Lightning prediction using model output statistics - Thorsten Simon; Georg J. Mayr; Nikolaus Umlauf; Achim Zeileis (University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), 2018)
A method to predict lightning by postprocessing numerical weather prediction (NWP) output is developed for the region of the European Eastern Alps. Cloud-to-ground-flashes - detected by the ground-based ALDIS network - are counted on the 18x18 km2 grid of the 51-member NWP ensemble of the European Centre of Medium-Range Weather Forecasts (ECMWF). These counts serve as target quantity in count data regression models for the occurrence and the intensity of lightning events. The probability whether lightning occurs or not is modelled by a binomial distribution. For the intensity a hurdle approach is employed, for which the binomial distribution is combined with a zero-truncated negative binomial to model the counts within a grid cell. In both statistical models the parameters of the distributions are described by additive predictors, which are assembled by potentially nonlinear terms of NWP covariates. Measures of location and spread of approx. 100 direct and derived NWP covariates provide a pool of candidates for the nonlinear terms. A combination of stability selection and gradient boosting selects influential terms. Markov chain Monte Carlo (MCMC) simulation estimates the final model to provide credible inference of effects, scores and predictions. The selection of terms and MCMC simulation are applied for data of the year 2016, and out-of-sample performance is evaluated for 2017. The occurrence model outperforms a reference climatology - based on seven years of data - up to a forecast horizon of 5 days. The intensity model is calibrated and also outperforms climatology for exceedance probabilities, quantiles, and full predictive distributions.
Keywords: C11; C53; Q54; 330; lightning detection data; distributional regression; count data model; gradient boosting; MCMC