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Lightning measurements from the Geostationary Lightning Mapper (GLM) thatwill be aboard the Geostationary Operational Environmental Satellite – RSeries will bring new information that can have the potential for improvingthe initialization of numerical weather prediction models by assisting in thedetection of clouds and convection through data assimilation. In this studywe focus on investigating the utility of lightning observations in mesoscaleand regional applications suitable for current operational environments, inwhich convection cannot be explicitly resolved. Therefore, we examine theimpact of lightning observations on storm environment. Preliminary steps indeveloping a lightning data assimilation capability suitable for mesoscalemodeling are presented in this paper. World Wide Lightning Location Network(WWLLN) data was utilized as a proxy for GLM measurements and was assimilatedwith the Maximum Likelihood Ensemble Filter, interfaced with theNonhydrostatic Mesoscale Model core of the Weather Research and Forecastingsystem (WRF-NMM). In order to test this methodology, regional dataassimilation experiments were conducted. Results indicate that lightning dataassimilation had a positive impact on the following: information content,influencing several dynamical variables in the model (e.g., moisture,temperature, and winds), and improving initial conditions during several dataassimilation cycles. However, the 6 h forecast after the assimilation didnot show a clear improvement in terms of root mean square (RMS) errors

Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model