377 Using Total Lightning Data to Improve Real-Time Tropical Cyclone Intensity Forecasts

Monday, 11 January 2016
Andrea B. Schumacher, CIRA/Colorado State Univ., Fort Collins, CO; and M. DeMaria

Using Total Lightning Data to Improve Real-Time Tropical Cyclone Intensity Forecasts


With the impending launch of GOES-R, a new capability for total lightning detection will become available via the onboard Geostationary Lightning Mapper (GLM). The GOES-R GLM will map total lightning activity continuously over the Americas and adjacent ocean regions. In preparation for this new dataset, this study seeks to explore the potential for using total lightning data to improve real-time TC intensity forecasts.

A recent study by DeMaria et al. (2012) demonstrated statistical relationships between azimuthal average lighting density and TC intensity change. The present study follows up on this work by exploring relationships between asymmetric lightning predictors and TC intensity change. Lightning data from both the World Wide Lightning Location Network (WWLLN) and the Earth Networks Total Lightning Network (ENTLN) were examined in looking for new asymmetric predictors. The latter network has significantly larger detection efficiencies in regions close to the U.S. mainland and hence may be a better proxy for GLM total lightning data. Results from initial predictor development and data comparisons will be presented and plans for incorporating these new predictors into statistical algorithms for TC rapid intensity change will be discussed.

Disclaimer: The views, opinions, and findings contained in this article are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.

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