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Spatial Structure of Precipitation and Lightning in the Vicinity of Wildfire Ignitions in the United States

The horizontal storm structure surrounding 92512 lightning ignited wildfires is examined in the mid to eastern sections of the United States from 2003-2015 using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV gauge corrected radar precipitation mosaic, and the US Forest Service’s Fire Occurrence Database. Though lightning flash density peaks strongly around fire ignitions on the instantaneous 1 km scale, on the hourly 10 km scale both the lightning and precipitation peaks are typically offset from fire ignitions. As expected, lightning density is higher and precipitation lower around ignition points compared to non-ignition points. The average spatial distribution of total lightning flashes around fire ignitions is symmetrical, while that of precipitation and positive flashes is not. The results of regressing positive and negative currents against ignition efficiency are ambiguous. These results can aid in modeling storms that are likely to produce the "dry lightning" associated with wildfire ignitions.

Brian Vant-Hull↗

NASA NTRS2010

The Sao Paulo Lightning Mapping Array (SPLMA): Prospects to GOES-R GLM and CHUVA

This paper presents the characteristics and prospects of a Lightning Mapping Array to be deployed at the city of S o Paulo (SPLMA). This LMA network will provide CHUVA campaign with total lightning, lightning channel mapping and detailed information on the locations of cloud charge regions for the thunderstorms investigated during one of its IOP. The real-time availability of LMA observations will also contribute to and support improved weather situational awareness and mission execution. For GOES-R program it will form the basis of generating unique and valuable proxy data sets for both GLM and ABI sensors in support of several on-going research investigations

Albrecht, Rachel I.↗

NASA NTRS2014

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011 in the vicinity of Sao Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012 during the Vale do Paraiba campaign. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to 150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. As the CHUVA Vale do Paraiba campaign opportunity was formulated, a broad community-based interest developed for a comprehensive Lightning Location System (LLS) intercomparison and assessment study, leading to the participation and/or deployment of eight other ground-based networks and the space-based Lightning Imaging Sensor (LIS). The SP-LMA data is being intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment including noise reduction criteria, detection efficiency estimates, and statistical and climatological (both temporal and spatially) analyses for intercomparison studies and GOES-R proxy activities.

Bailey, J. C.↗

NASA NTRS1999

Global Lightning Variations Caused by Changes in Flash Rate and by Changes in Number of Thunderstorms

Observations from the Optical Transient Detector in space are used to examine the response of the number of storms and the total flash rate to thermodynamic forcing of deep convection on the diurnal and annual time scales. On the diurnal time scale, the flash rate appears to track with cloud buoyancy, and storm numbers dominate later in the cycle. On the annual time scale, almost all of the total lightning response is due to changes in the number of storms, with a negligible contribution from flash rate.

Williams, E.↗

NASA NTRS2015

Lightning Observations from the International Space Station (ISS) for Science Research and Operational Applications

There exist several core science applications of LIS lightning observations, that range from weather and climate to atmospheric chemistry and lightning physics due to strong quantitative connections that can be made between lightning and other geophysical processes of interest. The space-base vantage point, such as provided by ISS LIS, still remains an ideal location to obtain total lightning observations on a global basis.

International Space Station↗

NASA NTRS2009

Lightning Behavior and its Dependence on Storm Kinematic and Precipitation Processes in Northern Alabama

Numerous case studies and recent modeling studies have found that various metrics of updraft intensity appear to be reasonably well correlated to lightning production in thunderstorms, particularly severe thunderstorms. Indeed, the relationship between updraft and lightning flash rate is hypothesized to be the physical connection between a lightning "jump" signature and manifestations of severe weather such as tornadic activity. This study further examines this connection using a combination of dual Doppler wind retrievals made with the UAH ARMOR dual polarimetric and KHTX WSR 88D Doppler radar pair, together with northern Alabama Lightning Mapping Array (LMA) data. The dual Doppler data were used to construct three dimensional wind fields and the retrieved vertical velocity fields were subsequently compared to collocated total lightning flash rates observed by the LMA. Particular attention was paid to the timing of updraft pulses relative to changes in the flash rate, with the goal of assessing impacts on warning decision lead time. Results from the analysis of severe and non severe thunderstorms in Northern Alabama will be presented including the EF 4 tornado producing supercell on 6 February 2008.

Johnson, Elsie V.↗

NASA NTRS1983

Lightning propagation and flash density in squall lines as determined with radar

Lightning echo rise times and range-time variations due to discharge propagation are determined using S and L band radars, and the evolution of precipitation reflectivity and the associated lightning activity in squall lines is investigated using VHF and L band radars. The rise time of radar echoes can be explained by ionized channel propagation through the radar beams. Speeds of at least 250,000 m/s are found from measurements of the radial velocity of streamer propagation along the antenna beam. The range-time variations in lightning echoes indicate that either new ionization occurs as streamers develop into different parts of the cloud, channel delay occurs during which adequate ionization exists for radar detection, or continuing current occurs. Determinations of the lightning flash density for a squall line in the U.S. show that the maximum lightning density tends to be near the leading edge of the precipitation cores in developing cells. Long discharges are produced as a cell in the squall line develops and the total lightning density increases, although short discharges predominate. As the cell dissipates, short flashes diminish or cease and the long flashes dominate the lightning activity.

Mazur, V.↗

NASA NTRS1999

The Lightning Imaging Sensor

The Lightning Imaging Sensor (LIS) is a NASA Earth Observing System (EOS) instrument on the Tropical Rainfall Measuring Mission (TRMM) platform designed to acquire and investigate the distribution and variability of total lightning (i.e., cloud-to-ground and intracloud) between q35' in latitude. Since lightning is one of the responses of the atmosphere to thermodynamic and dynamic forcing, the LIS data is being used to detect deep convection without land-ocean bias, estimate the precipitation mass in the mixed phased region of thunderclouds, and differentiate storms with strong updrafts from those with weak vertical motion.

Christian, Hugh J.↗

NASA NTRS2014

Lightning Jump Algorithm Development for the GOES·R Geostationary Lightning Mapper

Current work on the lightning jump algorithm to be used in GOES‐R Geostationary Lightning Mapper (GLM)'s data stream is multifaceted due to the intricate interplay between the storm tracking, GLM proxy data, and the performance of the lightning jump itself. This work outlines the progress of the last year, where analysis and performance of the lightning jump algorithm with automated storm tracking and GLM proxy data were assessed using over 700 storms from North Alabama. The cases analyzed coincide with previous semi‐objective work performed using total lightning mapping array (LMA) measurements in Schultz et al. (2011). Analysis shows that key components of the algorithm (flash rate and sigma thresholds) have the greatest influence on the performance of the algorithm when validating using severe storm reports. Automated objective analysis using the GLM proxy data has shown probability of detection (POD) values around 60% with false alarm rates (FAR) around 73% using similar methodology to Schultz et al. (2011). However, when applying verification methods similar to those employed by the National Weather Service, POD values increase slightly (69%) and FAR values decrease (63%). The relationship between storm tracking and lightning jump has also been tested in a real‐time framework at NSSL. This system includes fully automated tracking by radar alone, real‐time LMA and radar observations and the lightning jump. Results indicate that the POD is strong at 65%. However, the FAR is significantly higher than in Schultz et al. (2011) (50‐80% depending on various tracking/lightning jump parameters) when using storm reports for verification. Given known issues with Storm Data, the performance of the real‐time jump algorithm is also being tested with high density radar and surface observations from the NSSL Severe Hazards Analysis & Verification Experiment (SHAVE).

Schultz. E.↗

NASA NTRS1990

Predicting thunderstorm evolution using ground-based lightning detection networks

Lightning measurements acquired principally by a ground-based network of magnetic direction finders are used to diagnose and predict the existence, temporal evolution, and decay of thunderstorms over a wide range of space and time scales extending over four orders of magnitude. The non-linear growth and decay of thunderstorms and their accompanying cloud-to-ground lightning activity is described by the three parameter logistic growth model. The growth rate is shown to be a function of the storm size and duration, and the limiting value of the total lightning activity is related to the available energy in the environment. A new technique is described for removing systematic bearing errors from direction finder data where radar echoes are used to constrain site error correction and optimization (best point estimate) algorithms. A nearest neighbor pattern recognition algorithm is employed to cluster the discrete lightning discharges into storm cells and the advantages and limitations of different clustering strategies for storm identification and tracking are examined.

Goodman, Steven J.↗

NASA NTRS2006

Improving Lightning NO(x) Parameterizations for Global Chemical Transport Models

Parameterization of lightning NO(x) in global chemical transport models requires a method to specify the temporal and geographic distribution of flash rates, an estimate of NO production per flash, and a method to specify the effective vertical distribution of NO production. Flash rate parameterizations using the upward convective cloud mass flux have been developed and tested for several GCM and assimilated meteorological data sets. This parameterization and others involving the cloud-top height and convective precipitation provide a generally acceptable climatological distribution of flashes after normalization with satellite data. However, considerable improvement in terms of geographic and temporal variability may be achieved in the future using cloud microphysics data available from next-generation GCMs. Cloud-resolving case-study simulations of convective transport and lightning NO production have yielded results which are directly applicable to the NO production per flash and the vertical NO distribution portions of the global model lightning parameterizations. In this work we have used cloud-resolving models (the Goddard Cumulus Ensemble Model (GCE) and MM5) to drive an off-line cloud-scale chemical transport model (CSCTM). The CSCTM, in conjunction with aircraft measurements of NO(x) in thunderstorms and ground-based lightning observations, has been used to constrain the amount of NO produced per flash. Observed lightning flash rates have been incorporated into the CSCTM, and several scenarios of NO production per intracloud (IC) and per cloud-to-ground (CG) flash have been tested in each of several case study storms. The resulting NO(x) mixing ratios are compared with aircraft measurements taken within the storm (typically the anvil region) to determine the most likely NO production scenario. The range of values of NO production per flash (or per meter of lightning channel length) that have been deduced from the model will be shown and compared with values of production in the literature that have been deduced from observed NO spikes and from anvil flux calculations. Results show that on a per flash basis, IC flashes are nearly as productive of NO as CG flashes. Estimates of mean NO production per flash vary by a factor of three from one simulated storm to another. Vertical profiles of lightning NOx mass at the end of the 3-D storm simulations have been summarized to yield suggested profiles for use in global models. These profiles show a larger percentage of the total lightning NO(x) mass in the free troposphere than those suggested by Pickering et al. (1998).

Pickering, Kenneth E.↗

NASA NTRS1992

Lightning Imaging Sensor (LIS) for the Earth Observing System

Not only are scientific objectives and instrument characteristics given of a calibrated optical LIS for the EOS but also for the Tropical Rainfall Measuring Mission (TRMM) which was designed to acquire and study the distribution and variability of total lightning on a global basis. The LIS can be traced to a lightning mapper sensor planned for flight on the GOES meteorological satellites. The LIS consists of a staring imager optimized to detect and locate lightning. The LIS will detect and locate lightning with storm scale resolution (i.e., 5 to 10 km) over a large region of the Earth's surface along the orbital track of the satellite, mark the time of occurrence of the lightning, and measure the radiant energy. The LIS will have a nearly uniform 90 pct. detection efficiency within the area viewed by the sensor, and will detect intracloud and cloud-to-ground discharges during day and night conditions. Also, the LIS will monitor individual storms and storm systems long enough to obtain a measure of the lightning flashing rate when they are within the field of view of the LIS. The LIS attributes include low cost, low weight and power, low data rate, and important science. The LIS will study the hydrological cycle, general circulation and sea surface temperature variations, along with examinations of the electrical coupling of thunderstorms with the ionosphere and magnetosphere, and observations and modeling of the global electric circuit.

Christian, Hugh J.↗

NASA NTRS2001

The Almena, Kansas, Tornadic Storm of 3 June 1999: A Long-Lived Supercell with Very Little Cloud-to-Ground Lightning

The visual, radar, and lightning characteristics of a severe thunderstorm that spawned a large F3 tornado near Almena, Kansas, on 3 June 1999 are documented. The storm is interesting in that it transitioned from a low-precipitation to classic supercell, then back to low-precipitation supercell again prior to dissipation after sunset. Remarkably, the storm produced only 17 cloud-to-ground lightning flashes during its 4.5 h lifetime, despite VIL values reaching 95 kg/sq m, reflectivities of 50 dBZ or greater at altitudes of 14 km, and baseball-size hail at the surface. In contrast, total lightning rates inferred from a portable lightning detector during the large tornado were very high, approximately 100/min as expected for a storm of this size and intensity.

McCaul, Eugene W., Jr.↗

NASA NTRS2003

Doppler Radar and Lightning Network Observations of a Severe Outbreak of Tropical Cyclone Tornadoes

Data from a single WSR-88D Doppler radar and the National Lightning Detection Network are used to examine in detail the characteristics of the convective storms that produced a severe tornado outbreak within Tropical Storm Beryl's remnants on 16 August 1994. Comparison of the radar data with reports of tornadoes suggests that only 13 cells produced the 29 tornadoes that were documented in Georgia and the Carolinas on that date. Six of these cells spawned multiple tornadoes, and the radar data confirm the presence of miniature supercells. One of the cells was identifiable on radar for 11 hours, spawning tornadoes over a time period spanning approximately 6.5 hours. Several other tornadic cells also exhibited great longevity, with cell lifetimes greater than ever previously documented in a landfalling tropical cyclone tornado event, and comparable to those found in major midlatitude tornadic supercell outbreaks. Time-height analyses of the three strongest tornadic supercells are presented in order to document storm kinematic structure and to show how these storms appear at different ranges from a WSR-88D radar. In addition, cloud-to-ground (CG) lightning data are examined for the outbreak, the most intense tropical cyclone tornado event studied thus far. Although the tornadic cells were responsible for most of Beryl's CG lightning, flash rates were only weak to moderate, even in the most intense supercells, and in all the tornadic storms the lightning flashes were almost entirely negative in polarity. A few of the single-tornado storms produced no detectable CG lightning at all. In the stronger cells, there is some evidence that CG lightning rates decreased during tornadogenesis, as has been documented before in some midlatitude tornadic storms. A number of the storms spawned tornadoes just after producing their final CG lightning flashes. Surprisingly, both peak currents and positive flash percentages were larger in Beryl s nontornadic storms than in the tornadic ones. Despite some intriguing patterns, the CG lightning behavior in this outbreak remains mostly inconsistent and ambiguous, and offers only secondary value for warning guidance. The present findings argue in favor of the implementation of observing systems capable of continuous monitoring of total lightning activity in storms.

McCaul, Eugene W., Jr.↗

NASA NTRS2024

Winter Lightning to the Lee of Lake Ontario: The Lake-Effect Electrification (LEE) Field Campaign

The National Science Foundation-sponsored Lake-Effect Electrification (LEE) field campaign intensive observation periods occurred between November and early February 2022-23 across the eastern Lake Ontario region. Project LEE documented, for the first time, the total lightning and electrical charge structures of lake-effect storms and the associated storm environment using a lightning mapping array (LMA), a mobile dual-polarization X-band radar, and balloon-based soundings that measured vertical profiles of temperature, humidity, wind, electric field, and hydrometeor types. LEE also observed abundant wind turbine-initiated lightning, which is climatologically more likely during the winter. The frequent occurrence of intense lake-effect storms and the proximity of a wind farm with nearly 300 turbines each more than 100 m tall to the lee of Lake Ontario provided an ideal laboratory for this study.

Scott M Steiger↗

NASA NTRS2014

Colorado Lightning Mapping Array Collaborations through the GOES-R Visiting Scientist Program

For the past two years, the GOES‐R Proving Ground has solicited proposals for its Visiting Scientist Program. NASA's Short‐term Prediction Research and Transition (SPoRT) Center has used this opportunity to support the GOES‐R Proving Ground by expanding SPoRT's total lightning collaborations. In 2012, this expanded the evaluation of SPoRT's pseudo‐geostationary lightning mapper product to the Aviation Weather Center and Storm Prediction Center. This year, SPoRT has collaborated with the Colorado Lightning Mapping Array (COLMA) and potential end users. In particular, SPoRT is collaborating with the Cooperative Institute for Research in the Atmosphere (CIRA) and Colorado State University (CSU) to obtain these data in real‐time. From there, SPoRT is supporting the transition of these data to the local forecast offices in Boulder, Colorado and Cheyenne, Wyoming as well as to Proving Ground projects (e.g., the Hazardous Weather Testbed's Spring Program and Aviation Weather Center's Summer Experiment). This presentation will focus on the results of this particular Visiting Scientist Program trip. In particular, the COLMA data are being provided to both forecast offices for initial familiarization. Additionally, several forecast issues have been highlighted as important uses for COLMA data in the operational environment. These include the utility of these data for fire weather situations, situational awareness for both severe weather and lightning safety, and formal evaluations to take place in the spring of 2014.

Stano, Geoffrey T.↗

NASA NTRS2012

Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and Other Meteorological Measurements

The lightning jump algorithm has a robust history in correlating upward trends in lightning to severe and hazardous weather occurrence. The algorithm uses the correlation between the physical principles that govern an updraft's ability to produce microphysical and kinematic conditions conducive for electrification and its role in the development of severe weather conditions. Recent work has demonstrated that the lightning jump algorithm concept holds significant promise in the operational realm, aiding in the identification of thunderstorms that have potential to produce severe or hazardous weather. However, a large amount of work still needs to be completed in spite of these positive results. The total lightning jump algorithm is not a stand-alone concept that can be used independent of other meteorological measurements, parameters, and techniques. For example, the algorithm is highly dependent upon thunderstorm tracking to build lightning histories on convective cells. Current tracking methods show that thunderstorm cell tracking is most reliable and cell histories are most accurate when radar information is incorporated with lightning data. In the absence of radar data, the cell tracking is a bit less reliable but the value added by the lightning information is much greater. For optimal application, the algorithm should be integrated with other measurements that assess storm scale properties (e.g., satellite, radar). Therefore, the recent focus of this research effort has been assessing the lightning jump's relation to thunderstorm tracking, meteorological parameters, and its potential uses in operational meteorology. Furthermore, the algorithm must be tailored for the optically-based GOES-R Geostationary Lightning Mapper (GLM), as what has been observed using Very High Frequency Lightning Mapping Array (VHF LMA) measurements will not exactly translate to what will be observed by GLM due to resolution and other instrument differences. Herein, we present some of the promising aspects and challenges encountered in utilizing objective tracking and GLM proxy data, as well as recent results that demonstrate the value added information gained by combining the lightning jump concept with traditional meteorological measurements.

Schultz, Christopher J.↗

NASA NTRS2009

Satellite Proving Ground for the GOES-R Geostationary Lightning Mapper (GLM)

The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products.

Goodman, Steven J.↗