Filter and export

Publications

What is a Publication?
73 Publications visible to you, out of a total of 73
Deep learning for postprocessing global probabilistic forecasts on subseasonal time scales

Abstract

Not specified

Authors: Nina Horat, Sebastian Lerch

Date Published: 1st Mar 2024

Publication Type: Journal

Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts

Abstract

Not specified

Authors: Mengmeng Song, Dazhi Yang, Sebastian Lerch, Xiang’ao Xia, Gokhan Mert Yagli, Jamie M. Bright, Yanbo Shen, Bai Liu, Xingli Liu, Martin János Mayer

Date Published: 1st Mar 2024

Publication Type: Journal

Easy uncertainty quantification (EasyUQ): Generating predictive distributions from single-valued model output

Abstract

Not specified

Authors: Eva-Maria Walz, Alexander Henzi, Johanna Ziegel, Tilmann Gneiting

Date Published: 8th Feb 2024

Publication Type: Journal

Learning to forecast: The Probabilistic Time Series Forecasting Challenge

Abstract

Not specified

Authors: Johannes Bracher, Nils Koster, Fabian Krüger, Sebastian Lerch

Date Published: 1st Feb 2024

Publication Type: Journal

Comparative evaluation of point process forecasts

Abstract

Not specified

Authors: Jonas R. Brehmer, Tilmann Gneiting, Marcus Herrmann, Warner Marzocchi, Martin Schlather, Kirstin Strokorb

Date Published: 1st Feb 2024

Publication Type: Journal

Scalable stellar evolution forecasting - Deep learning emulation versus hierarchical nearest-neighbor interpolation

Abstract (Expand)

Many astrophysical applications require efficient yet reliable forecasts of stellar evolution tracks. One example is population synthesis, which generates forward predictions of models for comparison … with observations. The majority of state-of-the-art rapid population synthesis methods are based on analytic fitting formulae to stellar evolution tracks that are computationally cheap to sample statistically over a continuous parameter range. The computational costs of running detailed stellar evolution codes, such as MESA, over wide and densely sampled parameter grids are prohibitive, while stellar-age based interpolation in-between sparsely sampled grid points leads to intolerably large systematic prediction errors. In this work, we provide two solutions for automated interpolation methods that offer satisfactory trade-off points between cost-efficiency and accuracy. We construct a timescale-adapted evolutionary coordinate and use it in a two-step interpolation scheme that traces the evolution of stars from zero age main sequence all the way to the end of core helium burning while covering a mass range from 0.65 to 300 M⊙. The feedforward neural network regression model (first solution) that we train to predict stellar surface variables can make millions of predictions, sufficiently accurate over the entire parameter space, within tens of seconds on a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm (second solution) that we hard-code to the same end achieves even higher predictive accuracy, the same algorithm remains applicable to all stellar variables evolved over time, but it is two orders of magnitude slower. Our methodological framework is demonstrated to work on the MESA Isochrones and Stellar Tracks (Choi et al. 2016) data set, but is independent of the input stellar catalog. Finally, we discuss the prospective applications of these methods and provide guidelines for generalizing them to higher dimensional parameter spaces.

Authors: K. Maltsev, F. R. N. Schneider, F. K. Röpke, A. I. Jordan, G. A. Qadir, W. E. Kerzendorf, K. Riedmiller, P. van der Smagt

Date Published: 2024

Publication Type: Journal

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

Abstract (Expand)

tive reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely … published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Authors: Elisabeth K. Brockhaus, Daniel Wolffram, Tanja Stadler, Michael Osthege, Tanmay Mitra, Jonas M. Littek, Ekaterina Krymova, Anna J. Klesen, Jana S. Huisman, Stefan Heyder, Laura M. Helleckes, Matthias an der Heiden, Sebastian Funk, Sam Abbott, Johannes Bracher

Date Published: 27th Nov 2023

Publication Type: Journal

Powered by
 (v.1.14.2)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH