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 preprint "Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions" currently open for discussion in Geoscientific Model Development (https://doi.org/10.5194/egusphere-2025-2661).<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>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 thesource 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

本仓库包含支撑预印本《数据驱动发现与模型降阶方法用于高空排放的大气效应》（Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions）的数据与代码，该预印本目前正在《地球科学模型开发》（Geoscientific Model Development）期刊接受公开讨论，链接：https://doi.org/10.5194/egusphere-2025-2661。
本仓库包含两个归档文件：**预处理（PREPROCESSED）归档**存储了经预处理的数据，用于描述超音速排放情景下臭氧分布（单位：千克）的纬向平均变化，此类数据集基于van 't Hoff等人2025年与2024年的研究数据构建；**代码（CODE）归档**包含一组Python文件，用于生成本数据集关联论文稿件的结果。本仓库的自述（README）文件汇总了两个归档的内容，并说明了如何使用仓库内的代码。
本研究相关数据作为本文的补充数据公开，以供其他研究人员将其用于自身科研工作。本数据集依托荷兰SURF合作组织提供的荷兰电子基础设施网络的Cartesius与Snellius超级计算机的计算资源生成，相关资助编号为EINF-1504、EINF-3690及EINF-5945。本研究由地平线2020（Horizon 2020）计划的MORE&LESS联盟资助，资助编号为No.101006856。
**数据集用途**
本数据集与代码旨在评估数据驱动发现与模型降阶方法作为降阶模型的性能，以处理描述大尺度扰动的化学传输模型（chemistry transport model, CTM）数据。本研究采用van 't Hoff等人2025年与2024年的预处理化学传输评估结果作为测试案例，此类案例描述了全球臭氧分布响应多种超音速排放情景的变化规律。有关此类数据集的详细信息，请参阅其关联仓库。本次采用的预处理步骤会以质量为单位计算臭氧变化量，并沿经度方向对该变化求平均。
**使用条款**
本数据与代码采用知识共享署名（CC BY）许可协议面向公众开放使用。使用者可基于本数据集与代码自由开展衍生研究，但需正确标注原始来源。
**参考文献：**
1. van ’t Hoff JA, Grewe V, Dedoussi IC. 两条飞行走廊内超音速飞机排放对臭氧与辐射强迫的敏感性. 《地球物理研究杂志：大气卷》. 2024;129(22):e2023JD040476. DOI:10.1029/2023JD040476
2. van ’t Hoff JA, Hauglustaine D, Pletzer J, Skowron A, Grewe V, Matthes S, et al. 超音速运输飞机大气与辐射效应的多模型评估. 《大气化学与物理》. 2025 Feb 27;25(4):2515–50. DOI:10.5194/acp-25-2515-2025