536 citations · 1.5k across the 20 of their papers we have counts for
23 papers
Deep learning for post-processing global probabilistic forecasts on sub-seasonal time scales
Nina Horat, Sebastian Lerch
Sub-seasonal weather forecasts are becoming increasingly important for a range of socio-economic activities. However, the predictive ability of physical weather models is very limi…
Direction Augmentation in the Evaluation of Armed Conflict Predictions
Johannes Bracher, Lotta Rüter, Fabian Krüger +2
In many forecasting settings, there is a specific interest in predicting the sign of an outcome variable correctly in addition to its magnitude. For instance, when forecasting arme…
Learning to forecast: The probabilistic time series forecasting challenge
Johannes Bracher, Nils Koster, Fabian Krüger +1
We report on a course project in which students submit weekly probabilistic forecasts of two weather variables and one financial variable. This real-time format allows students to…
Generative machine learning methods for multivariate ensemble post-processing
Jieyu Chen, Tim Janke, Florian Steinke +1
Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts.…
Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts
Mária Lakatos, Sebastian Lerch, Stephan Hemri +1
An influential step in weather forecasting was the introduction of ensemble forecasts in operational use due to their capability to account for the uncertainties in the future stat…
Convolutional autoencoders for spatially-informed ensemble post-processing
Sebastian Lerch, Kai L. Polsterer
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks…
Aggregating distribution forecasts from deep ensembles
Benedikt Schulz, Lutz Köhler, Sebastian Lerch
The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting. In particular, a variety of deep learning approaches…
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Benedikt Schulz, Sebastian Lerch
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on…
From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work
Kai Lars Polsterer, Antonio D'Isanto, Sebastian Lerch
The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides d…
Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
Benedikt Schulz, Mehrez El Ayari, Sebastian Lerch +1
In order to enable the transition towards renewable energy sources, probabilistic energy forecasting is of critical importance for incorporating volatile power sources such as sola…
Evaluating Ensemble Post-Processing for Wind Power Forecasts
Kaleb Phipps, Sebastian Lerch, Maria Andersson +3
Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather…
Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World
Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer +21
Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of…
Machine learning for total cloud cover prediction
Ágnes Baran, Sebastian Lerch, Mehrez El Ayari +1
Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres is…
Desublimation Frosting on Nanoengineered Surfaces
Christopher Walker, Sebastian Lerch, Matthias Reininger +4
Ice nucleation from vapor presents a variety of challenges across a wide range of industries and applications including refrigeration, transportation, and energy generation. Howeve…
Neural networks for post-processing ensemble weather forecasts
Stephan Rasp, Sebastian Lerch
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished w…
Evaluating probabilistic forecasts with scoringRules
Alexander Jordan, Fabian Krüger, Sebastian Lerch
Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demograph…
Predictive Inference Based on Markov Chain Monte Carlo Output
Fabian Krüger, Sebastian Lerch, Thordis L. Thorarinsdottir +1
In Bayesian inference, predictive distributions are typically in the form of samples generated via Markov chain Monte Carlo (MCMC) or related algorithms. In this paper, we conduct…
Combining predictive distributions for statistical post-processing of ensemble forecasts
Sándor Baran, Sebastian Lerch
Statistical post-processing techniques are now widely used to correct systematic biases and errors in calibration of ensemble forecasts obtained from multiple runs of numerical wea…
Forecaster's Dilemma: Extreme Events and Forecast Evaluation
Sebastian Lerch, Thordis L. Thorarinsdottir, Francesco Ravazzolo +1
In public discussions of the quality of forecasts, attention typically focuses on the predictive performance in cases of extreme events. However, the restriction of conventional fo…
Similarity-based semi-local estimation of EMOS models
Sebastian Lerch, Sandor Baran
Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physi…
Mixture EMOS model for calibrating ensemble forecasts of wind speed
Sándor Baran, Sebastian Lerch
Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather predictio…
Log-normal distribution based EMOS models for probabilistic wind speed forecasting
Sándor Baran, Sebastian Lerch
Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast unce…
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
Sebastian Lerch, Thordis L. Thorarinsdottir
In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed for…