Search Results for author: Sebastian Lerch

Found 8 papers, 5 papers with code

Convolutional autoencoders for spatially-informed ensemble post-processing

1 code implementation8 Apr 2022 Sebastian Lerch, Kai L. Polsterer

Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing.

Aggregating distribution forecasts from deep ensembles

1 code implementation5 Apr 2022 Benedikt Schulz, Sebastian Lerch

The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting.

Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison

2 code implementations17 Jun 2021 Benedikt Schulz, Sebastian Lerch

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations.

From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work

no code implementations5 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

Machine learning for total cloud cover prediction

no code implementations16 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.

Neural networks for post-processing ensemble weather forecasts

1 code implementation23 May 2018 Stephan Rasp, Sebastian Lerch

Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.

Evaluating probabilistic forecasts with the R package scoringRules

1 code implementation14 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

Probabilistic Forecasting and Comparative Model Assessment Based on Markov Chain Monte Carlo Output

no code implementations24 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

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