Evaluating the potential impact of assimilating satellite lightning data utilizing hybrid variational-ensemble methods

A hybrid data assimilation algorithm seeking a nonlinear solution, which employs an iterative minimization of a cost function, is utilized to assimilate a nonlinear observation operator (lightning) into a numerical weather prediction model. The potential impact of lightning data assimilation to correct the intensity and location of deep moist convection, which may lead to the development of severe weather (e.g. thunderstorms and tornadoes) is evaluated. The Maximum Likelihood Ensemble Filter (MLEF), interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM) and with the inclusion of lightning flash rate as a forward observation operator is utilized to assimilate World Wide Lightning Location Network (WWLLN) lightning data. WWLLN data assimilation is utilized as a proxy to test the potential impact of lightning data produced by the Geostationary Lightning Mapper (GLM) that will be aboard the next generation of NOAA's geostationary satellites (i.e., GOES–R). Regional data assimilation experiments are conducted for a severe tornado outbreak across the Southeastern United States on April 25-28, 2011. Preliminary results indicate the utility of lightning data assimilation in the analysis and improvement of six-hour WRF-NMM forecasts. Results that highlight the differences with and without the assimilation lightning data will be presented.