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This paper addresses the impact of assimilating data from the Earth NetworksTotal Lightning Network (ENTLN) during two cases of severe weather. Data fromthe ENTLN serve as a substitute for those from the upcoming launch of the GOESLightning Mapper (GLM). We use the Weather Research and Forecast (WRF) modeland variational data assimilation techniques at 9 km spatial resolution. Themain goal is to examine the potential impact of lightning observations from thefuture GLM.  Previous efforts to assimilate lightning observations mainly utilized nudgingapproaches. We develop three more sophisticated approaches, 3D-VAR WRFDA and1D+nD-VAR (n=3,4) WRFDA schemes that currently are being considered foroperational implementation by the National Centers for Environmental Prediction(NCEP) and the Naval Research Laboratory (NRL). This research uses ConvectiveAvailable Potential Energy (CAPE) as a proxy between lightning data and modelvariables.  To test the performance of the aforementioned schemes, we assess the qualityof resulting analysis and forecasts of precipitation compared to those from acontrol experiment and verify them against NCEP stage IV precipitation. Resultsdemonstrate that assimilating lightning observations improves precipitationstatistics during the assimilation window and for 3-7 h thereafter. The1D+4D-VAR approach performs best, significantly improving precipitation rootmean square errors by 25% and 27.5% compared to the control during theassimilation window on the two cases. This finding confirms that thevariational nD-VAR (n=3,4) assimilation of lightning observations improves theaccuracy of moisture analyses and forecasts. Finally, we briefly discusslimitations inherent in the current lightning assimilation schemes, theirimplications, and possible ways to improve them.

1D+4D-VAR data assimilation of lightning with WRFDA system using nonlinear observation operators