1 code implementation • 4 Jun 2025 • Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva-Maria Walz, Alexander I. Jordan, Sebastian Lerch
Then we find PC as the mean continuous ranked probability score (CRPS) of the postprocessed probabilistic forecasts.
1 code implementation • 6 Feb 2025 • Jieyu Chen, Kevin Höhlein, Sebastian Lerch
The first approach derives a distribution-based representation of an input ensemble by applying standard dimensionality reduction techniques in a member-by-member fashion and merging the member representations into a joint parametric distribution model.
no code implementations • 8 Jul 2024 • Moritz Feik, Sebastian Lerch, Jan Stühmer
Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing.
1 code implementation • 6 Jun 2024 • Nina Horat, Sina Klerings, Sebastian Lerch
In a case study based on a benchmark dataset for the Jacumba solar plant in the U. S., we develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance and solar power generation.
1 code implementation • 20 Mar 2024 • Christopher Bülte, Nina Horat, Julian Quinting, Sebastian Lerch
In a case study on medium-range forecasts of selected weather variables over Europe, the probabilistic forecasts obtained by using the Pangu-Weather model in concert with uncertainty quantification methods show promising results and provide improvements over ensemble forecasts from the physics-based ensemble weather model of the European Centre for Medium-Range Weather Forecasts for lead times of up to 5 days.
no code implementations • 8 Sep 2023 • Kevin Höhlein, Benedikt Schulz, Rüdiger Westermann, Sebastian Lerch
Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions.
1 code implementation • 26 Sep 2022 • Jieyu Chen, Tim Janke, Florian Steinke, Sebastian Lerch
Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts.
1 code implementation • 8 Apr 2022 • Sebastian Lerch, Kai L. Polsterer
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing.
2 code implementations • 5 Apr 2022 • Benedikt Schulz, Lutz Köhler, Sebastian Lerch
Using theoretical arguments and a comprehensive analysis on twelve benchmark data sets, we systematically compare probability- and quantile-based aggregation methods for three neural network-based approaches with different forecast distribution types as output.
2 code implementations • 17 Jun 2021 • Benedikt Schulz, Sebastian Lerch
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations.
no code implementations • 5 Mar 2021 • Kai Lars Polsterer, Antonio D'Isanto, Sebastian Lerch
We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting.
Weather Forecasting
Instrumentation and Methods for Astrophysics
no code implementations • 16 Jan 2020 • Ágnes Baran, Sebastian Lerch, Mehrez El Ayari, Sándor Baran
We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor.
1 code implementation • 23 May 2018 • Stephan Rasp, Sebastian Lerch
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.
1 code implementation • 14 Sep 2017 • 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 demography.
Computation Applications
no code implementations • 24 Aug 2016 • Fabian Krüger, Sebastian Lerch, Thordis L. Thorarinsdottir, Tilmann Gneiting
Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output.
Methodology