WunderBlog Archive » Category 6™
Category 6 has moved! See the latest from Dr. Jeff Masters and Bob Henson here.What Can Lightning Tell Us about Storm Severity ?
As soon as an incipient thunderstorm spits out its first cloud-to-ground lightning bolt, it’s a potentially deadly threat. A complex of severe storms can generate many thousands of lightning flashes per hour. Along with being a hazard in its own right, lightning can serve as an useful clue to how quickly a thunderstorm is strengthening. New tools along these lines have been developed to help forecasters, and they’re being tested this spring at NOAA's Hazardous Weather Testbed at the National Weather Center in Norman, OK, where I visited earlier this month.
Figure 1. Lightning and the setting sun paint a colorful scene over Washington, D.C., on April 20, 2015. Image credit: wunderphotographer wolfpackwx.
Those of you in warm climates may have seen a weak shower suddenly deposit a burst of rain just minutes after you heard the first clap of thunder. For decades, this anecdotal evidence couldn’t be reliably examined. The advent of lightning detection systems in the 1980s meant that lightning flashes could be quantified for the first time. Most of the country is now covered by a network of sensors that detect the location and timing of cloud-to-ground lightning (CGs) by measuring the electromagnetic signals released at ground level. There are also regional lightning mapping arrays (LMAs) that detect intracloud lightning (ICs) by sensing the very high frequency radiation released at each “elbow” of a zigzagging flash. Together, these systems allow what's called total lightning (CGs + ICs) to be quantified. Some of the earliest work using these data found intriguing connections to severe weather. A 1989 study led by Don MacGorman (National Severe Storms Laboratory) found that the total lightning flash rate in one Oklahoma thunderstorm peaked about 5 minutes before a tornado developed. In another early study, Steven Goodman (NOAA/NESDIS) and colleagues discovered a spike in the total flash rate of an Alabama thunderstorm that preceded small hail and a microburst by several minutes. Goodman and Patrick Gatlin (University of Alabama in Huntsville) also developed the first algorithm designed to automatically identify lightning jumps.
More recently, Christopher Schultz (now at NASA) led a series of studies looking more broadly at whether lightning-jump algorithms could serve as an early-warning tool to help forecasters identify which storms might soon become severe. The results were highly encouraging, especially when the algorithm flagged jumps in total lightning that were two standard deviations greater than the rate observed a few minutes earlier. In a 2011 study of more than 700 thunderstorms (mostly in northern Alabama), this “two-sigma” index detected 79% of all severe thunderstorms, with a relatively low false-alarm rate of 36%. Schultz’s recent dissertation work demonstrated that lightning jumps, as opposed to more general increases in flash rate, were closely related to increases in the storm’s peak updraft speed, and in the storm volume that features updrafts classified as intense (e.g., at least 10 meters per second or 22 mph). These increases in updraft size and speed preceded lightning jumps by roughly 4 to 12 minutes.
Lightning jumps can’t tell us exactly what kind of severe weather a storm will produce, and not every lightning jump will lead to a severe outcome. Moreover, some thunderstorms reach severe levels without producing huge amounts of lightning. However, in general, lightning jumps can provide an almost-instantaneous measure of how quickly a thunderstorm updraft is strengthening. As the vertical motion in a storm intensifies, ice crystals, supercooled water droplets, and graupel (snow pellets)--together referred to as mixed-phase precipitation--grow more rapidly and bump into each other more readily, transferring charge among each other. Different speeds of descent allow for the charged particles to assemble in zones of positive and negative charge, which increases the storm’s ability to generate lightning. In a newly forming thunderstorm, the strengthening updraft typically produce the charge separation needed for a lightning jump a few minutes before it has time to generate one or more of the markers that the National Weather Service uses to classify a thunderstorm as severe: hail larger than 1” in diameter, winds reaching 58 mph, and/or a tornado. (Interestingly, lightning itself is not one of the elements that officially define a storm as severe, in part because it’s traditionally been so hard to quantify.) “The lightning jump provides forecasters vital information on the growth of the mixed-phase updraft size and speed within the thunderstorm, which is one crucial component that forecasters seek during the warning decision-making process,” says Schultz.
Figure 2. The solid dark-orange blob indicates a four- to five-sigma jump in lightning activity at 2009 GMT on Monday, May 18, in Tillman County, OK. The storm grew quickly, and several tornadoes were reported in Wichita County, TX, and Tillman County, OK, between 2030 and 2100 GMT. Image credit: GOES-R Proving Ground Blog.
Satellite-based measurements of total lightning are a promising adjunct to ground-based networks, which leads us to this year’s spring experiment in Oklahoma. The project is testing a variety of storm-intensity clues that will soon be provided by the GOES-R series of satellites, which are scheduled for launch beginning in the spring of 2016. GOES-R is set to include a Geostationary Lightning Mapper (GLM) that will use optical sensing to quantify total lightning at a spatial resolution of around 10 km, with a mere 20 seconds needed for data processing. In place of these yet-to-be-launched instruments, ground-based mapping array data from Earth Networks are being used in this spring’s experiment as proxies to replicate what GOES-R will be able to provide. Using this "pseudo-GLM," or PGLM, researchers are testing a lightning jump algorithm that notifies forecasters when a storm is showing jumps at various sigma levels, based on lightning rates across 1- and 6-minute intervals. The forecaster can then keep a closer eye on a particular storm if the lightning jump is dramatic enough, and if it's quickly backed up by other evidence, such as satellite imagery or radar data. “It’ll help forecasters in terms of situational awareness--what storms they should focus on,” says Geoffrey Stano (NASA/ENSCO), who's been helping to coordinate the spring experiment.
Even a few minutes of extra notice may be important with a fast-growing storm, especially if it has the potential to cause serious trouble. In a case study of the storm that produced a deadly tornado in Newcastle and Moore, OK, on May 20, 2013, Stano and colleagues found that one distinct lightning jump preceded the formation of severe hail by 19 minutes. A second jump occurred 26 minutes before the tornado formed, indicating that the storm’s updraft speed and size had increased again and that the potential for severe weather was still present. Lightning jumps may also be helpful in identifying the few storms that have severe potential on a day that's otherwise marginal.
Many examples of how forecasters are using the lightning-jump algorithm, and other tools being tested this spring, can be found at the GOES-R Proving Ground Blog at the Hazardous Weather Testbed.
This week's WunderPoster: Shazam!
Lightning strikes again in today's blog post, this time as the star of the latest in our WunderPoster series. Created by WU’s Skyler Rexrode, this depiction of lightning's power and majesty was inspired by a lighting flash in Wyoming photographed by Bryan Downie (@b_down13). Today's installment concludes our initial set of WunderPosters, but all 16 of the posters produced to date will continue to be available for downloading in formats suitable for posters or postcards. Our thanks go to the hundreds of community members who submitted images for the community-inspired WunderPosters that debuted this month. Hats off to the entire WU design team as well!
We'll be back on Friday with coverage of the weather we can expect this holiday weekend and what the upcoming summer may bring us.
Bob Henson
Figure 3. A severe thunderstorm over California’s Antelope Valley is laced with intracloud lightning. Image credit: wunderphotographer brandyn.
Figure 1. Lightning and the setting sun paint a colorful scene over Washington, D.C., on April 20, 2015. Image credit: wunderphotographer wolfpackwx.
Those of you in warm climates may have seen a weak shower suddenly deposit a burst of rain just minutes after you heard the first clap of thunder. For decades, this anecdotal evidence couldn’t be reliably examined. The advent of lightning detection systems in the 1980s meant that lightning flashes could be quantified for the first time. Most of the country is now covered by a network of sensors that detect the location and timing of cloud-to-ground lightning (CGs) by measuring the electromagnetic signals released at ground level. There are also regional lightning mapping arrays (LMAs) that detect intracloud lightning (ICs) by sensing the very high frequency radiation released at each “elbow” of a zigzagging flash. Together, these systems allow what's called total lightning (CGs + ICs) to be quantified. Some of the earliest work using these data found intriguing connections to severe weather. A 1989 study led by Don MacGorman (National Severe Storms Laboratory) found that the total lightning flash rate in one Oklahoma thunderstorm peaked about 5 minutes before a tornado developed. In another early study, Steven Goodman (NOAA/NESDIS) and colleagues discovered a spike in the total flash rate of an Alabama thunderstorm that preceded small hail and a microburst by several minutes. Goodman and Patrick Gatlin (University of Alabama in Huntsville) also developed the first algorithm designed to automatically identify lightning jumps.
More recently, Christopher Schultz (now at NASA) led a series of studies looking more broadly at whether lightning-jump algorithms could serve as an early-warning tool to help forecasters identify which storms might soon become severe. The results were highly encouraging, especially when the algorithm flagged jumps in total lightning that were two standard deviations greater than the rate observed a few minutes earlier. In a 2011 study of more than 700 thunderstorms (mostly in northern Alabama), this “two-sigma” index detected 79% of all severe thunderstorms, with a relatively low false-alarm rate of 36%. Schultz’s recent dissertation work demonstrated that lightning jumps, as opposed to more general increases in flash rate, were closely related to increases in the storm’s peak updraft speed, and in the storm volume that features updrafts classified as intense (e.g., at least 10 meters per second or 22 mph). These increases in updraft size and speed preceded lightning jumps by roughly 4 to 12 minutes.
Lightning jumps can’t tell us exactly what kind of severe weather a storm will produce, and not every lightning jump will lead to a severe outcome. Moreover, some thunderstorms reach severe levels without producing huge amounts of lightning. However, in general, lightning jumps can provide an almost-instantaneous measure of how quickly a thunderstorm updraft is strengthening. As the vertical motion in a storm intensifies, ice crystals, supercooled water droplets, and graupel (snow pellets)--together referred to as mixed-phase precipitation--grow more rapidly and bump into each other more readily, transferring charge among each other. Different speeds of descent allow for the charged particles to assemble in zones of positive and negative charge, which increases the storm’s ability to generate lightning. In a newly forming thunderstorm, the strengthening updraft typically produce the charge separation needed for a lightning jump a few minutes before it has time to generate one or more of the markers that the National Weather Service uses to classify a thunderstorm as severe: hail larger than 1” in diameter, winds reaching 58 mph, and/or a tornado. (Interestingly, lightning itself is not one of the elements that officially define a storm as severe, in part because it’s traditionally been so hard to quantify.) “The lightning jump provides forecasters vital information on the growth of the mixed-phase updraft size and speed within the thunderstorm, which is one crucial component that forecasters seek during the warning decision-making process,” says Schultz.
Figure 2. The solid dark-orange blob indicates a four- to five-sigma jump in lightning activity at 2009 GMT on Monday, May 18, in Tillman County, OK. The storm grew quickly, and several tornadoes were reported in Wichita County, TX, and Tillman County, OK, between 2030 and 2100 GMT. Image credit: GOES-R Proving Ground Blog.
Satellite-based measurements of total lightning are a promising adjunct to ground-based networks, which leads us to this year’s spring experiment in Oklahoma. The project is testing a variety of storm-intensity clues that will soon be provided by the GOES-R series of satellites, which are scheduled for launch beginning in the spring of 2016. GOES-R is set to include a Geostationary Lightning Mapper (GLM) that will use optical sensing to quantify total lightning at a spatial resolution of around 10 km, with a mere 20 seconds needed for data processing. In place of these yet-to-be-launched instruments, ground-based mapping array data from Earth Networks are being used in this spring’s experiment as proxies to replicate what GOES-R will be able to provide. Using this "pseudo-GLM," or PGLM, researchers are testing a lightning jump algorithm that notifies forecasters when a storm is showing jumps at various sigma levels, based on lightning rates across 1- and 6-minute intervals. The forecaster can then keep a closer eye on a particular storm if the lightning jump is dramatic enough, and if it's quickly backed up by other evidence, such as satellite imagery or radar data. “It’ll help forecasters in terms of situational awareness--what storms they should focus on,” says Geoffrey Stano (NASA/ENSCO), who's been helping to coordinate the spring experiment.
Even a few minutes of extra notice may be important with a fast-growing storm, especially if it has the potential to cause serious trouble. In a case study of the storm that produced a deadly tornado in Newcastle and Moore, OK, on May 20, 2013, Stano and colleagues found that one distinct lightning jump preceded the formation of severe hail by 19 minutes. A second jump occurred 26 minutes before the tornado formed, indicating that the storm’s updraft speed and size had increased again and that the potential for severe weather was still present. Lightning jumps may also be helpful in identifying the few storms that have severe potential on a day that's otherwise marginal.
Many examples of how forecasters are using the lightning-jump algorithm, and other tools being tested this spring, can be found at the GOES-R Proving Ground Blog at the Hazardous Weather Testbed.
This week's WunderPoster: Shazam!
Lightning strikes again in today's blog post, this time as the star of the latest in our WunderPoster series. Created by WU’s Skyler Rexrode, this depiction of lightning's power and majesty was inspired by a lighting flash in Wyoming photographed by Bryan Downie (@b_down13). Today's installment concludes our initial set of WunderPosters, but all 16 of the posters produced to date will continue to be available for downloading in formats suitable for posters or postcards. Our thanks go to the hundreds of community members who submitted images for the community-inspired WunderPosters that debuted this month. Hats off to the entire WU design team as well!
We'll be back on Friday with coverage of the weather we can expect this holiday weekend and what the upcoming summer may bring us.
Bob Henson
Figure 3. A severe thunderstorm over California’s Antelope Valley is laced with intracloud lightning. Image credit: wunderphotographer brandyn.
The views of the author are his/her own and do not necessarily represent the position of The Weather Company or its parent, IBM.