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
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
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.
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.
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
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.
1 code implementation • 5 Apr 2022 • Benedikt Schulz, Sebastian Lerch
The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting.
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.
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.
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 • 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.