This study develops and evaluates a statistical scheme for forecasting warm season lightning over Florida. Four warm seasons of analysis data from the Rapid Update Cycle (RUC) and lightning data from the National Lightning Detection Network are used in a perfect prognosis (PP) technique to develop a high-resolution, gridded forecast guidance product for warm season cloud-to-ground (CG) lightning for Florida. The most important RUC-derived parameters are used to develop equations producing 3-hourly spatial probability forecasts for one or more CG flashes, as well as the probability of exceeding various flash count percentile thresholds. Binary logistic regression is used to develop the equations for one or more flashes, while a negative binomial (NB) model is used to predict the amount of lightning, conditional on one or more flashes occurring.
The scheme is applied to output from three mesoscale models during an independent test period (the 2006 warm season). The evaluation is performed using output from the National Centers for Environmental Prediction (NCEP) 13-km Rapid Update Cycle (RUC13), the NCEP 12-km North American Mesoscale (NAM), and local high-resolution runs of the Weather Research and Forecasting (WRF) model for a domain over South Florida. Forecasts from all three mesoscale models generally show positive skill through the 2100-2359 UTC period compared to 1) a model containing only climatology and persistence (L-CLIPER) and 2) persistence alone. Skill statistics will be presented for the entire independent test period, and a forecast example will be shown using the high-resolution WRF model for 16-17 August 2006. Although the exact timing and placement of forecast lightning is not perfect, the forecasts generally exhibit good agreement with their verification, with most of the observed lightning occurring within the higher forecast probability contours.