Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales

The lightning assimilation (LTA) technique in the Kain-Fritsch convective parameterization in the Weather Research and Forecasting (WRF) model has been updated and applied to continental and hemispheric simulations using lightning flash data obtained from the National Lightning Detection Network (NLDN) and the World Wide Lightning Location Network (WWLLN), respectively. The LTA technique uses lightning data to trigger the Kain-Fritsch convective parameterization via realistic temperature and moisture perturbations. The impact of different values for cumulus parameters associated with the Kain-Fritsch scheme on simulations with and without LTA were evaluated for both the continental and the hemispheric simulations. Comparisons to gauge-based rainfall products and near-surface meteorological observations indicated that the LTA improved the model's performance for most variables. The simulated precipitation with LTA, using WWLLN lightning flashes in the hemispheric applications, was significantly improved over the simulations without LTA when compared to precipitation from satellite observations in the equatorial regions. The simulations without LTA showed significant sensitivity to the cumulus parameters (i.e., user-toggled switches) for monthly precipitation that was as large as 40 % during convective seasons for monthly mean daily precipitation. With LTA, the differences in simulated precipitation due to the different cumulus parameters were minimized. The horizontal grid spacing of the modeling domain strongly influenced the LTA technique and the predicted total precipitation, especially in the coarser scales used for the hemispheric simulation. The user-definable cumulus parameters and domain resolution manifested the complexity of convective process modeling both with and without LTA. These results revealed sensitivities to domain resolution, geographic heterogeneity, and the source and quality of the lightning dataset.