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Abstract
A two-dimensional cloud-resolving simulation is combined with dual-Doppler and polarimetric radar analysis to study the evolution, dynamic structure, cloud microphysics, and rainfall processes of monsoon convection observed during the South China Sea (SCS) summer monsoon onset.
Overall, the model simulations show many similarities to the radar observations. The rainband associated with the convection remains at a very stable position throughout its life cycle in the northern SCS. The reflectivity pattern exhibits a straight upward structure with little tilt. The positions of the convective, transition, and stratiform regions produced by the model are consistent with the observations. The major difference from the observations is that the model tends to overestimate the magnitude of updraft. As a result, the maximum reflectivity generated by the model appears at an elevated altitude.
The surface rainfall processes and associated thermodynamic, dynamic, and cloud microphysical processes are examined by the model in terms of surface rainfall, temperature and moisture perturbations, circulations, and cloud microphysical budget. At the preformation and dissipating stages, although local vapor change and vapor convergence terms are the major contributors in determining rain rate, they cancel each other out and cause little rain. The vapor convergence/divergence is closely related to the lower-tropospheric updraft/subsidence during the early/late stages of the convection. During the formation and mature phases, vapor convergence term is in control of the rainfall processes. Meanwhile, water microphysical processes are dominant in these stages. The active vapor condensation process causes a large amount of raindrops through the collection of cloud water by raindrops. Ice microphysical processes including riming are negligible up to the mature phase but are dominant during the weakening stage. Cloud source/sink terms make some contributions to the rain rate at the formation and weakening stages, while the role of surface evaporation term is negligible throughout the life cycle of the convection.
Abstract
A two-dimensional cloud-resolving simulation is combined with dual-Doppler and polarimetric radar analysis to study the evolution, dynamic structure, cloud microphysics, and rainfall processes of monsoon convection observed during the South China Sea (SCS) summer monsoon onset.
Overall, the model simulations show many similarities to the radar observations. The rainband associated with the convection remains at a very stable position throughout its life cycle in the northern SCS. The reflectivity pattern exhibits a straight upward structure with little tilt. The positions of the convective, transition, and stratiform regions produced by the model are consistent with the observations. The major difference from the observations is that the model tends to overestimate the magnitude of updraft. As a result, the maximum reflectivity generated by the model appears at an elevated altitude.
The surface rainfall processes and associated thermodynamic, dynamic, and cloud microphysical processes are examined by the model in terms of surface rainfall, temperature and moisture perturbations, circulations, and cloud microphysical budget. At the preformation and dissipating stages, although local vapor change and vapor convergence terms are the major contributors in determining rain rate, they cancel each other out and cause little rain. The vapor convergence/divergence is closely related to the lower-tropospheric updraft/subsidence during the early/late stages of the convection. During the formation and mature phases, vapor convergence term is in control of the rainfall processes. Meanwhile, water microphysical processes are dominant in these stages. The active vapor condensation process causes a large amount of raindrops through the collection of cloud water by raindrops. Ice microphysical processes including riming are negligible up to the mature phase but are dominant during the weakening stage. Cloud source/sink terms make some contributions to the rain rate at the formation and weakening stages, while the role of surface evaporation term is negligible throughout the life cycle of the convection.
Abstract
Three methodologies for correcting the radar reflectivity factor (Z H) in the presence of partial beam blockage are implemented, compared, and evaluated using a polarimetric radar dataset from the North American Monsoon Experiment (NAME) in northwestern Mexico. One methodology uses simulated interactions between radar beams and digital terrain maps, while the other two invoke the self-consistency of polarimetric radar measurands in rainfall, and the relative insensitivity of a specific differential phase to beam blockage. While the different methodologies often agree to within 1–2 dB, significant disagreements can occur in regions of sharp azimuthal gradients in beam blockage patterns, and in areas where the terrain-caused radar clutter map is complex. These disagreements may be mitigated by the use of additional radar data to develop the polarimetric correction techniques, by a more sophisticated terrain-beam interaction model, or by a higher-resolution digital terrain map. Intercomparisons between ground radar data and Tropical Rainfall Measuring Mission satellite overpasses suggest that all of the methodologies can correct mean Z H to within the expected uncertainty of such intercomparisons (1–1.5 dB). The polarimetric correction methods showed good results even in severely blocked regions (>10 dB reduction). The results suggest the possibility that all of the techniques may be valid approaches to correcting partial beam blockage, and within that context relative advantages and disadvantages of each technique are discussed. However, none of the techniques can correct radar data when weak echoes are reduced to noise by strong blocks, thus leading to biases in corrected Z H and rainfall climatologies.
Abstract
Three methodologies for correcting the radar reflectivity factor (Z H) in the presence of partial beam blockage are implemented, compared, and evaluated using a polarimetric radar dataset from the North American Monsoon Experiment (NAME) in northwestern Mexico. One methodology uses simulated interactions between radar beams and digital terrain maps, while the other two invoke the self-consistency of polarimetric radar measurands in rainfall, and the relative insensitivity of a specific differential phase to beam blockage. While the different methodologies often agree to within 1–2 dB, significant disagreements can occur in regions of sharp azimuthal gradients in beam blockage patterns, and in areas where the terrain-caused radar clutter map is complex. These disagreements may be mitigated by the use of additional radar data to develop the polarimetric correction techniques, by a more sophisticated terrain-beam interaction model, or by a higher-resolution digital terrain map. Intercomparisons between ground radar data and Tropical Rainfall Measuring Mission satellite overpasses suggest that all of the methodologies can correct mean Z H to within the expected uncertainty of such intercomparisons (1–1.5 dB). The polarimetric correction methods showed good results even in severely blocked regions (>10 dB reduction). The results suggest the possibility that all of the techniques may be valid approaches to correcting partial beam blockage, and within that context relative advantages and disadvantages of each technique are discussed. However, none of the techniques can correct radar data when weak echoes are reduced to noise by strong blocks, thus leading to biases in corrected Z H and rainfall climatologies.
Abstract
Lightning stroke data from both the World Wide Lightning Location Network (WWLLN) and the Earth Networks Total Lightning Network (ENTLN) were compared to lightning group data from the Lightning Imaging Sensor (LIS) from 1 January 2010 through 30 June 2011. The region of study, from 39°S to 39°N latitude, chosen based on the orbit of LIS, and 164°E east to 17°W longitude, chosen to approximate the possible Geostationary Lightning Mapper (GLM) longitude, was considered in its entirety and then divided into geographical subregions. Over this 18-month time period, WWLLN had an 11.0% entire region, 13.2% North American, 6.2% South American, 16.4% Atlantic Ocean, and 18.9% Pacific Ocean coincidence percent (CP) value. The ENTLN CP values were 28.5%, 63.3%, 2.2%, 3.0%, and 2.5%, respectively. During the 18 months, WWLLN CP values remained rather consistent but low and often higher over ocean than land; ENTLN CP values showed large spatial and temporal variability. With both networks, North America had less variability during summer months than winter months and higher CP values during winter months than summer months. The highest ENTLN CP values were found in the southeastern United States, especially in a semicircle that extended from central Oklahoma, through Texas, along the northern Gulf of Mexico, across southern Florida, and along the U.S. East Coast. There was no significant change in CP values over time; the lowest monthly North American ENTLN CP value was found in June 2011 at 48.1%, the last month analyzed. These findings are consistent with most ENTLN sensors being located in the United States.
Abstract
Lightning stroke data from both the World Wide Lightning Location Network (WWLLN) and the Earth Networks Total Lightning Network (ENTLN) were compared to lightning group data from the Lightning Imaging Sensor (LIS) from 1 January 2010 through 30 June 2011. The region of study, from 39°S to 39°N latitude, chosen based on the orbit of LIS, and 164°E east to 17°W longitude, chosen to approximate the possible Geostationary Lightning Mapper (GLM) longitude, was considered in its entirety and then divided into geographical subregions. Over this 18-month time period, WWLLN had an 11.0% entire region, 13.2% North American, 6.2% South American, 16.4% Atlantic Ocean, and 18.9% Pacific Ocean coincidence percent (CP) value. The ENTLN CP values were 28.5%, 63.3%, 2.2%, 3.0%, and 2.5%, respectively. During the 18 months, WWLLN CP values remained rather consistent but low and often higher over ocean than land; ENTLN CP values showed large spatial and temporal variability. With both networks, North America had less variability during summer months than winter months and higher CP values during winter months than summer months. The highest ENTLN CP values were found in the southeastern United States, especially in a semicircle that extended from central Oklahoma, through Texas, along the northern Gulf of Mexico, across southern Florida, and along the U.S. East Coast. There was no significant change in CP values over time; the lowest monthly North American ENTLN CP value was found in June 2011 at 48.1%, the last month analyzed. These findings are consistent with most ENTLN sensors being located in the United States.
Abstract
Many studies over the past several decades have attempted to correlate trends in lightning (e.g., rates, polarity) to severe weather occurrence. These studies mainly used cloud-to-ground (CG) lightning information due to the ease of data availability, high detection efficiency, and broad coverage across the United States, with somewhat inconclusive results. Conversely, it has been demonstrated that trends in total lightning are more robustly correlated to severe weather occurrence, with rapid increases in total lightning observed 10s of minutes prior to the onset of severe weather. Unfortunately, total lightning observations are not as numerous, or available over the same areal coverage domain, as provided by CG networks. Relatively few studies have examined concurrent trends in both total and CG lightning within the same severe thunderstorm, or even large sets of thunderstorms using an objective lightning jump algorithm. Multiple studies have shown that the total flash rate rapidly increases prior to the onset of severe weather. What is untested within the same framework is the use of CG information to perform the same task. Herein, total and CG lightning trends for 711 thunderstorms occurring in four regions of the country were examined to demonstrate the increased utility that total lightning provides over CG lightning, specifically within the framework of developing a useful lightning-based severe weather warning decision support tool. Results indicate that while both lightning datasets demonstrate the presence of increased lightning activity prior to the onset of severe weather, the use of total lightning trends was more effective than CG trends [probability of detection (POD), 79% versus 66%; false alarm rate (FAR), 36% versus 53%; critical success index (CSI), 55% versus 38%; Heidke skill score (HSS), 0.71 versus 0.55]. Moreover, 40% of false alarms associated with total lightning, and 16% of false alarms with CG lightning trends, occurred when a lightning jump associated with a severe weather “warning” was already in effect. If these false alarms are removed, the FAR drops from 36% to 22% for total lightning and from 53% to 44% for CG lightning. Importantly, average lead times prior to severe weather occurrence were higher using total lightning as compared with CG lightning (20.65 versus 13.54 min). The ultimate goal of this study was to demonstrate the increased utility of total lightning information that the Geostationary Lightning Mapper (GLM) will provide to operational meteorology in anticipation of severe convective weather on a hemispheric scale once Geostationary Operational Environmental Satellite-R (GOES-R) is deployed in the next decade.
Abstract
Many studies over the past several decades have attempted to correlate trends in lightning (e.g., rates, polarity) to severe weather occurrence. These studies mainly used cloud-to-ground (CG) lightning information due to the ease of data availability, high detection efficiency, and broad coverage across the United States, with somewhat inconclusive results. Conversely, it has been demonstrated that trends in total lightning are more robustly correlated to severe weather occurrence, with rapid increases in total lightning observed 10s of minutes prior to the onset of severe weather. Unfortunately, total lightning observations are not as numerous, or available over the same areal coverage domain, as provided by CG networks. Relatively few studies have examined concurrent trends in both total and CG lightning within the same severe thunderstorm, or even large sets of thunderstorms using an objective lightning jump algorithm. Multiple studies have shown that the total flash rate rapidly increases prior to the onset of severe weather. What is untested within the same framework is the use of CG information to perform the same task. Herein, total and CG lightning trends for 711 thunderstorms occurring in four regions of the country were examined to demonstrate the increased utility that total lightning provides over CG lightning, specifically within the framework of developing a useful lightning-based severe weather warning decision support tool. Results indicate that while both lightning datasets demonstrate the presence of increased lightning activity prior to the onset of severe weather, the use of total lightning trends was more effective than CG trends [probability of detection (POD), 79% versus 66%; false alarm rate (FAR), 36% versus 53%; critical success index (CSI), 55% versus 38%; Heidke skill score (HSS), 0.71 versus 0.55]. Moreover, 40% of false alarms associated with total lightning, and 16% of false alarms with CG lightning trends, occurred when a lightning jump associated with a severe weather “warning” was already in effect. If these false alarms are removed, the FAR drops from 36% to 22% for total lightning and from 53% to 44% for CG lightning. Importantly, average lead times prior to severe weather occurrence were higher using total lightning as compared with CG lightning (20.65 versus 13.54 min). The ultimate goal of this study was to demonstrate the increased utility of total lightning information that the Geostationary Lightning Mapper (GLM) will provide to operational meteorology in anticipation of severe convective weather on a hemispheric scale once Geostationary Operational Environmental Satellite-R (GOES-R) is deployed in the next decade.
Abstract
A propagation correction algorithm utilizing the differential propagation phase (ϕ dp) was developed and tested on C-band polarimetric radar observations of tropical convection obtained during the Maritime Continent Thunderstorm Experiment. An empirical procedure was refined to estimate the mean coefficient of proportionality a (b) in the linear relationship between ϕ dp and the horizontal (differential) attenuation throughout each radar volume. The empirical estimates of these coefficients were a factor of 1.5–2 times larger than predicted by prior scattering simulations. This discrepancy was attributed to the routine presence of large drops [e.g., differential reflectivity Z dr ≥ 3 dB] within the tropical convection that were not included in prior theoretical studies.
Scattering simulations demonstrated that the coefficients a and b are nearly constant for small to moderate sized drops (e.g., 0.5 ≤ Z dr ≤ 2 dB; 1 ≤ diameter D 0 < 2.5 mm) but actually increase with the differential reflectivity for drop size distributions characterized by Z dr > 2 dB. As a result, large drops 1) bias the mean coefficients upward and 2) increase the standard error associated with the mean empirical coefficients down range of convective cores that contain large drops. To reduce this error, the authors implemented a “large drop correction” that utilizes enhanced coefficients a* and b* in large drop cores.
Validation of the propagation correction algorithm was accomplished with cumulative rain gauge data and internal consistency among the polarimetric variables. The bias and standard error of the cumulative radar rainfall estimator R(Z h ) [R(K dp,Z dr)], where Z h is horizontal reflectivity and K dp is specific differential phase, were substantially reduced after the application of the attenuation (differential attenuation) correction procedure utilizing ϕ dp. Similarly, scatterplots of uncorrected Z h (Z dr) versus K dp substantially underestimated theoretical expectations. After application of the propagation correction algorithm, the bias present in observations of both Z h (K dp) and Z dr(K dp) was removed and the standard errors relative to scattering simulation results were significantly reduced.
Abstract
A propagation correction algorithm utilizing the differential propagation phase (ϕ dp) was developed and tested on C-band polarimetric radar observations of tropical convection obtained during the Maritime Continent Thunderstorm Experiment. An empirical procedure was refined to estimate the mean coefficient of proportionality a (b) in the linear relationship between ϕ dp and the horizontal (differential) attenuation throughout each radar volume. The empirical estimates of these coefficients were a factor of 1.5–2 times larger than predicted by prior scattering simulations. This discrepancy was attributed to the routine presence of large drops [e.g., differential reflectivity Z dr ≥ 3 dB] within the tropical convection that were not included in prior theoretical studies.
Scattering simulations demonstrated that the coefficients a and b are nearly constant for small to moderate sized drops (e.g., 0.5 ≤ Z dr ≤ 2 dB; 1 ≤ diameter D 0 < 2.5 mm) but actually increase with the differential reflectivity for drop size distributions characterized by Z dr > 2 dB. As a result, large drops 1) bias the mean coefficients upward and 2) increase the standard error associated with the mean empirical coefficients down range of convective cores that contain large drops. To reduce this error, the authors implemented a “large drop correction” that utilizes enhanced coefficients a* and b* in large drop cores.
Validation of the propagation correction algorithm was accomplished with cumulative rain gauge data and internal consistency among the polarimetric variables. The bias and standard error of the cumulative radar rainfall estimator R(Z h ) [R(K dp,Z dr)], where Z h is horizontal reflectivity and K dp is specific differential phase, were substantially reduced after the application of the attenuation (differential attenuation) correction procedure utilizing ϕ dp. Similarly, scatterplots of uncorrected Z h (Z dr) versus K dp substantially underestimated theoretical expectations. After application of the propagation correction algorithm, the bias present in observations of both Z h (K dp) and Z dr(K dp) was removed and the standard errors relative to scattering simulation results were significantly reduced.
Abstract
Lightning initiation (LI) events over Florida and Oklahoma are examined and statistically compared to understand the behavior of observed radar and infrared satellite interest fields (IFs) in the 75-min time frame surrounding LI. Lightning initiation is defined as the time of the first lightning, of any kind, generated in a cumulonimbus cloud. Geostationary Operational Environmental Satellite (GOES) infrared IFs, contoured frequency by altitude diagrams (CFADs) of radar reflectivity, and model sounding data, analyzed in concert, show the mean characteristics over time for 36 and 23 LI events over Florida and Oklahoma, respectively. CFADs indicate that radar echoes formed 60 min before Florida LI, yet Oklahoma storms exhibited a ~30-min delayed development. Large ice volumes in Florida developed from the freezing of lofted liquid hydrometeors formed by long-lived (~45 min) warm rain processes, which are mostly absent in Oklahoma. However, ice volumes developed abruptly in Oklahoma storms despite missing a significant warm rain component. GOES fields were significantly different before 30 min prior to LI between the two locations. Compared to Florida storms, lower precipitable water (PW), higher convective available potential energy, and higher 3.9-μm reflectance in Oklahoma, suggest stronger and drier updrafts producing a greater abundance of small ice particles. Somewhat larger 15-min 10.7-μm cooling rates in Oklahoma confirm stronger updrafts, while clouds in the 60–30-min pre-LI period show more IF variability (e.g., in the 6.5–10.7-μm difference). Florida storms (high PW, slower growth) offer more lead time for LI predictability, compared to Oklahoma storms (low PW, explosive growth), with defined anvils being obvious at the time of LI.
Abstract
Lightning initiation (LI) events over Florida and Oklahoma are examined and statistically compared to understand the behavior of observed radar and infrared satellite interest fields (IFs) in the 75-min time frame surrounding LI. Lightning initiation is defined as the time of the first lightning, of any kind, generated in a cumulonimbus cloud. Geostationary Operational Environmental Satellite (GOES) infrared IFs, contoured frequency by altitude diagrams (CFADs) of radar reflectivity, and model sounding data, analyzed in concert, show the mean characteristics over time for 36 and 23 LI events over Florida and Oklahoma, respectively. CFADs indicate that radar echoes formed 60 min before Florida LI, yet Oklahoma storms exhibited a ~30-min delayed development. Large ice volumes in Florida developed from the freezing of lofted liquid hydrometeors formed by long-lived (~45 min) warm rain processes, which are mostly absent in Oklahoma. However, ice volumes developed abruptly in Oklahoma storms despite missing a significant warm rain component. GOES fields were significantly different before 30 min prior to LI between the two locations. Compared to Florida storms, lower precipitable water (PW), higher convective available potential energy, and higher 3.9-μm reflectance in Oklahoma, suggest stronger and drier updrafts producing a greater abundance of small ice particles. Somewhat larger 15-min 10.7-μm cooling rates in Oklahoma confirm stronger updrafts, while clouds in the 60–30-min pre-LI period show more IF variability (e.g., in the 6.5–10.7-μm difference). Florida storms (high PW, slower growth) offer more lead time for LI predictability, compared to Oklahoma storms (low PW, explosive growth), with defined anvils being obvious at the time of LI.
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
A detailed case study analysis of four thunderstorms is performed using polarimetric and multi-Doppler capabilities to provide specificity on the physical and dynamical drivers behind lightning jumps. The main differences between small increases in the total flash rate and a lightning jump are the increases in graupel mass and updraft volumes ≥10 m s−1 between the −10° and −40°C isotherms. Updraft volumes ≥10 m s−1 increased in magnitude at least 3–5 min in advance of the increase in both graupel mass and total flash rate. Updraft volumes ≥10 m s−1 are more robustly correlated to total flash rate than maximum updraft speed over a thunderstorm’s entire life cycle. However, peak updraft speeds increase prior to 8 of the 12 lightning jumps examined. Decreases in mean and median flash footprint size during increases in total lightning are observed in all four thunderstorms and are most notable during development stages within the most intense storms. However, this inverse relationship breaks down on larger storm scales as storms mature and anvils and stratiform regions developed with time. Promisingly, smaller flash sizes are still collocated with the strongest updraft speeds, while larger flash sizes are observed within weaker updraft regions. The results herein emphasize the following for lightning jump applications: both the lightning jump sigma level and the resultant magnitude of the total flash rate must be employed in conjunction to assess storm intensity using lightning data. The sigma-level magnitude of the lightning jump is the early warning that indicates that rapid intensification is occurring, while the magnitude of the total flash rate provides insight into the size and maintenance of the updraft volume and graupel mass. These cases serve as conceptual models for future applications of the lightning jump algorithm for hazardous weather monitoring.
Abstract
A detailed case study analysis of four thunderstorms is performed using polarimetric and multi-Doppler capabilities to provide specificity on the physical and dynamical drivers behind lightning jumps. The main differences between small increases in the total flash rate and a lightning jump are the increases in graupel mass and updraft volumes ≥10 m s−1 between the −10° and −40°C isotherms. Updraft volumes ≥10 m s−1 increased in magnitude at least 3–5 min in advance of the increase in both graupel mass and total flash rate. Updraft volumes ≥10 m s−1 are more robustly correlated to total flash rate than maximum updraft speed over a thunderstorm’s entire life cycle. However, peak updraft speeds increase prior to 8 of the 12 lightning jumps examined. Decreases in mean and median flash footprint size during increases in total lightning are observed in all four thunderstorms and are most notable during development stages within the most intense storms. However, this inverse relationship breaks down on larger storm scales as storms mature and anvils and stratiform regions developed with time. Promisingly, smaller flash sizes are still collocated with the strongest updraft speeds, while larger flash sizes are observed within weaker updraft regions. The results herein emphasize the following for lightning jump applications: both the lightning jump sigma level and the resultant magnitude of the total flash rate must be employed in conjunction to assess storm intensity using lightning data. The sigma-level magnitude of the lightning jump is the early warning that indicates that rapid intensification is occurring, while the magnitude of the total flash rate provides insight into the size and maintenance of the updraft volume and graupel mass. These cases serve as conceptual models for future applications of the lightning jump algorithm for hazardous weather monitoring.
