 2004. As shown in the figure, only those flashes that occurred within a 600-km range are considered for this analysis, which add up to a total of 1.7 million flashes. Each bin in the figure represents a normalized number of flashes that occurred within a 20-km-wide concentric ring around the center of the array (PLK). The normalized flash number was computed by dividing the total number of flashes within a ring by the area of the ring, so that fair comparison can be done over different ranges. It is shown that within the range of 100 km, LASA detected 2–5 times more ICs than CGs, in general agreement with the IC/CG ratios of the two storm systems in Figs. 5 and 6, and slightly greater than what have been previously reported for Florida storms (e.g., Livingston and Krider 1978; Boccippio et al. 2001). As the range increases to 200 km and beyond, LASA starts

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FIG. 9. Distribution of LASA located IC and CG flashes during August 2004. Flash numbers are normalized to unit area (1 km2) at each range.

to see more CGs than ICs, apparently because of the general disparity between return stroke currents and weaker IC discharge currents. Based on the same 1-month (August 2004) observation, Fig. 10 illustrates the minimum peak current that LASA was able to detect as a function of the lightning distance. The peak current for both CG and IC discharges was computed by the same method as that implemented by NLDN (Cummins et al. 1998). However, the method was developed specifically for return strokes and is not necessarily valid for IC processes. The IC peak currents presented here should be treated only as rough estimates. In Fig. 10, a positive current

FIG. 10. Peak current vs lightning distance in August 2004. Data gap at the middle shows the minimum peak current LASA was able to detect as a function of distance. Colors show event number density. Fewer events were captured at more distant ranges. Cross section at a given distance shows increasingly more events associated with smaller peak current.

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corresponds to ⫹CGs and IC pulses of positive polarity, and negative current corresponds to ⫺CGs and IC pulses of negative polarity. The edges of the wedgeshaped data gap between the two opposite polarities show the minimum detectable current of the LASA system. As expected, the minimum detectable current increases as the distance increases. Within the range of 100 km, LASA is able to detect events of a few kiloampere, as also demonstrated by the triggered lightning observations discussed earlier. At 600 km, LASA can only detect events with peak current greater than 25 kA. For the purpose of comparison, we found that for the eight triggered return strokes located by LASA, the peak currents inferred from LASA’s remote measurements are within a factor of 1.2 of those measured directly at the trigger site. The colors in the figure indicate the relative event density, and it is self-explanatory that increasingly fewer events were captured as distance increases because of the range loss, which is the same as that shown in Fig. 9. The other clear signature shown in Fig. 10 is the asymmetry between the two polarities, showing that the peak current of ⫺CGs and ICs of negative polarity spans a much wider range than that of ⫹CGs and ICs of positive polarity. It also shows that many more negative events were detected than the positive events at larger peak currents. Furthermore, in the distribution of the peak current it is evident that more small-current events were detected than big-current events, if a vertical cross section at a given distance range is considered.

d. New 3D location capability The redeployed LASA has six of eight stations within a relatively small area of ⬃100 km in diameter (Fig. 2). Together with more accurate time tagging of the signals, this allows some limited three-dimensional (3D) capability that did not exist for the previous LASA configurations. Figure 11 shows LASA’s preliminary 3D observations for a storm at the south edge of the dense array during a 4-min interval. The location algorithm here is the same as that for the 2D location, except for an additional altitude parameter. The black dots are located lightning sources, and the colored contours are simultaneous radar reflectivity obtained by a NEXRAD radar at Melbourne, Florida. The lower-left panel overlays the horizontal projection of the lightning sources on top of the radar reflectivity, in which the maximum reflectivity along the vertical columns is displayed. The two side panels show the vertical projections of the lightning sources over the vertical cross sections of the radar reflectivity, through the two per-

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pendicular lines shown in the lower-left panel. From the radar observation, this storm was apparently at a very active stage at which the 30-dBZ reflectivity reached beyond 17-km altitude, and the maximum reflectivity in the core was greater than 70 dBZ. During the 4-min time interval, a total of 724 lightning sources were located. The sources were found to be nicely associated with the active storm region and preferably at the top of the convective turret. Among the sources, 120 were located on or just above the ground (⬍2 km in altitude). Examination of the corresponding field change waveforms shows that they were all associated with return strokes or field change pulses near the return strokes, which further verifies the viability of the 3D algorithm. Nevertheless, LASA is not able to map detailed lightning channel structures like the VHF DTOA (e.g., Rison et al. 1999; Thomas et al. 2004) and interferometer (Rhodes et al. 1994; Shao et al. 1995; Shao and Krehbiel 1996) systems, because of the LF/VLF signals it observes. The VHF systems detect the radiation signals produced by smaller-scale breakdown processes, whereas LASA detects the field changes that are produced by larger-scale current transportation. The source for the latter often cannot be considered as a dimensionless point, and therefore its location determined with the DTOA technique will be intrinsically less accurate. Even though no detailed channel structure can be revealed, LASA can be used to monitor the overall storm development with the height and rate of the sources, without being overwhelmed by a great number of source points. In addition, LASA can easily pinpoint the ground strike points of return strokes and can readily distinguish between ICs and CGs based on the waveforms and the heights of the sources. In spite of distant lightning, LASA is able to see over the horizon, unlike a VHF system that is constrained by the direct line of sight, and can provide reasonable 2D locations. Therefore, for storm-level studies, LASA proves to be an extremely useful tool because of its total lightning (both IC and CG) capabilities. Similar to our dense array, an array that detects magnetic flux at the same VLF/LF frequency range is operated in Germany (Betz et al. 2004). That array is able to distinguish ICs from CGs based on the source height, and the source height is determined with a pseudo-3D technique that relies on the sensor closest to the lightning source.

5. Summary In this report, we described the upgraded LASA sensors, the operation of the reconfigured array, and dis-

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FIG. 11. The 3D location of lightning sources compared to radar reflectivity during a 4-min interval; (lower left) horizontal projection and (sides) vertical projections across the center of the storm. Total of 724 sources were located. Among them, 120 were on or near the ground (⬍2 km) and were CG return strokes.

cussed the much improved performance and capability. Compared to the previous LASA system, the new system detects and locates one–two orders of magnitude more lightning sources because of the improved electronics, the computer hardware, the floating triggering threshold, and the zero-dead-time data acquisition. For storms over or near the dense array, the new system has the capability to characterize the waveforms over the entire flash period. The original system, particular with its 30-ms dead times between triggers, would be unable to do so. In addition, a new, 3D capability for nearby (within a 100-km range) lightning discharges was presented. The purpose of this paper is to introduce the general capabilities of the upgraded LASA system. As mentioned at the beginning of this report, LASA has been deployed to support our satellite programs and has been required to record full-time waveforms of the

lightning field changes. As have been shown in the past (Smith et al. 2002, 2004) as well as in this paper, LASA has also proved to be a useful research tool for lightning and storm studies. For instance, LASA has captured detailed waveforms of a handful of lightning discharges that were associated with high-energy terrestrial ␥-ray flashes (TGF) detected by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) satellite (Smith et al. 2005). Analysis of the waveforms suggests that the TGFs are associated with IC discharges that occur at high altitudes (⬃13 km) (Stanley et al. 2006). LASA has also been used to verify the detection efficiency of the World-Wide Lightning Location (WWLL) network (Jacobson et al. 2006), and has been compared to the elves and sprite observations of the Republic of China Satellite (ROCSat-2; Heavner et al. 2004). Other studies utilizing LASA observations are now underway, which include 1) monitoring storm develop-

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FIG. 12. Great Plains array stations (black squares). Red dots are lightning activity during a 1-h time period (2300–2400 UTC) on 24 May 2005.

ment with the total lightning activity and comparing the results to radar measurements, 2) the occurrence of NBEs verse storm type and severity, 3) a detailed physical nature of NBP events, 4) discharge physics at the initial stage of lightning flashes by examining the field change waveforms at multiple stations, and 5) the VLF/ LF signal propagation and attenuation over different ground paths. At the time of this writing, a new array of six stations has been deployed in the Great Plains to provide a much wider spatial coverage to better support our satellite programs. As shown in Fig. 12, the interstation separations are in the range of ⬃500 km, which are much greater than that of the Florida array. At the same time, each station is more sensitive than the Florida stations with an estimated trigger threshold at or under ⫾0.2 V m⫺1. Data processing algorithms for the Great Plains array are currently under development and the algorithms will be different because of the longrange signal propagation and attenuation. Figure 12 shows a 1-h lightning observation on 24 May 2005 to demonstrate the potential capability. The Great Plains array is normally operated separately from the Florida array, but they can be operated together to improve the location accuracy and the detection efficiency for lightning discharges between the two arrays. For instance, combination of the two arrays provided successful light-

ning observations of Hurricanes Katrina and Rita, as reported by Shao et al. (2005b). In the future, we intend to further improve the LASA operation by employing the more advanced lightning location algorithms discussed by Koshak et al. (2000) and Hager and Wang (1995) that exploit all possible stations to obtain a set of linearized equations. Our current algorithm uses only four stations for the initial location guess. Koshak and Solakiewicz (2001) further introduced a linear algebraic solution for the oblate spheroidal earth geometry. This solution would provide a superior first guess as compared to the planar linear solution, especially for the Great Plains array that extends over a much larger area. Acknowledgments. The authors want to thank the following individuals and institutions who host LANL Sferic Array stations in Florida: Bill Embach and Allan Bonamy of the Daytona Beach Community College; Larry Barker, Richard Wendel, and Edward Wiley of the St. Johns River Community College at Palatka and St. Augustine; Brian DeCarlo, Vlad Rakov, and Martin Uman of the University of Florida; Mark Price, John Sarman, John Alexander, and David Lambert of the University of North Florida; Ron Kielty and Susan Cable of the Central Florida Community College; Bill Cottrill of the Florida State University; Mark Lefevre

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and David Rabson of the University of South Florida. The enthusiastic support of all these people made the success of this project. The authors also acknowledge Alfred Fernandez, Skyler Speakman, Bill Junor, and Dan Holden of Los Alamos National Laboratory for their help on the system upgrade, Dave Smith and Matt Heavner for their early effort on lightning type classification, and Kyle Wiens for his help on radar data induction. This work was performed in Los Alamos National Laboratory under the auspices of the U.S. Department of Energy. REFERENCES Betz, H.-D., K. Schmidt, P. Oettinger, and M. Wirz, 2004: Lightning detection with 3-D discrimination of intracloud and cloud-to-ground discharges. Geophys. Res. Lett., 31, L11108, doi:10.1029/2004GL019821. Boccippio, D. J., K. L. Cummins, H. J. Christian, and S. J. Goodman, 2001: Combined satellite- and surface-based estimation of the intracloud–cloud-to-ground lightning ratio over the continental United States. Mon. Wea. Rev., 129, 108–122. Cummins, K. L., M. J. Murphy, E. A. Bardo, W. L. Hiscox, R. B. Pyle, and A. E. Pifer, 1998: A combined TOA/MDF technology upgrade of the U.S. National Lightning Detection Network. J. Geophys. Res., 103 (D8), 9035–9044. Hager, W. W., and D. Wang, 1995: An analysis of errors in the location, current, and velocity of lightning. J. Geophys. Res., 100 (D12), 25 721–25 730. Heavner, M. J., D. M. Suszcynsky, and D. A. Smith, 2003: LF/ VLF intracloud waveform classification. Proc. 12th Int. Conf. on Atmospheric Electricity, Versailles, France, Centre National d’Etudes Spatiales, 601–604. ——, W. L. Boeck, X. M. Shao, H. U. Frey, and S. B. Mende, 2004: Ground based ISUAL validation with the Los Alamos sferic array. Eos, Trans. Amer. Geophys. Union, 85 (Fall Meeting Suppl.), AE31B-0166. Jackson, J. D., 1975: Classical Electrodynamics. 2d ed. John Wiley & Sons, 848 pp. Jacobson, A. R., and X. M. Shao, 2001: Using geomagnetic birefringence to located sources on impulsive, terrestrial VHF signals detected by satellites on orbit. Radio Sci., 36, 671–680. ——, and ——, 2002: On-orbit direction finding of lightning radiofrequency emissions recorded by the FORTE satellite. Radio Sci., 37, 1064, doi:10.1029/2001RS002510. ——, and M. J. Heavner, 2005: Comparison of narrow bipolar events with ordinary lightning as proxies for severe convection. Mon. Wea. Rev., 133, 1144–1154. ——, S. O. Knox, R. Franz, and D. C. Enemark, 1999: FORTE observations of lightning radio-frequency signatures: Capabilities and basic results. Radio Sci., 34, 337–354. ——, K. L. Cummins, M. Carter, P. Klingner, D. Roussel-Dupre, and S. O. Knox, 2000: FORTE radio-frequency observations of lightning strokes detected by the National Lightning Detection Network. J. Geophys. Res., 105 (D12), 15 653–15 662. ——, R. Holzworth, R. Dowden, J. Harlin, and E. Lay, 2006: Performance assessment of the World Wide Lightning Location Network (WWLL), using the Los Alamos Sferic Array (LASA) array as ground truth. J. Atmos. Oceanic Technol., 23, 1082–1092. Koshak, W. J., and R. J. Solakiewicz, 2001: TOA lightning loca-

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