Supporting data and code for "Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions"

This repository contains the data and code supporting the journal article "Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions" in Geoscientific Model Development (https://doi.org/10.5194/gmd-19-1867-2026).<br>The repository contains two archives: The PREPROCESSED archive contains pre-processed data describing the zonal-average changes in the ozone distribution (in kg) in response to supersonic emission scenarios, these datasets are based on data from van 't Hoff et al. 2025 and 2024. The CODE archive contains a set of python files containing the code used for the generation of the results of the manuscript associated with this dataset. The README file contains a summary of the archive's contents and how to use the code within this repository.<br>The data supporting this work is made public as supplementary data to this article, and in order to allow other researchers to use these datasets for their own research. The dataset was generated using computational resources of the Cartesius and Snellius supercomputers of the Dutch e-infrastructure network provided by the SURF collective under grant numbers EINF-1504, EINF-3690, and EINF-5945. This research was funded by the MORE&amp;LESS consortium of the Horizon 2020 cycle (grant No.101006856).<br><strong>Dataset Purpose</strong>This dataset and code was built to evaluate the ability of data-driven discovery and model reduction methods to act as reduced-order models for data from chemistry transport models describing large-scale perturbations. Pre-processed Chemistry transport evaluations from van `t Hoff et al. 2025 and van `t Hoff et al. 2024 are used as test cases. These test cases describe how the distribution of global ozone changes in response to several supersonic emission scenarios. For descriptions of these datasets we refer to their associated repositories. The applied pre-processing calculates the change in ozone in terms of mass, and longitudinally averages this change.<br><strong>Dataset changelog</strong>v2: Updated codebase to represent figures in the revised manuscript, small fixes to environment.yml, updated DOIv1: First publication for peer review<br><strong>Terms of use</strong> This data and code is provided for public use under the CC BY license. Others may freely build upon it, given that the source is properly acknowledged.<br><strong>References:</strong>van ’t Hoff JA, Grewe V, Dedoussi IC. Sensitivities of Ozone and Radiative Forcing to Supersonic Aircraft Emissions Across Two Flight Corridors. Journal of Geophysical Research: Atmospheres. 2024;129(22):e2023JD040476. DOI:10.1029/2023JD040476van ’t Hoff JA, Hauglustaine D, Pletzer J, Skowron A, Grewe V, Matthes S, et al. Multi-model assessment of the atmospheric and radiative effects of supersonic transport aircraft. Atmospheric Chemistry and Physics. 2025 Feb 27;25(4):2515–50. DOI:10.5194/acp-25-2515-2025