To achieve well calibrated probabilistic forecasts, ensemble forecasts often need to be statistically post-processed. One recent ensemble-calibration method is extended logistic regression which extends the popular logistic regression to yield full probability distribution forecasts. Although...

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 hours are generally made by using statistical methods to postprocess forecasts from numerical weather prediction systems. Two major problems that...

Raw ensemble forecasts display large errors in predicting precipitation amounts and its forecast uncertainty, especially in mountainous regions where local e.ects are often not captured. Therefore, statistical post-processing is typically applied to obtain automatically corrected weather...

Non-homogeneous regression is often used to statistically post-process ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input but other potentially useful information sources are ignored. Although it is straightforward to add further input variables,...

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...

Non-homogeneous regression models are widely used to statistically post-process numerical ensemble weather prediction models. Such regression models are capable of forecasting full probability distributions and correct for ensemble errors in the mean and variance. To estimate the corresponding...

Statistical post-processing of ensemble predictions is usually adjusted to a particular lead time so that several models must be fitted to forecast multiple lead times. To increase the coherence between lead times, we propose to use standardized anomalies instead of direct observations and...

Airport operations are sensitive to visibility conditions. Low-visibility events may lead to capacity reduction, delays and economic losses. Different levels of low-visibility procedures (lvp) are enacted to ensure aviation safety. A nowcast of the probabilities for each of the lvp categories...

Low-visibility conditions at airports can lead to capacity reductions and therefore to delays or cancelations of arriving and departing flights. Accurate visibility forecasts are required to keep the airport capacity as high as possible. We generate probabilistic nowcasts of low-visibility...