Geostationary Lightning Mapper Research Articles

Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.

How Do Schumann Resonance Frequency Changes in the Vertical Electric Field Component Reflect Global Lightning Dynamics at Different Time Scales?

AbstractThe electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural “lightning antennas” excite the Earth‐ionosphere cavity confined between the Earth's surface and the lower ionosphere. The peak frequencies of SR are known to vary with source‐observer distance (SOD), while the daily frequency range (DFR: fmax − fmin) is also indicative of the average size of thunderstorm regions. This paper provides observational evidence for these relationships based on SR frequency observations of the vertical electric (EZ) field component at Nagycenk (NCK), Hungary in Central Europe from the period 1994–2015. Variations of the peak frequencies are considered on the annual, seasonal and diurnal time scales as well as during a specific event when squall‐line formation of lightning activity in South America moves toward NCK. DFR is studied in relation to the El Niño Southern Oscillation (ENSO). Increasing area of lightning activity in mid‐high Northern hemisphere latitudes has been identified by DFR variations during the transition from warm to cold episodes of the ENSO in 1998 and 2010. The extension of the lightning area is considered as a consequence of energy released in the tropics and exported to higher latitudes with some months of delay from the end of the El Niño episodes. The frequency variations are interpreted via model calculations and supported with satellite‐based optical lightning observations (Optical Transient Detector, Geostationary Lightning Mapper). The described variations of SR peak frequencies and DFR yield information on the global/regional lightning dynamics and on this basis they have important application to climate issues as well.

Quality Control of Geostationary Lightning Mapper Observations for Tropical Cyclone Applications

Abstract The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, ‘bar’ effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC wasn’t applied beforehand.

Continuing Current Seen Above and Below the Cloud: Comparing Observations From GLM and High‐Speed Video Cameras

AbstractThis study assesses the reliability and limitations of the Geostationary Lightning Mapper (GLM) in detecting continuing currents by comparing observations from ground‐based high‐speed cameras with GLM‐16 data. Our findings show that the GLM's one‐group detection efficiency (DE_1) is 53%, while the more stringent five‐consecutive‐group detection efficiency (DE_5) is 10%. Optical signals detected by the GLM predominantly occur during the early stages of continuing currents. Additionally, there is a notable disparity in detection efficiencies between positive and negative continuing currents, with positive continuing currents being detected more frequently. The application of the logistic regression model developed by Fairman and Bitzer (2022) further illustrates the limitations in continuing current identification. The study underscores the challenges of relying solely on satellite data to monitor and analyze continuing currents, emphasizing the need for advancements in detection technologies and methodologies to reliably detect continuing current at a large spatial scale.

Ground‐Based Observations of 337 nm and 777 nm Optical Emissions Produced by Lightning

AbstractLightning flashes were observed in Barrancabermeja (Colombia) by two photometers centered at 337 and 777 nm wavelengths, a high‐speed camera, and the Colombia Lightning Mapping Array. Both optical and VHF radio emissions of different lightning processes are investigated. Flash initiations by upward in‐cloud breakdown present a first short optical pulse followed by a period of ∼1 kHz breakdown propagation. The ratio remains at unity or below, associated with the transition to negative horizontal leader propagation. In flash initiations by a fast‐stepped negative downward leader to ground, a dominance of the 337 nm radiance is found. Observations of downward leaders before ground strokes reveal that the luminosity measured by the photometers is dominated most of the time by the light coming from the cloud and not from the visible leader channels. One case of a visible cloud‐to‐ground channel with positive continuing current shows a high similarity in the evolution of the luminosity along the channel, 777 nm radiance, and the energy detected by the Geostationary Lightning Mapper from space. Finally, two types of recoil leaders have been observed: connecting and non‐connecting. Whereas the measured 777 nm peak radiance is similar in both types of recoil leaders, connecting recoil leaders present about seven times higher 337 nm peak radiance. Our observations generally show agreement between the VHF power and optical emissions.

Determining the population of large meteoroids in major meteor showers

We have estimated the largest meteoroids present in major meteor showers from observations conducted between 2019-2022 by the Geostationary Lightning Mapper (GLM) instrument on the GOES-R satellites. Our integrated time area products for the Leonids, Perseids and eta Aquariids are of order 5 × 1010 km2 hours. We compute photometric masses for shower fireballs using the approach of Vojáček et al. (2022) to correct from narrow-band GLM luminosity to bolometric luminosity and apply the luminous efficiency relation of Ceplecha & McCrosky (1976) at high speeds. Between 2019 and 2022, the showers definitely observed by GLM were the Leonids, Perseids, and eta Aquariids, with probable detections of the Orionids and Taurids. We find the largest meteoroids to be of order 7 kg for the Leonids, 3 kg for the Perseids, and 3 kg for the eta Aquariids, corresponding to meteoroids of ≈0.2 m diameter. The Orionids and Taurids had maximum meteoroid masses of 4 kg and 150 kg respectively. The Leonids and eta Aquariids are well fit by a single power-law with differential mass exponent, s, of 2.08 ± 0.08 and 2.00 ± 0.09 over the mass range 10−7< m < 1 kg. All showers had maximum meteoroid masses compatible with Whipple gas-drag ejection, with the exception of the Perseids which have much larger meteoroids than expected a result also consistent with observations from ground based instruments. This may reflect preferential ejection in narrow jets or possibly some form of mantle erosion/release in the past for the parent comet, 109P/Swift-Tuttle.

Bayesian Analysis of the Detection Performance of the Lightning Imaging Sensors

Abstract Identical Lightning Imaging Sensors (LIS) aboard the Tropical Rainfall Measuring Mission satellite (TRMM LIS, 1998–2015) and International Space Station (ISS LIS, 2017–23) have provided over two decades of lightning observations over the global tropics, with ISS LIS extending coverage into the midlatitudes. Quantifying the detection performance of both LIS sensors is a necessary step toward generating a combined LIS climatological record and accurately combining LIS data with lightning detections from other sensors and networks. We compare lightning observations from both LIS sensors with reference sources including the Geostationary Lightning Mapper (GLM) and ground-based Earth Networks Total Lightning Network (ENTLN), Earth Networks Global Lightning Network (ENGLN), National Lightning Detection Network (NLDN), and Global Lightning Dataset (GLD360). Instead of a relative detection efficiency (DE) approach that assumes the perfect performance of the reference sensor, we employ a Bayesian approach to estimate the upper limit of the absolute DE (ADE) of each system being analyzed. The results of this analysis illustrate the geographical pattern of ADE as well as its diurnal cycle and yearly evolution. Reference network ADE increased by ∼15%–30% during the TRMM era, leading to a decline in TRMM LIS ADE. ISS LIS flash ADE has been relatively consistent at 61%–65%, about 4%–5% lower than TRMM LIS at the end of its lifetime.

Imaging of 3 bright terrestrial gamma-ray flashes by the atmosphere-space interactions monitor and their parent thunderstorms

The Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS) includes an instrument designed to geolocate Terrestrial Gamma-ray Flashes (TGF) produced by thunderstorms. It does so with a coded aperture system shadowing the pixelated Low Energy Detector of the Modular X- and Gamma-ray Sensor (MXGS). Additionally, it locates associated lightning flashes with the Modular Multispectral Imaging Array (MMIA). Here we present 3 bright TGFs with very similar duration, fluency and observation distance. The innovative imaging capabilities allow us to determine the TGF position and correlate the TGF-lightning parent event in time and position simultaneously. The accurate position determination and distance to the observer allow us to perform a comparative study of TGF characteristics. These TGFs were produced in association with lightning flashes below the highest cloud tops of developing to mature convective cells. In one event, GLM (Geostationary Lightning Mapper) cloud flash rates were slowing down after the TGF while negative cloud-to-ground flashes suddenly ceased from 10 to 5 min before the TGF. An 8-stroke (strongest: -93 kA) cloud-to-ground flash occurred 31 s before the TGF. Vertical profiles from the ERA5 reanalysis data suggest TGFs may be produced in variety of tropical environments.

Validating GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) Product over CONUS

Abstract Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN’s accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning.

Inter-Comparison of Lightning Measurements in Quasi-Linear Convective Systems

Data from four lightning networks collected during three quasi-linear convective systems (QLCS) are used to understand the differences in detection for optimizing their combined use. Additionally, using unique aspects from each network provides a more complete picture of lightning in a thunderstorm. The four lightning networks examined include a Lightning Mapping Array (LMA), the Earth Networks Total Lightning Network (ENTLN), the Geostationary Lightning Mapper (GLM), and the National Lightning Detection Network (NLDN). The data from each network are inter-matched and locations where each network uniquely detected a flash versus all are analyzed in reference to three QLCSs, including two QLCSs that occurred in the Southeast (22 March 2022 and 30 March 2022) during the Propagation, Evolution, and Rotation in Linear Systems (PERiLS) field campaign, and one case from Oklahoma (26 February 2023). Unique aspects of the lightning provided by each network are examined, including flash initiation altitude, size, type, and energy. Lightning flash trends and characteristics for each QLCS are similar between networks in general, but deviate in certain conditions and locations. Times of decreased matching between networks were associated with localized increases in lightning rates, smaller flash sizes, and lower-energy flashes. The differences in each network’s performance across the QLCSs demonstrates the importance of understanding the limitations in each and the advantage of using multiple networks.

The Impact of Single Group Flashes on Geostationary Lightning Mapper Data

AbstractThe Geostationary Lightning Mapper (GLM) is an instrument on the NOAA Geostationary Operational Environmental Satellites‐R (GOES‐R) Series. The GLM data are processed by the Lightning Cluster Filter Algorithm which filters the GLM data and clusters it into a series of events, groups, and flashes. One of the current filters in the GLM algorithm (as of June 2023) removes all flashes that have only a single group. Combining the flashes (including the flashes with only single group) from two of the GLMs (the GLM on GOES‐16 and the GLM on GOES‐17) in the region where they overlap produces a unique data set of coincident flashes (detected by both GLMs) and non‐coincident flashes (detected by only one GLM). Coincident flashes detected by both GLMs are very unlikely to be from noise sources. This data set allows us to estimate the impact of the single group flashes on the Detection Efficiency (DE) and the False Alarm Rate (FAR). We find that the single group flash filter does greatly improve the FAR but does slightly decrease the DE. Subsequent analysis shows that some of the removed flashes with single groups are from true lightning. A simple modification to the single group flash filter to only remove flashes with a single event increases the FAR without a corresponding improvement in DE. A more complex algorithm is needed to recover the single group flashes that are likely from lightning without adding back single group flashes that are from noise sources.

Automated and Objective Thunderstorm Identification and Tracking Using Geostationary Lightning Mapper (GLM) Data

Abstract A unique storm identification and tracking method is analyzed in varying storm environments within the United States spanning 273 hours in 2018. The methodology uses a quantity calculated through fusion of radar-based vertically integrated liquid (VIL) and satellite-based GLM flash rate density (FRD) called VILFRD to identify storms in space and time. This research analyzes GLM data use within VILFRD for the first time (method original: O), assesses four modifications to VILFRD implementation to find a more stable storm size with time (method new: N), larger storms (method original dilated: OD), or both (method new dilated: ND), and compares VILFRD methods with storm tracking using the 35-dBZ isosurface at −10°C (method non-VILFRD: NV). A case study analysis from 2019 is included to assess methods on a smaller scale and introduce a “lightning only” (LO) version of VILFRD. Large study results highlight that VILFRD-based storm identification produces smaller storms with more lightning than the NV method, and the NV method produces larger storms with a more stable size over time. Methods N and ND create smaller storm size fluctuations, but size changes more often. Dilation (OD, ND) creates larger storms and almost double the number of storms identified relative to nondilated methods (O, N, NV). The case study results closely resemble the large sample results and show that the LO method identifies storms with more lightning and shorter durations. Overall, these findings can aid in choice of storm tracking method based on desired user application and promote further testing of a lightning-only version of VILFRD.

Correcting for Undercounting of Lightning Flashes by Space‐Based Optical Sensors

AbstractAccurate measurements of global lightning are essential for understanding present and future atmospheric electricity, composition, and climate. The latest space‐based lightning detector, the Geostationary Lightning Mapper (GLM), was the first to be placed in geostationary orbit, with a continuous view of most of the American continents. Prior to the GLM, the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite collected lightning measurements from which numerous lightning climatologies have been developed, including those used in global models. However, this study finds that both the GLM and a second, similar LIS placed on the International Space Station (ISS) in 2017 detect lightning at similar rates and are undercounting lightning compared to ground‐based Lightning Mapping Arrays (LMAs). The GLM undercounts lightning by an average factor of 7.0, reaching a maximum over 120 as a function of satellite zenith angle, radar reflectivity at a height where the temperature is −10°C, flash height, and thunderstorm polarity. The LIS is estimated to undercount lightning by an average factor of 5.6, reaching a maximum of 75.0 as a function of radar reflectivity at the −10°C level, flash height, and thunderstorm polarity. Preliminary predictive equations for the GLM and LIS lightning undercount factor, or scaling factor (SF), use ice‐water content, equilibrium level, flash height, and satellite zenith angle, all of which can be derived in models. These equations are developed to encourage updating lightning parameterizations within global models and will likely increase modeled lightning's effects on atmospheric electrical circuits, composition, chemistry, and climate change.

Estimation of Lightning Flash Rate in Precipitation Features by Applying Shallow AI Neural Network Models to the GMI Passive Microwave Brightness Temperatures

AbstractThe satellite‐constellation passive‐microwave Brightness Temperature (TB) observations, with global coverage, and more additions from upcoming CubeSats, have been mainly used in surface precipitation retrievals. However, these observations can also be used to indicate the intensity of convective systems. This study attempts to relate Global Precipitation Mission (GPM) Microwave Imager (GMI) TBs to Geostationary Lightning Mapper (GLM) lightning flashes by using 4 years (02/2018–04/2022) of GPM Precipitation Feature (PF) database. GMI TBs are collocated to the GLM lightning counts, and to the ERA5 reanalysis 2‐m air temperature in PFs. Three Artificial Intelligent Neural Network Models (AI‐NN) are trained to classify PFs producing lightning in a 20‐min window respectively for land, ocean, or coast. The flash rate of the determined Lightning producing PFs (LPFs) is then quantified by three other AI‐NNs, each trained for one of the three regions. Though the models clearly capture the global geographical distribution of LPFs with a Probability Of Detection over 90%, high False Alarm Rates are found, ranging from 49.9% over land to 91.5% over the ocean. The importance of TB at each passive microwave channel varies regionally, corresponding to the different microphysical properties in various types of precipitation systems. The global lightning distribution is derived by applying the AI models to global PFs and is well compared to the lightning climatology from Lightning Imaging Sensor and Optical Transient Detector. This suggests that the use of passive microwave TBs can help to fill the gaps in lightning monitoring thanks to their global coverage.

Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

Forecast Applications of GLM Gridded Products: A Data Fusion Perspective

Abstract The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including flash extent density (FED), minimum flash area (MFA), and total optical energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April to 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210–230 K and isothermal reflectivity at −10°C of 20–30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts. Significance Statement The Geostationary Lightning Mapper (GLM) is a lightning sensor on the current generation of U.S. weather satellites. This research shows how data from the space-based lightning sensor can be combined with radar, satellite imagery, and ground-based lightning networks to improve how forecasters monitor thunderstorms and issue warnings for severe weather. The rate of GLM flashes detected and the area they cover correspond well with radar and satellite signatures, especially in cases of intense and severe thunderstorms. When the GLM observes the same thunderstorm from the GOES-East and GOES-West satellites, the optical energy (brightness) of the flashes may help forecasters interpret the types of flashes observed from each sensor.

Automated Lightning Jump (LJ) Detection from Geostationary Satellite Data

Abstract Rapid increases in the flash rate (FR) of a thunderstorm, so-called lightning jumps (LJs), have potential for nowcasting applications and to increase lead times for severe weather warnings. To date, there are some automated LJ algorithms that were developed and tuned for ground-based lightning locating systems. This study addresses the optimization of an automated LJ algorithm for the Geostationary Lightning Mapper (GLM) lightning observations from space. The widely used σ-LJ algorithm is used in its original form and in an adapted calculation including the footprint area of the storm cell (FRarea LJ algorithm). In addition, a new relative increase level (RIL) LJ algorithm is introduced. All algorithms are tested in different configurations, and detected LJs are verified against National Centers for Environmental Information severe weather reports. Overall, the FRarea algorithm with an activation FR threshold of 15 flashes per minute and a σ-level threshold of 1.0–1.5 as well as the RIL algorithm with FR threshold of 15 flashes per minute and RIL threshold of 1.1 are recommended. These algorithms scored the best critical success index (CSI) of ∼0.5, with a probability of detection of 0.6–0.7 and a false alarm ratio of ∼0.4. For daytime warm-season thunderstorms, the CSI can exceed 0.5, reaching 0.67 for storms observed during three consecutive days in April 2021. The CSI is generally lower at night and in winter.

On the Role of Continuing Currents in Lightning‐Induced Fire Ignition

AbstractLightning flashes are an important source of wildfires worldwide, contributing to the emission of trace gases to the atmosphere. Based on experiments and field observations, continuing currents in lightning have since a long time been proposed to play a significant role in the ignition of wildfires. However, simultaneous detections of optical and radio signals from fire‐igniting lightning confirming the role of continuing currents in igniting wildfires are rare. In this work, we first analyze the optical signal of the lightning‐ignited wildfires reported by the Geostationary Lightning Mapper over the Contiguous United States (CONUS) during the summer of 2018, and we then analyze the optical and the Extremely Low Frequency signal of a confirmed fire‐igniting lightning flash in the Swiss Alps. Despite data uncertainties, we found that the probability of ignition of a lightning flash with Continuing Current (CC) lasting more than 10 ms is higher than that of cloud‐to‐ground lightning in CONUS. Finally, we confirm the existence of a long CC (lasting about 400 ms) associated with a long‐lasting optical signal (lasting between 2 and 4 s) of a video‐recorded fire‐igniting lightning flash.

GOES GLM, biased bolides, and debiased distributions

The large combined field of view of the Geostationary Lightning Mapper (GLM) instruments onboard the GOES weather satellites makes them useful for studying the population of other atmospheric phenomena, such as bolides. Being a lightning mapper, GLM has many detection biases when applied to non-lightning and these systematics must be studied and properly accounted for before precise measurements of bolide flux can be ascertained. We developed a Bayesian Poisson regression model which simultaneously estimates instrumental biases and our statistic of principal interest: the latitudinal variation of bolide flux. We find that the estimated bias due to the angle of incident light upon the instrument corresponds roughly with the known sensitivity of the GLM instruments. We compare our latitudinal flux variation estimates to existing theoretical models and find our estimates consistent with GLM being strongly biased towards high-velocity bolides.

Validation of the Geostationary Lightning Mapper With a Lightning Mapping Array in Argentina: Implications for Current and Future Spaceborne Lightning Observations

AbstractA validation study of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite 16 (GOES‐16) was done using a ground‐based lightning mapping array (LMA) deployed as part of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign in Argentina. GLM detected lightning with 74.6% efficiency over 61 thunderstorm days in December 2018 through April 2019. However, GLM detection efficiency (DE) was negatively correlated (r = −0.49) with LMA flash rate. GLM DE also was negatively correlated with LMA flash altitude (r = −0.24), reflecting the influence of multiple competing trends. GLM DE was positively correlated (r = 0.27) with number of LMA sources in a flash, indicating improved DE for larger flashes. During periods with anomalously electrified storms, GLM DE was reduced to 50.9%. Statistics were found to be sensitive to analysis criteria, but most of the above trends remained consistent regardless of specific criteria. Because the methodology allowed a GLM flash to match more than one LMA flash, actual GLM flash rate was a factor of 2.9 lower than the LMA flash rate, and this ratio grew larger as LMA flash rate increased. A sensitivity study examined the impact of improved DE for smaller flashes; that is, an improved sensor (or algorithm) that was better able to detect and distinguish between separate small lightning flashes. The results showed improved correlation with LMA flash rates, as well as improved ability to identify lightning jumps associated with intensifying convection.