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Abstract. This study develops methods for estimating lightning climatologies on the day −1 km −2 scale for regions with complex terrain and applies them to summertime observations (2010)(2011)(2012)(2013)(2014)(2015) of the lightning location system ALDIS in the Austrian state of Carinthia in the Eastern Alps.Generalized additive models (GAMs) are used to model both the probability of occurrence and the intensity of lightning. Additive effects are set up for altitude, day of the year (season) and geographical location (longitude/latitude). The performance of the models is verified by 6-fold crossvalidation.The altitude effect of the occurrence model suggests higher probabilities of lightning for locations on higher elevations. The seasonal effect peaks in mid-July. The spatial effect models several local features, but there is a pronounced minimum in the north-west and a clear maximum in the eastern part of Carinthia. The estimated effects of the intensity model reveal similar features, though they are not equal. The main difference is that the spatial effect varies more strongly than the analogous effect of the occurrence model.A major asset of the introduced method is that the resulting climatological information varies smoothly over space, time and altitude. Thus, the climatology is capable of serving as a useful tool in quantitative applications, i.e. risk assessment and weather prediction.

Abstract. Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.

A probabilistic forecasting method to predict thunderstorms in the European eastern Alps is developed. A statistical model links lightning occurrence from the ground-based Austrian Lightning Detection and Information System (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) with a grid spacing of 16 km. 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 64 × 64 to 16 × 16 km2 and five forecast horizons from 5 days to 1 day ahead are investigated to predict thunderstorms during afternoons (1200–1800 UTC). Frequently selected covariates for the nonlinear terms are variants of convective precipitation, convective potential available energy, relative humidity, and temperature in the midlayers of the troposphere, among others. All models, even for a lead time of 5 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 5 days ahead.