Articles | Volume 16, issue 11
https://doi.org/10.5194/acp-16-6863-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-6863-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Jun 2016
Research article |
| 06 Jun 2016
Cluster analysis of European surface ozone observations for evaluation of MACC reanalysis data
Olga Lyapina
Forschungszentrum Jülich, Institute for Energy and Climate
Research: Troposphere (IEK-8), Jülich, 52425, Germany
Martin G. Schultz
CORRESPONDING AUTHOR
Forschungszentrum Jülich, Institute for Energy and Climate
Research: Troposphere (IEK-8), Jülich, 52425, Germany
Andreas Hense
Meteorological Institute, Bonn University, Bonn, 53121, Germany
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Cited
22 citations as recorded by crossref.
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- Detecting local and regional air pollution from biomass burning at a suburban site P. Krecl et al. 10.1016/j.atmosenv.2023.119591
- A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes N. Kaffashzadeh 10.5194/amt-16-3085-2023
- Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach E. Boleti et al. 10.5194/acp-20-9051-2020
- Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends P. Young et al. 10.1525/elementa.265
- A new approach of air pollution regionalization based on geographically weighted variations for multi-pollutants in China P. Qiu et al. 10.1016/j.scitotenv.2023.162431
- Spatial Regionalization on Surface Ozone in the Yangtze River Delta of China L. Tong et al. 10.1007/s13143-021-00250-5
- Using Clustering, Statistical Modeling, and Climate Change Projections to Analyze Recent and Future Region‐Specific Compound Ozone and Temperature Burden Over Europe S. Jahn & E. Hertig 10.1029/2021GH000561
- Understanding the origin of carbonaceous aerosols during periods of extensive biomass burning in northern India G. Singh et al. 10.1016/j.envpol.2020.116082
- Ozone air concentration trend attributes assist hours-ahead forecasts from univariate recorded data avoiding exogenous data inputs D. Wood 10.1016/j.uclim.2022.101382
- Cluster-based analysis of multi-model climate ensembles R. Hyde et al. 10.5194/gmd-11-2033-2018
- Spatial clustering and meteorological drivers of summer ozone in Europe L. Carro-Calvo et al. 10.1016/j.atmosenv.2017.08.050
- From lowland plains to the Altiplano: The impacts of regional transport of wildfire smoke on the air quality of Bolivian cities E. Mollinedo et al. 10.1016/j.atmosenv.2023.120137
- Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico T. Stolz et al. 10.1016/j.apr.2020.05.005
- Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019) P. Govender & V. Sivakumar 10.1016/j.apr.2019.09.009
- Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide E. Ryan & O. Wild 10.5194/gmd-14-5373-2021
- Air stagnation in Europe: Spatiotemporal variability and impact on air quality J. Garrido-Perez et al. 10.1016/j.scitotenv.2018.07.238
- A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption X. Yu et al. 10.1016/j.apenergy.2019.113497
- A short climatology of black and brown carbon and their sources at a suburban site impacted by smoke in Brazil A. Targino et al. 10.1016/j.jes.2022.12.025
- Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties D. Tarasick et al. 10.1525/elementa.376
- Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future R. Crespo-Miguel et al. 10.1016/j.scitotenv.2024.175071
- Surface ozone climatology of South Eastern Brazil and the impact of biomass burning events A. Targino et al. 10.1016/j.jenvman.2019.109645
22 citations as recorded by crossref.
- Affinity zone identification approach for joint control of PM2.5 pollution over China X. Yao et al. 10.1016/j.envpol.2020.115086
- Detecting local and regional air pollution from biomass burning at a suburban site P. Krecl et al. 10.1016/j.atmosenv.2023.119591
- A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes N. Kaffashzadeh 10.5194/amt-16-3085-2023
- Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach E. Boleti et al. 10.5194/acp-20-9051-2020
- Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends P. Young et al. 10.1525/elementa.265
- A new approach of air pollution regionalization based on geographically weighted variations for multi-pollutants in China P. Qiu et al. 10.1016/j.scitotenv.2023.162431
- Spatial Regionalization on Surface Ozone in the Yangtze River Delta of China L. Tong et al. 10.1007/s13143-021-00250-5
- Using Clustering, Statistical Modeling, and Climate Change Projections to Analyze Recent and Future Region‐Specific Compound Ozone and Temperature Burden Over Europe S. Jahn & E. Hertig 10.1029/2021GH000561
- Understanding the origin of carbonaceous aerosols during periods of extensive biomass burning in northern India G. Singh et al. 10.1016/j.envpol.2020.116082
- Ozone air concentration trend attributes assist hours-ahead forecasts from univariate recorded data avoiding exogenous data inputs D. Wood 10.1016/j.uclim.2022.101382
- Cluster-based analysis of multi-model climate ensembles R. Hyde et al. 10.5194/gmd-11-2033-2018
- Spatial clustering and meteorological drivers of summer ozone in Europe L. Carro-Calvo et al. 10.1016/j.atmosenv.2017.08.050
- From lowland plains to the Altiplano: The impacts of regional transport of wildfire smoke on the air quality of Bolivian cities E. Mollinedo et al. 10.1016/j.atmosenv.2023.120137
- Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico T. Stolz et al. 10.1016/j.apr.2020.05.005
- Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019) P. Govender & V. Sivakumar 10.1016/j.apr.2019.09.009
- Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide E. Ryan & O. Wild 10.5194/gmd-14-5373-2021
- Air stagnation in Europe: Spatiotemporal variability and impact on air quality J. Garrido-Perez et al. 10.1016/j.scitotenv.2018.07.238
- A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption X. Yu et al. 10.1016/j.apenergy.2019.113497
- A short climatology of black and brown carbon and their sources at a suburban site impacted by smoke in Brazil A. Targino et al. 10.1016/j.jes.2022.12.025
- Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties D. Tarasick et al. 10.1525/elementa.376
- Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future R. Crespo-Miguel et al. 10.1016/j.scitotenv.2024.175071
- Surface ozone climatology of South Eastern Brazil and the impact of biomass burning events A. Targino et al. 10.1016/j.jenvman.2019.109645
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Short summary
This study applies numerical clustering for the classification of about 1500 ozone data sets in Europe. We show the usefulness of cluster analysis (CA) for the quantitative evaluation of a global model: pre-selection of stations and validation of a global model in a phase-space produce clearer and more interpretable results. CA can be easily updated for new stations, different length of data, and other type of input properties, as well as other type of data (for example, meteorological).
This study applies numerical clustering for the classification of about 1500 ozone data sets in...
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