Probabilistic forecasting of thunderstorms in the Eastern Alps - Thorsten Simon; Peter Fabsic; Georg J. Mayr; Nikolaus Umlauf; Achim Zeileis (University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), 2017)
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.
Keywords: C11; C53; Q54; 330; lightning detection data; statistical post-processing; generalized additive models; gradient boosting; stability selection; MCMC