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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.

A probabilistic forecasting method to predict thunderstorms in the European Eastern Alps is developed. A statistical model links lightning occurrence from the ground-based ALDIS detection network to a large set of direct and derived variables from a numerical weather prediction (NWP) system. The NWP system is the high resolution run (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The statistical model is a generalized additive model (GAM) framework, which is estimated by Markov chain Monte Carlo (MCMC) simulation. Gradient boosting with stability selection serves as a tool for selecting a stable set of potentially nonlinear terms. Three grids from 64X64 km2 to 16X16 km2 and 5 forecasts horizons from 5 to 1 day ahead are investigated to predict thunderstorms during afternoons (1200 UTC to 1800 UTC). Frequently selected covariates for the nonlinear terms are variants of convective precipitation, convective potential available energy, relative humidity and temperature in the mid layers of the troposphere, among others. All models, even for a lead time of five days, outperform a forecast based on climatology in an out-of-sample comparison. An example case illustrates that coarse spatial patterns are already successfully forecast five days ahead.