This study presents the assimilation of total lightning data to help initiate convection at cloud-resolving scales within a numerical weather prediction model. The test case is the 24 May 2011 Oklahoma tornado outbreak, which was characterized by an exceptional synoptic/mesoscale setup for the development of long-lived supercells with large destructive tornadoes. In an attempt to reproduce the observed storms at a predetermined analysis time, total lightning data were assimilated into the Weather Research and Forecasting Model (WRF) and analyzed via a suite of simple numerical experiments. Lightning data assimilation forced deep, moist precipitating convection to occur in the model at roughly the locations and intensities of the observed storms as depicted by observations from the National Severe Storms Laboratory’s three-dimensional National Mosaic and Multisensor Quantitative Precipitation Estimation (QPE)—i.e., NMQ—radar reflectivity mosaic product. The nudging function for the total lightning data locally increases the water vapor mixing ratio (and hence relative humidity) via a simple smooth continuous function using gridded pseudo-Geostationary Lightning Mapper (GLM) resolution (9 km) flash rate and simulated graupel mixing ratio as input variables. The assimilation of the total lightning data for only a few hours prior to the analysis time significantly improved the representation of the convection at analysis time and at the 1-h forecast within the convective permitting and convective resolving grids (i.e., 3 and 1 km, respectively). The results also highlighted possible forecast errors resulting from errors in the initial mesoscale thermodynamic variable fields. Although this case was primarily an analysis rather than a forecast, this simple and computationally inexpensive assimilation technique showed promising results and could be useful when applied to events characterized by moderate to intense lightning activity.
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Fierro et al. (2012) studied this question.
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