The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.

Wintertime lightning in central Europe is rare but has a large damage potential for tall structures such as wind turbines. We use a data-driven approach to explain why it even occurs when the meteorological processes causing thunderstorms in summer are absent. In summer, with strong solar input, thunderclouds have a large vertical extent, whereas in winter, thunderclouds are shallower in the vertical but tilted and elongated in the horizontal by strong winds that increase with altitude.

State-of-the-art statistical methods are applied to postprocess an ensemble of numerical forecasts for vertical profiles of air temperature. These profiles are important tools in weather forecasting as they show the stratification and the static stability of the atmosphere. Flexible regression models combined with the multi-dimensionality of the data lead to better calibration and representation of uncertainty of the vertical profiles.

Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.

This study presents airborne measurements in shallow convection over land to investigate the dynamic properties of clouds focusing on possible narrow downdraughts in the surrounding of the clouds. A characteristic narrow downdraught region (subsiding shell) is found directly outside the cloud borders for the mean vertical wind distribution. The subsiding shell results from the distribution of the highly variable updraughts and downdraughts in the near vicinity of the cloud.

Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.

Low-visibility conditions reduce the flight capacity of airports and can lead to delays and supplemental costs for airlines and airports. In this study, the forecasting skill and most important model predictors of airport-relevant low visibility are investigated for multiple flight planning horizons with different statistical models.

This article presents a method for improving probabilistic air temperature forecasts, particularly at Alpine sites. Using a nonsymmetric forecast distribution, the probabilistic forecast quality can be improved with respect to the common symmetric Gaussian distribution used. Furthermore, a long-term training approach of 3 years is presented to ensure the stability of the regression coefficients. The research was based on a PhD project on building an automated forecast system for northern Italy.

Lightning in Alpine regions is associated with events such as thunderstorms, extreme precipitation, high wind gusts, flash floods, and debris flows. We present a statistical approach to predict lightning counts based on numerical weather predictions. Lightning counts are considered on a grid with 18 km mesh size. Skilful prediction is obtained for a forecast horizon of 5 days over complex terrain.

This paper investigates frequently occurring foehn at the Dead Sea, which strongly impacts the local climatic conditions, in particular temperature and humidity, as well as evaporation from the Dead Sea, the aerosol load, and visibility. A statistical classification exposes two types of foehn and first-time, high-resolution measurements reveal trigger mechanisms and relevant characteristics, such as wind velocities, affected air layers, and resulting phenomena such as hydraulic jumps and rotors.

Snowfall forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. This work presents a novel statistical approach to provide accurate forecasts for fresh snow amounts and the probability of snowfall combining data from various sources. The results demonstrate that the new approach is able to provide reliable high-resolution hourly snowfall forecasts for the eastern European Alps up to 3 days ahead.

The study presents a newly developed statistical method to assess the risk of thunderstorms in complex terrain. Observations of lightning serve as an indicator for thunderstorms. The application of the method is illustrated for Carinthia which is located in Austria, Europe.

Three practical meteorological applications with different characteristics highlight the core computer science aspects and applicability of distributed computing to meteorology. Presenting cloud and grid computing this paper shows use case scenarios fitting a wide range of meteorological applications from operational to research studies. The paper concludes that distributed computing complements and extends existing high performance computing concepts.