de.pangaea.dataset939121 und dataset PANGAEA
info@pangaea.de
https://www.pangaea.de/ information pointOfContact 2026-04-30T21:38:14Z https://doi.pangaea.de/10.1594/PANGAEA.939121 <gco:CharacterString>Benchmark dataset for 24-hour stratospheric ozone tendencies (SWIFT-AI-DS)</gco:CharacterString> 2021-12-08T08:11:06 publication https://doi.org/10.1594/PANGAEA.939121 Mohn, Helge
helge.mohn@awi.de
author Kreyling, Daniel
daniel.kreyling@awi.de
https://orcid.org/0000-0002-6441-7636 information author Wohltmann, Ingo https://orcid.org/0000-0003-4606-6788 information author Lehmann, Ralph author Rex, Markus
Markus.Rex@awi.de
https://orcid.org/0000-0001-7847-8221 information author PANGAEA
info@pangaea.de
https://www.pangaea.de/ information publisher tableDigital SWIFT-AI-DS is a benchmark dataset that consists of samples that have been derived from two simulation runs (each 2.5 years long) of the chemistry and transport model ATLAS (Wohltmann and Rex, 2009; Wohltmann et al., 2010). This data set of nearly 200 million samples meets the requirements of a labelled data set and is ideally suited for training and testing of a machine learning based surrogate model. Two time periods were considered in the simulation runs: first from November 1998 to March 2001 and the second from November 2004 to March 2007. The dataset covers the entire Earth geographically, but is vertically restricted to the altitudes of the lower to middle stratosphere, for which the SWIFT (Rex et al., 2014; Kreyling et. al, 2017; Wohltmann et al., 2017) approach of 24-hour ozone tendencies can be applied. Applicability was determined in terms of the chemical lifetime of stratospheric ozone, which is a function of solar irradiance and altitude. It can be described by a dynamic upper bound [Kreyling et. Al, 2017]. Within the range where the chemical lifetime is longer than 14 days, ozone is not in quasi-chemical equilibrium. Moreover, this data set focuses on the region of the lower to middle stratosphere because it is the region with the largest contribution to the total ozone column. State-of-the-art physical process models for stratospheric chemistry require enormous computational time. Our research is focused on developing much faster, yet accurate, surrogate models for computing the 24-hour tendencies of stratospheric ozone. Much faster models of stratospheric ozone provide a new application area such as for climate models. These surrogate models benefit greatly from the methodological and hardware improvements of the last decade. Each simulation run uses the full stratospheric chemistry model to solve a system of differential equations involving 47 chemical species and 171 chemical reactions at a very high (<< seconds) and variable temporal resolution. The ATLAS model is driven by ECMWF reanalysis data (either ERA-I or ERA5). The air parcel state has been sampled at a 24-hour time step (00:00 UTC model time). During postprocessing some variables are stored as 24-hour averages, as 24-hour tendencies or as the state at the beginning of the 24-hour time step. The dataset is stored in 12 monthly netCDF-files. completed Mohn, Helge
helge.mohn@awi.de
principalInvestigator Atmospheric chemistry Atmospheric physics climate science machine learning ozone stratospheric chemistry stratospheric ozone Surrogate model theme <gco:CharacterString>PANGAEA Keyword List</gco:CharacterString> 1995-01-01 creation PANGAEA
info@pangaea.de
https://www.pangaea.de/ information pointOfContact copyright copyright CC-BY-4.0: Creative Commons Attribution 4.0 International textTable und geoscientificInformation The benchmark-dataset consists of training- and test-data. Variables are being described in the document Description_variables.pdf. Training-Data: The training data consists of files that include ca. 100 million data samples. Each data sample consists of the input and output features that can be used to train a data-driven model on a regression task Input X: choice of variables (see document Description_variables.pdf) Output y: 24-hour tendency of stratospheric ozone Test-Data: Similar to the training data, but this test-data includes ca. 100 million data samples that have not been used for training. It can be used to assess model performance. Binary Object thematicClassification Binary Object (Media Type) thematicClassification Binary Object (File Size) physicalMeasurement Bytes Bytes Binary Object (MD5 Hash) thematicClassification text/tab-separated-values unknown https://doi.pangaea.de/10.1594/PANGAEA.939121?format=textfile download dataset The data set was checked for completeness, correctness, and consistency of metainformation. Validity of used methods was checked and - if applicable - precision and range of data. Related to <gco:CharacterString>The Extrapolar SWIFT model (version 1.0): fast stratospheric ozone chemistry for global climate models</gco:CharacterString> 2018-01-01 publication https://doi.org/10.5194/gmd-11-753-2018 Kreyling, Daniel
daniel.kreyling@awi.de
https://orcid.org/0000-0002-6441-7636 information author Wohltmann, Ingo https://orcid.org/0000-0003-4606-6788 information author Lehmann, Ralph author Rex, Markus
Markus.Rex@awi.de
https://orcid.org/0000-0001-7847-8221 information author documentDigital Geoscientific Model Development 11(2) 753-769 Related to <gco:CharacterString>An Algorithm for the Determination of All Significant Pathways in Chemical Reaction Systems</gco:CharacterString> 2004-01-01 publication https://doi.org/10.1023/B:JOCH.0000012284.28801.b1 Lehmann, Ralph author documentDigital Journal of Atmospheric Chemistry 47(1) 45-78 Related to <gco:CharacterString>Technical Note: SWIFT - a fast semi-empirical model for polar stratospheric ozone loss</gco:CharacterString> 2014-01-01 publication https://doi.org/10.5194/acp-14-6545-2014 Rex, Markus
Markus.Rex@awi.de
https://orcid.org/0000-0001-7847-8221 information author Kremser, Stefanie author Huck, P author Bodeker, G E https://orcid.org/0000-0003-1094-5852 information author Wohltmann, Ingo https://orcid.org/0000-0003-4606-6788 information author Santee, M L author Bernath, P author documentDigital Atmospheric Chemistry and Physics 14(13) 6545-6555 Related to <gco:CharacterString>The Lagrangian chemistry and transport model ATLAS: simulation and validation of stratospheric chemistry and ozone loss in the winter 1999/2000</gco:CharacterString> 2010-01-01 publication https://doi.org/10.5194/gmd-3-585-2010 Wohltmann, Ingo https://orcid.org/0000-0003-4606-6788 information author Lehmann, Ralph author Rex, Markus
Markus.Rex@awi.de
https://orcid.org/0000-0001-7847-8221 information author documentDigital Geoscientific Model Development 3(2) 585-601 Related to <gco:CharacterString>Update of the Polar SWIFT model for polar stratospheric ozone loss (Polar SWIFT version 2)</gco:CharacterString> 2017-01-01 publication https://doi.org/10.5194/gmd-10-2671-2017 Wohltmann, Ingo https://orcid.org/0000-0003-4606-6788 information author Lehmann, Ralph author Rex, Markus
Markus.Rex@awi.de
https://orcid.org/0000-0001-7847-8221 information author documentDigital Geoscientific Model Development 10(7) 2671-2689 Related to <gco:CharacterString>The Lagrangian chemistry and transport model ATLAS: validation of advective transport and mixing</gco:CharacterString> 2009-01-01 publication https://doi.org/10.5194/gmd-2-153-2009 Wohltmann, Ingo https://orcid.org/0000-0003-4606-6788 information author Rex, Markus
Markus.Rex@awi.de
https://orcid.org/0000-0001-7847-8221 information author documentDigital Geoscientific Model Development 2(2) 153-173 Further details <gco:CharacterString>SWIFT-AI-DS: Description of Variables</gco:CharacterString> documentHardcopy