How do you predict where lightning will strike?

Since late 2016, NASA has been able to do it through a new addition to their NOAA GOES-16 satellite – a lightning tracker that can give insights to weather forecasters and emergency response teams before the storms hit.

The tool itself is called the Geostationary Lightning Mapper, or GLM for short. Positioned along the equator as it observes Earth from the orbit of the GOES-16 satellite, GLM is constantly looking at the Western Hemisphere for signs of the next potentially dangerous thunderstorm.

This is one hour of GOES-16’s Geostationary Lightning Mapper (GLM) lightning data from Feb. 14, when GLM acquired 1.8 million images of the Earth. It is displayed over GOES-16 ABI full disk Band 2 imagery. Brighter colors indicate more lightning energy was recorded; color bar units are the calculated kilowatt-hours of total optical emissions from lightning. The brightest storm system is located over the Gulf Coast of Texas, the same storm system in the accompanying video. This is preliminary, non-operational data. Credits: NOAA/NASA

While weather mapping and forecasting from orbit has been in use for many years, this is first time that the actual flashes of light are being tracked and synced with other weather mapping instruments to get detailed understandings of storm development.

Despite the fact that lightning strikes to humans are relatively uncommon, they are one of the leading weather-related causes of death and injury, and strikes to infrastructure can damage electrical components in buildings and power lines. In fact, only flooding is more deadly in the United States than lightning.

Community electrical blackouts are also likely in the event that energy companies are not prepared.

“When power disturbances are not handled quickly, there is risk of cascading failure. When a power line goes down, the electricity that once flowed down the damaged line is forced down other paths. If those other lines are already close to full capacity, the onslaught of electricity will cause them to overload as a result of congestion, creating a domino effect that is the leading cause of massive blackouts” (1).

Learn even more about GOES-16 and its exciting possibilities for weather forecasting improvements by visiting the GOES-16 website.

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