The capabilities of the package exams for automatic generation of (statistical) exams in R are extended by adding support for learning management systems: As in earlier versions of the package exam generation is still based on separate Sweave files for each exercise - but rather than just...

The capabilities of the package exams for automatic generation of (statistical) exams in R are extended by adding support for learning management systems: As in earlier versions of the package exam generation is still based on separate Sweave files for each exercise - but rather than just...

It is popular belief that the weather is bad more frequently on weekends than on other days of the week and this is often perceived to be associated with an increased chance of rain. In fact, the meteorological literature does report some evidence for such human-induced weekly cycles although...

Flexible spatio-temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily...

Probabilistic forecasts provided by numerical ensemble prediction systems have systematic errors and are typically underdispersive. This is especially true over complex topography with extensive terrain induced small-scale effects which cannot be resolved by the ensemble system. To alleviate...

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...

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...

Bayesian analysis provides a convenient setting for the estimation of complex generalized additive regression models (GAMs). Since computational power has tremendously increased in the past decade it is now possible to tackle complicated inferential problems, e.g., with Markov chain Monte Carlo...

Structured additive regression (STAR) models provide a flexible framework for modeling possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models (GLM) and generalized additive models (GAM) as special cases but also allow a wider class of...