Professor Michael Navon has been awarded a grant from the National Oceanic and Atmospheric Administration to study how lightning data can improve limited area numerical forecasts.

The $476,000 grant aims to increase the accuracy of severe weather models in hurricane and tropical cyclone research by integrating newly available data from recently established lightning networks. Professor Navon and co-PI Prof Henry Fuelberg plan to use data from the upcoming launch of the GOES-R Lightning Mapper (GLM) that will provide continuous, full disc, high resolution detection of total lightning (IC + CG), Vaisala’s Global Lightning Dataset (GLD), National Lightning Detection Network (NLDN) data and Lightning Mapping Arrays (LMA) in their research. Each of these datasets brings different information to the proposed model.  The GLD states a detection efficiency of approximately 70% with a location accuracy of 5-10 km and is the primary lightning data that will be used.  The study will focus on the relation between lightning bursts and storm intensity changes, and will increase the robustness of hurricane modeling and improve hurricane intensity forecasts by integrating lightning data into tropical cyclone models.  Most previous efforts at lightning assimilation have employed nudging, in which the lightning data are used to alter vertical profiles of humidity or latent heating. Navon and Fuelberg, however, propose to develop a more sophisticated approach involving 4-D VAR toward which both National Centers for Environmental Prediction and Naval Research Laboratory are moving.

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