{"667844":{"#nid":"667844","#data":{"type":"news","title":"New Approaches, Including Artificial Intelligence, Could Boost Tornado Prediction","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EResearch using data from a pair of geostationary satellites and a ground-based lightning mapping array could lead to more accurate forecasting of devastating tornadoes spinning off from severe storms. By analyzing dozens of factors, such as the electrical charge patterns within the storms and variations in lightning frequency, researchers are working to identify a \u201cgenetic profile\u201d of the thunderstorms likely to produce tornadoes.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIf they\u2019re successful in using an artificial intelligence technique known as machine learning to associate potentially dozens of factors with the formation of tornadoes, the work could dramatically improve the detection of severe storms \u2013 and reduce false alarms. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cThis is a great opportunity to apply machine learning to take advantage of the severe storm reports available for the past several years,\u201d said Levi Boggs, a research scientist at the Severe Storms Research Center (SSRC) at the Georgia Tech Research Institute (GTRI). \u201cWe can feed all of this information, potentially 30 or 40 different predictors, into the machine learning models and train them to identify patterns that we could potentially use to predict when tornadoes will form. Using AI, we can take on tasks that would be too challenging for humans alone.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EUsing data from their ground-based lightning mapping array, the researchers also are studying \u201cjumps\u201d and \u201cdives\u201d in lightning activity to see how they may help predict the formation of tornadoes.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EOvercoming the Challenges of Radar\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EForecasters now rely on weather radar to identify tornadoes and predict which storms may spin them off. But in areas such as North Georgia, topographical features such as mountains can limit the ability to see lower portions of potentially-dangerous storms, while the time required for radars to update their views can cut into warning times. Electromagnetic interference also can create confusing radar results, and during large severe weather outbreaks stretching across hundreds of miles, there can be multiple storms that must be watched for signs of tornadic activity.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAs a result, the development of tornadoes can be missed, while false alarms may lead citizens to disregard warnings \u2013 or wait too long to seek shelter. Based on research conducted so far, Boggs believes warnings based on machine learning techniques could be significantly faster and more accurate \u2013 and offer the potential to automate the tracking of the storms.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cWith radar-based methods, there can be a high false alarm rate, as much as 60 or 70 percent,\u201d he said. \u201cAt the same time, the probability of detection can be as low as 50 or 60 percent, which means a lot of tornadoes are missed. With these machine-learning techniques, we expect to improve on both detection and false alarm rates.\u201d \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003ETraining Machine Learning with Detailed Storm Reports\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESo far, researchers have trained their machine learning system on data from 62 tornadoes resulting from 40 different storms in Georgia. In the Peach State, tornadoes commonly pop up from squall lines of storms, though supercells \u2013 larger rotating behemoths more often seen in the Midwest \u2013 also bring tornadoes into the state. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESupercells can spawn more powerful tornadoes \u2013 EF3, EF4, and EF5 \u2013 which are more dangerous to humans and destructive to property. But squall line tornadoes can also be deadly, even if they create less powerful EF0, EF1, and EF2 tornadoes, and lines of storms capable of producing them may extend across multiple states.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cOne of the main benefits of this machine learning technique is that by using data from the geostationary lightning mapper on the GOES satellite, you would be able to avoid the limitations of radar,\u201d he said. \u201cUsing satellite data, you have a huge field of view without the terrain blockages, and you can detect tornadoes over a huge distance \u2013 potentially the entire continental United States.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EUsing the technique, Boggs and his colleagues are evaluating as many as 40 different parameters to see which ones may be relevant to predicting tornado formation. Among them is the pattern of electrical charge within the storms, which he compares to a genetic profile. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cA typical thunderstorm may have two or three charge regions, but the supercells could have a dozen or more separate regions,\u201d he said. \u201cIt\u2019s really complicated to see what\u2019s going on with the lightning because those complex charge structures will create different types of discharges. The flash rate can be just noisy.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDespite the potential advantages of satellite tornado prediction, Boggs believes forecasters will likely continue to use existing radar techniques, supplementing them with new technology as it develops. GTRI has submitted proposals to funding organizations to continue testing the machine learning tool, which also could be useful to countries that lack the weather radar network available to forecasters in the United States.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EAnalyzing Lightning \u2018Jumps\u2019 and \u2018Dives\u2019\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESatellite data and machine learning aren\u2019t the only approaches SSRC researchers are using to identify where tornadoes and other severe weather will pop up.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EFor several years, GTRI has operated the ground-based North Georgia Lightning Mapping Array (NGLMA) that tracks lightning bursts in North Georgia, centered on the Atlanta metropolitan area. Researchers are using radio-frequency emissions recorded by the array to study lightning flashes in an effort to correlate \u201cjumps\u201d \u2013 increases in lightning occurrence \u2013 and \u201cdives\u201d \u2013 reductions in frequency \u2013 with the development of severe storms.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe ground-based array \u2013 one of several operating in the United States \u2013 provides information not available from satellites, so the two sources are complementary, providing both optical and radio-frequency data.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe array was deployed by John Trostel, director of the SSRC, and correlates data on electromagnetic energy produced by the lightning bursts with precise timing and location information. The network of 12 ground stations tracks both lightning that interacts with the ground as well as bursts that stay in the clouds \u2013 which account for 75 percent of all lightning \u2013 providing a detailed map of electrical charge in the atmosphere.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cWhat we are looking for is a rapid increase in how many flashes there are over a brief period of time, on the order of a couple of minutes,\u201d said Jessica Losego, an SSRC research meteorologist who is using a NASA-developed algorithm to study the phenomena. \u201cIf you see a jump, you can feel somewhat confident that you\u2019re going to soon have some type of severe weather that may include damaging wind, hail, or a tornado. Analyzing this can help with all modes of severe weather, not just tornadoes.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ELosego is among the weather researchers worldwide who are also studying dives, sudden declines in lightning rates, though it\u2019s not yet clear how \u2013 and if \u2013 they may help forecasters. The dives in lightning activity may serve as yet another indicator of the strength of a storm and how it may be changing. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EHow Georgia\u2019s Severe Weather Is Different\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAfter a tornado killed a dozen people in North Georgia in 1998, the SSRC was created by the state of Georgia to develop improved means of providing early warning of tornadoes and severe storms. Beyond topographical issues, Georgia\u2019s tornadoes can differ from those of neighboring states in other ways, Losego noted. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cA lot of our storms come through later in the day, which means there\u2019s less sunlight to provide energy to the storms,\u201d she said. \u201cThe storms may start in Mississippi early in the day and may fall apart by the time they get there, but they are still dangerous. Storms that arrive late in the day or evening can make it more difficult to warn citizens who may be asleep when tornadoes are detected.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EData gathered by the NGLMA is shared with National Weather Service (NWS) forecasters in Peachtree City, providing an additional source of information for its forecasts.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cOur goal is to provide another tool that the NWS can use to provide more warning and have more confidence in that warning,\u201d Losego said. \u201cData from our lightning mapping array goes directly into their systems, and we will share what we learn about using information from jumps and dives that could improve warnings to Georgia citizens.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe NGLMA now covers North Georgia. Because the southern part of Georgia is out of the range of the NGLMA network and can have a different set of weather conditions, the researchers would like to establish a second array to track severe storms there.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EResearch Supports SSRC Goals\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe SSRC was created through funding from the Georgia Emergency Management Agency (GEMA), the Federal Emergency Management Agency (FEMA), and the state of Georgia to serve as a focal point for severe storm research in Georgia.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cThe SSRC serves the state of Georgia by actively developing alternative methods for detecting and forecasting severe local storms and exploring improvements to existing storm prediction and sensor technology,\u201d said Trostel. \u201cWe are utilizing the latest in machine learning, data analysis, and other technologies to support the goals of keeping Georgians safe from severe storms.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWriter: John Toon (john.toon@gtri.gatech.edu)\u003Cbr \/\u003E\r\nGTRI Communications\u003Cbr \/\u003E\r\nGeorgia Tech Research Institute\u003Cbr \/\u003E\r\nAtlanta, Georgia\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe\u0026nbsp;\u003Ca href=\u0022https:\/\/gtri.gatech.edu\/\u0022\u003E\u003Cstrong\u003EGeorgia Tech Research Institute (GTRI)\u003C\/strong\u003E\u003C\/a\u003E\u0026nbsp;is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech).\u202fFounded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $800 million of problem-solving research annually for government and industry.\u202fGTRI\u0027s renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EResearch using data from a pair of geostationary satellites and a ground-based lightning mapping array could lead to more accurate forecasting of devastating tornadoes spinning off from severe storms. By analyzing dozens of factors, such as the electrical charge patterns within the storms and variations in lightning frequency, GTRI researchers are working to identify a \u201cgenetic profile\u201d of the thunderstorms likely to produce tornadoes.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"GTRI researchers are working to utilize an artificial intelligence technique, known as machine learning, that could dramatically improve the detection of severe storms \u2013 and reduce false alarms. "}],"uid":"35832","created_gmt":"2023-05-23 15:03:21","changed_gmt":"2023-06-12 15:02:29","author":"Michelle Gowdy","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2023-05-23T00:00:00-04:00","iso_date":"2023-05-23T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"670855":{"id":"670855","type":"image","title":"Map of Lightning Jumps in Alabama and Georgia","body":"\u003Cp\u003E\u003Cem\u003EResearchers studied lightning jumps and dives in long-track tornadoes that occurred in Alabama and Georgia in March 2021. 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(National Oceanic and Atmospheric Administration image)\u003C\/em\u003E\u003C\/p\u003E\r\n"}},"groups":[{"id":"1276","name":"Georgia Tech Research Institute (GTRI)"},{"id":"1188","name":"Research Horizons"}],"categories":[{"id":"42901","name":"Community"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"154","name":"Environment"},{"id":"135","name":"Research"}],"keywords":[{"id":"416","name":"GTRI"},{"id":"365","name":"Research"},{"id":"187915","name":"go-researchnews"},{"id":"166902","name":"science and technology"},{"id":"3432","name":"weather"},{"id":"170862","name":"storm"},{"id":"1233","name":"tornado"},{"id":"192657","name":"tornado prediction"},{"id":"2556","name":"artificial intelligence"},{"id":"1564","name":"community"},{"id":"171151","name":"State of Georgia"},{"id":"9167","name":"machine learning"},{"id":"177742","name":"SSRC"},{"id":"169457","name":"Severe Storms Research Center"},{"id":"2621","name":"radar"},{"id":"192658","name":"supercells"},{"id":"192659","name":"North Georgia Lightning Mapping Array"},{"id":"192660","name":"lightning jumps"},{"id":"171162","name":"severe storms"},{"id":"191027","name":"thunderstorm"},{"id":"192661","name":"NGLMA"}],"core_research_areas":[{"id":"39501","name":"People and Technology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E(Interim) Director of Communications\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EMichelle Gowdy\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EMichelle.Gowdy@gtri.gatech.edu\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E404-407-8060\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"email":["michelle.gowdy@gtri.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}