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- Author or Editor: Phillip Bitzer x
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Abstract
The timing uncertainty of the Lightning Imaging Sensor (LIS), on orbit, is not currently accurately known. This is due to an imprecise value of the frame rate in the literature; the conceptual design value of 500 frames per second (fps) is often quoted. As researchers explore more ways to apply LIS data—in particular, the utility of group-level data, which correspond to strokes—a more precise value of the frame rate and timing uncertainty is important for proper understanding and use of the data. In this study, the average on-orbit frame rate was documented. From this, the timing uncertainty for LIS data was determined. Using on-orbit LIS data, the average frame rate of LIS is 558.58 fps and the timing uncertainty for LIS groups and events is 250 μs. It is shown that this uncertainty is associated with the quantization of the time of each frame. Further, the source time of optical pulses from lightning can have a bias that is not currently accounted for in the LIS data. This study shows how this correction can be on the order of the timing uncertainty, and a method in which this correction can be determined is outlined.
Abstract
The timing uncertainty of the Lightning Imaging Sensor (LIS), on orbit, is not currently accurately known. This is due to an imprecise value of the frame rate in the literature; the conceptual design value of 500 frames per second (fps) is often quoted. As researchers explore more ways to apply LIS data—in particular, the utility of group-level data, which correspond to strokes—a more precise value of the frame rate and timing uncertainty is important for proper understanding and use of the data. In this study, the average on-orbit frame rate was documented. From this, the timing uncertainty for LIS data was determined. Using on-orbit LIS data, the average frame rate of LIS is 558.58 fps and the timing uncertainty for LIS groups and events is 250 μs. It is shown that this uncertainty is associated with the quantization of the time of each frame. Further, the source time of optical pulses from lightning can have a bias that is not currently accounted for in the LIS data. This study shows how this correction can be on the order of the timing uncertainty, and a method in which this correction can be determined is outlined.
Evaluation of the Performance Characteristics of the Lightning Imaging SensorAbstract
The Lightning Imaging Sensor (LIS) that was on board the Tropical Rainfall Measuring Mission (TRMM) satellite captured optical emissions produced by lightning. In this work, we quantify and evaluate the LIS performance characteristics at both the pixel level of LIS events and contiguous clusters of events known as groups during a recent 2-yr period. Differences in the detection threshold among the four quadrants in the LIS pixel array produce small but meaningful differences in their optical characteristics. In particular, one LIS quadrant (Q1, X ≥ 64; Y ≥ 64) detects 15%–20% more lightning events than the others because of a lower detection threshold. Sensitivity decreases radially from the center of the LIS array to the edges because of sensor optics. The observed falloff behavior is larger on orbit than was measured during the prelaunch laboratory calibration and is likely linked to changes in cloud scattering pathlength with instrument viewing angle. Also, a two-season comparison with the U.S. National Lightning Detection Network (NLDN) has uncovered a 5–7-km north–south LIS location offset that changes sign because of periodic TRMM yaw maneuvers. LIS groups and flashes that had any temporally and spatially corresponding NLDN reports (i.e., NLDN reported the radio signals from the same group and/or from other groups in the same flash) tended to be spatially larger and last longer (only for flashes) than the overall population of groups/flashes.
Abstract
The Lightning Imaging Sensor (LIS) that was on board the Tropical Rainfall Measuring Mission (TRMM) satellite captured optical emissions produced by lightning. In this work, we quantify and evaluate the LIS performance characteristics at both the pixel level of LIS events and contiguous clusters of events known as groups during a recent 2-yr period. Differences in the detection threshold among the four quadrants in the LIS pixel array produce small but meaningful differences in their optical characteristics. In particular, one LIS quadrant (Q1, X ≥ 64; Y ≥ 64) detects 15%–20% more lightning events than the others because of a lower detection threshold. Sensitivity decreases radially from the center of the LIS array to the edges because of sensor optics. The observed falloff behavior is larger on orbit than was measured during the prelaunch laboratory calibration and is likely linked to changes in cloud scattering pathlength with instrument viewing angle. Also, a two-season comparison with the U.S. National Lightning Detection Network (NLDN) has uncovered a 5–7-km north–south LIS location offset that changes sign because of periodic TRMM yaw maneuvers. LIS groups and flashes that had any temporally and spatially corresponding NLDN reports (i.e., NLDN reported the radio signals from the same group and/or from other groups in the same flash) tended to be spatially larger and last longer (only for flashes) than the overall population of groups/flashes.
A Bayesian Approach to Assess the Performance of Lightning Detection SystemsAbstract
Historically, researchers explore the effectiveness of one lightning detection system with respect to another system; that is, the probability that system A detects a discharge given that system B detected the same discharge is estimated. Since no system detects all lightning, a more rigorous comparison should include the reverse process—that is, the probability that system B detects a discharge given that system A detected it. Further, the comparison should use the fundamental physical process detected by each system. Of particular interest is the comparison of ground-based radio frequency detectors with space-based optical detectors. Understanding these relationships is critical as the availability and use of lightning data, both ground based and space based, increases. As an example, this study uses Bayesian techniques to compare the effectiveness of the Earth Networks Total Lightning Network (ENTLN), a ground-based wideband network, and the Lightning Imaging Sensor (LIS), a space-based optical detector. This comparison is completed by matching LIS groups and ENTLN pulses, each of which correspond to stroke-type discharges. The comparison covers the period from 2009 to 2013 over several spatial domains. In 2013 LIS detected 52.0% of the discharges ENTLN reported within the LIS field of view globally and 53.2% near North America. Conversely, ENTLN detected 5.9% of the pulses detected by LIS globally and 26.9% near North America in 2013. Using these results in the Bayesian-based methodology outlined, the study finds that LIS detected 80.1% of discharges near North America in 2013, while ENTLN detected 40.1%.
Abstract
Historically, researchers explore the effectiveness of one lightning detection system with respect to another system; that is, the probability that system A detects a discharge given that system B detected the same discharge is estimated. Since no system detects all lightning, a more rigorous comparison should include the reverse process—that is, the probability that system B detects a discharge given that system A detected it. Further, the comparison should use the fundamental physical process detected by each system. Of particular interest is the comparison of ground-based radio frequency detectors with space-based optical detectors. Understanding these relationships is critical as the availability and use of lightning data, both ground based and space based, increases. As an example, this study uses Bayesian techniques to compare the effectiveness of the Earth Networks Total Lightning Network (ENTLN), a ground-based wideband network, and the Lightning Imaging Sensor (LIS), a space-based optical detector. This comparison is completed by matching LIS groups and ENTLN pulses, each of which correspond to stroke-type discharges. The comparison covers the period from 2009 to 2013 over several spatial domains. In 2013 LIS detected 52.0% of the discharges ENTLN reported within the LIS field of view globally and 53.2% near North America. Conversely, ENTLN detected 5.9% of the pulses detected by LIS globally and 26.9% near North America in 2013. Using these results in the Bayesian-based methodology outlined, the study finds that LIS detected 80.1% of discharges near North America in 2013, while ENTLN detected 40.1%.
Mitigating VHF Lightning Source Retrieval ErrorsAbstract
The problem of inferring the location and time of occurrence of a very high frequency (VHF) lightning source emission from Lightning Mapping Array (LMA) network time-of-arrival (TOA) measurements is closely examined in order to clarify the cause of retrieval errors and to determine how best to mitigate these errors. With regard to this inverse problem, the previous literature lacks a comprehensive discussion of the associated forward problem. Hence, the forward problem is analyzed in this study to better clarify why retrieval errors increase with increasing source horizontal range and/or decreasing source altitude. Further insight is obtained by performing carefully designed Monte Carlo inversion simulations that provide specific retrieval error plots, which in turn lead to clear recommendations for mitigating retrieval errors. Based on all of the numerical results, the following strategies are recommended for mitigating retrieval errors (when possible, and without obstructing the line of sight): expand the horizontal extent of the LMA network, maximize the vertical sensor baseline by using mountainous terrain if available, and improve TOA measurement timing accuracy. Adding sensors to the network is relatively ineffective, unless of course the addition of sensors expands the horizontal extent and/or vertical baseline of the network. It is also shown how the standard retrieval method can be generalized by considering, in addition to the regular (unpolarized) point VHF source, the polarized transient very low frequency/low frequency (VLF/LF) electric point dipole source. Multiple observations (i.e., VHF arrival time and power, and VLF/LF arrival time and electric field amplitude) are simultaneously implemented into the new generalized mathematical framework, and the potential benefits are indicated.
Abstract
The problem of inferring the location and time of occurrence of a very high frequency (VHF) lightning source emission from Lightning Mapping Array (LMA) network time-of-arrival (TOA) measurements is closely examined in order to clarify the cause of retrieval errors and to determine how best to mitigate these errors. With regard to this inverse problem, the previous literature lacks a comprehensive discussion of the associated forward problem. Hence, the forward problem is analyzed in this study to better clarify why retrieval errors increase with increasing source horizontal range and/or decreasing source altitude. Further insight is obtained by performing carefully designed Monte Carlo inversion simulations that provide specific retrieval error plots, which in turn lead to clear recommendations for mitigating retrieval errors. Based on all of the numerical results, the following strategies are recommended for mitigating retrieval errors (when possible, and without obstructing the line of sight): expand the horizontal extent of the LMA network, maximize the vertical sensor baseline by using mountainous terrain if available, and improve TOA measurement timing accuracy. Adding sensors to the network is relatively ineffective, unless of course the addition of sensors expands the horizontal extent and/or vertical baseline of the network. It is also shown how the standard retrieval method can be generalized by considering, in addition to the regular (unpolarized) point VHF source, the polarized transient very low frequency/low frequency (VLF/LF) electric point dipole source. Multiple observations (i.e., VHF arrival time and power, and VLF/LF arrival time and electric field amplitude) are simultaneously implemented into the new generalized mathematical framework, and the potential benefits are indicated.
Abstract
To increase understanding of the relationships between lightning and nonlightning convective storms, lightning observations from the National Aeronautics and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) campaign were analyzed with Meteosat Second Generation (MSG) geostationary satellite and S-band NASA Polarimetric Doppler Weather Radar (NPOL) data. The study’s goal was to analyze the time evolution of infrared satellite fields and ground-based polarimetric radar during NAMMA to quantify relationships between satellite and radar observations for lightning and nonlightning convective clouds over equatorial Africa. Using NPOL data, very low-frequency arrival time difference lightning data, and MSG Spinning Enhanced Visible and Infrared Imager observations, the physical attributes of growing cumulus clouds, including ice mass production, updraft strength, cloud depth, and cloud-top glaciation were examined. It was found that, on average, the lightning storms had stronger updrafts (seen in the satellite and radar fields), which lead to the formation of deeper clouds (seen in the satellite and radar fields) and subsequently much more ice in the mixed-phase region (as confirmed in radar observations), as well as much more nonprecipitating ice in the top 1 km of the cloud (as quantified in both satellite and radar fields) than the nonlightning storms. Computed radar-derived ice masses in cumulus clouds verifies the traditional MSG indicators of cloud-top glaciation, while NPOL verifies internal structures (i.e., large amounts of graupel) where satellite and radar show strong updrafts.
Abstract
To increase understanding of the relationships between lightning and nonlightning convective storms, lightning observations from the National Aeronautics and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) campaign were analyzed with Meteosat Second Generation (MSG) geostationary satellite and S-band NASA Polarimetric Doppler Weather Radar (NPOL) data. The study’s goal was to analyze the time evolution of infrared satellite fields and ground-based polarimetric radar during NAMMA to quantify relationships between satellite and radar observations for lightning and nonlightning convective clouds over equatorial Africa. Using NPOL data, very low-frequency arrival time difference lightning data, and MSG Spinning Enhanced Visible and Infrared Imager observations, the physical attributes of growing cumulus clouds, including ice mass production, updraft strength, cloud depth, and cloud-top glaciation were examined. It was found that, on average, the lightning storms had stronger updrafts (seen in the satellite and radar fields), which lead to the formation of deeper clouds (seen in the satellite and radar fields) and subsequently much more ice in the mixed-phase region (as confirmed in radar observations), as well as much more nonprecipitating ice in the top 1 km of the cloud (as quantified in both satellite and radar fields) than the nonlightning storms. Computed radar-derived ice masses in cumulus clouds verifies the traditional MSG indicators of cloud-top glaciation, while NPOL verifies internal structures (i.e., large amounts of graupel) where satellite and radar show strong updrafts.
Abstract
Relationships between lightning and lightning jumps and physical updraft properties are frequently observed and generally understood. However, a more intensive characterization of how lightning relates to traditional radar-based metrics of storm intensity may provide further operational utility. This study addresses the supercell storm mode because of the intrinsic relationship between a supercell’s characteristic rotating updraft–downdraft couplet, or mesocyclone, and its prolific ability to produce severe weather. Lightning and radar measurements of a diverse sample of 19 supercell thunderstorms were used to assess the conceptual model that lightning and the mesocyclone may be linked by the updraft’s role in the formation and enhancement of each. Analysis of early stages of supercell development showed that the initial lightning jump occurred prior to the time of mesocyclogenesis inferred from three methods by median values of 5–10 min. Comparison between lightning jumps and subsequent increases in mesocyclonic rotation indicated that lightning can also be used to infer or confirm imminent strengthening or reintensification of the mesocyclone. Stronger relationships emerged in supercells that exhibited more robust updrafts, in which 85% of lightning jumps were associated with at least one increase in rotation and 77% of observed increases in rotation were temporally associated with a lightning jump. Preliminary results from analysis of the relationship between lightning jumps and intensification of the low-level mesocyclone in tornadic supercells also offer motivation for the future analysis of lightning data with respect to downdraft-related processes.
Abstract
Relationships between lightning and lightning jumps and physical updraft properties are frequently observed and generally understood. However, a more intensive characterization of how lightning relates to traditional radar-based metrics of storm intensity may provide further operational utility. This study addresses the supercell storm mode because of the intrinsic relationship between a supercell’s characteristic rotating updraft–downdraft couplet, or mesocyclone, and its prolific ability to produce severe weather. Lightning and radar measurements of a diverse sample of 19 supercell thunderstorms were used to assess the conceptual model that lightning and the mesocyclone may be linked by the updraft’s role in the formation and enhancement of each. Analysis of early stages of supercell development showed that the initial lightning jump occurred prior to the time of mesocyclogenesis inferred from three methods by median values of 5–10 min. Comparison between lightning jumps and subsequent increases in mesocyclonic rotation indicated that lightning can also be used to infer or confirm imminent strengthening or reintensification of the mesocyclone. Stronger relationships emerged in supercells that exhibited more robust updrafts, in which 85% of lightning jumps were associated with at least one increase in rotation and 77% of observed increases in rotation were temporally associated with a lightning jump. Preliminary results from analysis of the relationship between lightning jumps and intensification of the low-level mesocyclone in tornadic supercells also offer motivation for the future analysis of lightning data with respect to downdraft-related processes.
Abstract
This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an additional 2175 lightning flashes within detected snowfall were identified that were not observed by the METAR reports, indicating that TSSN has been under reported in previous literature. TSSN flashes observed by GLM have mean flash areas, durations, and total optical energy outputs of 754 km2, 402 ms, and 1342 fJ, which are between the 50th and 99th percentile values for all flashes within the GLM field of view. A comparison with data from the National Lightning Detection Network (NLDN) indicated that the NLDN had at least one cloud or ground flash detection in 1709 of the 2214 flashes observed by GLM in snowfall. An average of 5.85 NLDN flashes was assigned to a single GLM flash when the NLDN flash data were constrained by the GLM flash duration and spatial footprint. Statistically significant (p < 0.01) differences in flash area and flash energy were found between flashes that were observed by the NLDN and those that were not. Additionally, when GLM was combined with the NLDN, at least 11.1% of flashes involved a tall human-made object like an antenna or wind turbine.
Abstract
This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an additional 2175 lightning flashes within detected snowfall were identified that were not observed by the METAR reports, indicating that TSSN has been under reported in previous literature. TSSN flashes observed by GLM have mean flash areas, durations, and total optical energy outputs of 754 km2, 402 ms, and 1342 fJ, which are between the 50th and 99th percentile values for all flashes within the GLM field of view. A comparison with data from the National Lightning Detection Network (NLDN) indicated that the NLDN had at least one cloud or ground flash detection in 1709 of the 2214 flashes observed by GLM in snowfall. An average of 5.85 NLDN flashes was assigned to a single GLM flash when the NLDN flash data were constrained by the GLM flash duration and spatial footprint. Statistically significant (p < 0.01) differences in flash area and flash energy were found between flashes that were observed by the NLDN and those that were not. Additionally, when GLM was combined with the NLDN, at least 11.1% of flashes involved a tall human-made object like an antenna or wind turbine.
The RELAMPAGO Lightning Mapping Array: Overview and Initial Comparison with the Geostationary Lightning MapperAbstract
During November 2018–April 2019, an 11-station very high frequency (VHF) Lightning Mapping Array (LMA) was deployed to Córdoba Province, Argentina. The purpose of the LMA was validation of the Geostationary Lightning Mapper (GLM), but the deployment was coordinated with two field campaigns. The LMA observed 2.9 million flashes (≥ five sources) during 163 days, and level-1 (VHF locations), level-2 (flashes classified), and level-3 (gridded products) datasets have been made public. The network’s performance allows scientifically useful analysis within 100 km when at least seven stations were active. Careful analysis beyond 100 km is also possible. The LMA dataset includes many examples of intense storms with extremely high flash rates (>1 s−1), electrical discharges in overshooting tops (OTs), as well as anomalously charged thunderstorms with low-altitude lightning. The modal flash altitude was 10 km, but many flashes occurred at very high altitude (15–20 km). There were also anomalous and stratiform flashes near 5–7 km in altitude. Most flashes were small (<50 km2 area). Comparisons with GLM on 14 and 20 December 2018 indicated that GLM most successfully detected larger flashes (i.e., more than 100 VHF sources), with detection efficiency (DE) up to 90%. However, GLM DE was reduced for flashes that were smaller or that occurred lower in the cloud (e.g., near 6-km altitude). GLM DE also was reduced during a period of OT electrical discharges. Overall, GLM DE was a strong function of thunderstorm evolution and the dominant characteristics of the lightning it produced.
Abstract
During November 2018–April 2019, an 11-station very high frequency (VHF) Lightning Mapping Array (LMA) was deployed to Córdoba Province, Argentina. The purpose of the LMA was validation of the Geostationary Lightning Mapper (GLM), but the deployment was coordinated with two field campaigns. The LMA observed 2.9 million flashes (≥ five sources) during 163 days, and level-1 (VHF locations), level-2 (flashes classified), and level-3 (gridded products) datasets have been made public. The network’s performance allows scientifically useful analysis within 100 km when at least seven stations were active. Careful analysis beyond 100 km is also possible. The LMA dataset includes many examples of intense storms with extremely high flash rates (>1 s−1), electrical discharges in overshooting tops (OTs), as well as anomalously charged thunderstorms with low-altitude lightning. The modal flash altitude was 10 km, but many flashes occurred at very high altitude (15–20 km). There were also anomalous and stratiform flashes near 5–7 km in altitude. Most flashes were small (<50 km2 area). Comparisons with GLM on 14 and 20 December 2018 indicated that GLM most successfully detected larger flashes (i.e., more than 100 VHF sources), with detection efficiency (DE) up to 90%. However, GLM DE was reduced for flashes that were smaller or that occurred lower in the cloud (e.g., near 6-km altitude). GLM DE also was reduced during a period of OT electrical discharges. Overall, GLM DE was a strong function of thunderstorm evolution and the dominant characteristics of the lightning it produced.
Abstract
This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in the Córdoba and Mendoza provinces in Argentina and western Rio Grande do Sul State in Brazil in 2018–19 that involved more than 200 scientists and students from the United States, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates, and other unusual lightning phenomena, but few tornadoes. The five distinct scientific foci of RELAMPAGO—convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification—are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multinational education, public outreach, and social media data gathering associated with the campaign, is summarized.
Abstract
This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in the Córdoba and Mendoza provinces in Argentina and western Rio Grande do Sul State in Brazil in 2018–19 that involved more than 200 scientists and students from the United States, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates, and other unusual lightning phenomena, but few tornadoes. The five distinct scientific foci of RELAMPAGO—convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification—are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multinational education, public outreach, and social media data gathering associated with the campaign, is summarized.
