  This paper addresses the impact of assimilating data from the Earth Networks
Total Lightning Network (ENTLN) during two cases of severe weather. Data from
the ENTLN serve as a substitute for those from the upcoming launch of the GOES
Lightning Mapper (GLM). We use the Weather Research and Forecast (WRF) model
and variational data assimilation techniques at 9 km spatial resolution. The
main goal is to examine the potential impact of lightning observations from the
future GLM.
  Previous efforts to assimilate lightning observations mainly utilized nudging
approaches. We develop three more sophisticated approaches, 3D-VAR WRFDA and
1D+nD-VAR (n=3,4) WRFDA schemes that currently are being considered for
operational implementation by the National Centers for Environmental Prediction
(NCEP) and the Naval Research Laboratory (NRL). This research uses Convective
Available Potential Energy (CAPE) as a proxy between lightning data and model
variables.
  To test the performance of the aforementioned schemes, we assess the quality
of resulting analysis and forecasts of precipitation compared to those from a
control experiment and verify them against NCEP stage IV precipitation. Results
demonstrate that assimilating lightning observations improves precipitation
statistics during the assimilation window and for 3-7 h thereafter. The
1D+4D-VAR approach performs best, significantly improving precipitation root
mean square errors by 25% and 27.5% compared to the control during the
assimilation window on the two cases. This finding confirms that the
variational nD-VAR (n=3,4) assimilation of lightning observations improves the
accuracy of moisture analyses and forecasts. Finally, we briefly discuss
limitations inherent in the current lightning assimilation schemes, their
implications, and possible ways to improve them.
