Event Title

Real-time Forecasting of Lightning Activity

Mentor 1

Paul Roebber

Location

Union Wisconsin Room

Start Date

29-4-2016 1:30 PM

End Date

29-4-2016 3:30 PM

Description

Although many thunderstorms produce lightning, it is not commonly known how much lightning or how damaging lightning from a storm will be. Innovative Weather forecasters became aware of this deficiency while trying to provide weather decision support forecasts for several energy companies. Being able to better forecast lightning and its’ magnitude would positively impact energy companies along with people’s safety. There have been some research studies to provide us with a good start to better forecasting lightning, but by using traditional meteorological data and also 20 years of high resolution national lightning detection data, we plan to create an algorithm to better forecast the amount of lightning and its magnitude in a given convective system. This will be done by using traditional analytical techniques such as multiple logistic regression or artificial neural networks to connect lightning to several meteorological factors such as storm type, CAPE, and echo tops.

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Apr 29th, 1:30 PM Apr 29th, 3:30 PM

Real-time Forecasting of Lightning Activity

Union Wisconsin Room

Although many thunderstorms produce lightning, it is not commonly known how much lightning or how damaging lightning from a storm will be. Innovative Weather forecasters became aware of this deficiency while trying to provide weather decision support forecasts for several energy companies. Being able to better forecast lightning and its’ magnitude would positively impact energy companies along with people’s safety. There have been some research studies to provide us with a good start to better forecasting lightning, but by using traditional meteorological data and also 20 years of high resolution national lightning detection data, we plan to create an algorithm to better forecast the amount of lightning and its magnitude in a given convective system. This will be done by using traditional analytical techniques such as multiple logistic regression or artificial neural networks to connect lightning to several meteorological factors such as storm type, CAPE, and echo tops.