Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Jun 2022
Model description paper |
| 03 Jun 2022
| 03 Jun 2022
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Clara Betancourt
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Timo T. Stomberg
Institute of Geodesy and Geoinformation, University of Bonn, Niebuhrstraße 1a, 53113 Bonn, Germany
Ann-Kathrin Edrich
Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Schinkelstrasse 2a, 52062 Aachen, Germany
Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany
Ankit Patnala
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Martin G. Schultz
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Ribana Roscher
Institute of Geodesy and Geoinformation, University of Bonn, Niebuhrstraße 1a, 53113 Bonn, Germany
Data Science in Earth Observation, Technical University of Munich, Lise-Meitner-Str. 9, 85521 Ottobrunn, Germany
Julia Kowalski
Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany
Scarlet Stadtler
CORRESPONDING AUTHOR
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
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Cited
14 citations as recorded by crossref.
- Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer et al. 10.5194/tc-16-4637-2022
- Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements 10.1038/s41467-022-32693-3
- Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration J. Becker et al. 10.1525/elementa.2022.00025
- Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc A. Bishnoi et al. 10.5194/gmd-17-261-2024
- A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning A. Edrich et al. 10.1007/s11069-024-06563-8
- A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends L. Gouldsbrough et al. 10.5194/acp-24-3163-2024
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
- Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
- Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence M. Sunder et al. 10.3390/ai4040040
- A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. 10.3389/fmars.2023.1077623
- Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019 R. Liu et al. 10.1021/acs.est.3c05424
- Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
- Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
- Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
13 citations as recorded by crossref.
- Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer et al. 10.5194/tc-16-4637-2022
- Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements 10.1038/s41467-022-32693-3
- Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration J. Becker et al. 10.1525/elementa.2022.00025
- Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc A. Bishnoi et al. 10.5194/gmd-17-261-2024
- A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning A. Edrich et al. 10.1007/s11069-024-06563-8
- A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends L. Gouldsbrough et al. 10.5194/acp-24-3163-2024
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
- Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
- Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence M. Sunder et al. 10.3390/ai4040040
- A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. 10.3389/fmars.2023.1077623
- Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019 R. Liu et al. 10.1021/acs.est.3c05424
- Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
- Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
Latest update: 15 Apr 2024
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Ozone is a toxic greenhouse gas with high spatial variability. We present a...
