Articles | Volume 16, issue 10
https://doi.org/10.5194/acp-16-6263-2016
© 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-16-6263-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 May 2016
Research article |
| 24 May 2016
Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation
Efisio Solazzo
European Commission, Joint Research Centre, Institute for Environment and
Sustainability, Air and Climate Unit, Ispra, Italy
Stefano Galmarini
CORRESPONDING AUTHOR
European Commission, Joint Research Centre, Institute for Environment and
Sustainability, Air and Climate Unit, Ispra, Italy
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30 citations as recorded by crossref.
- Multi-stage ensemble-learning-based model fusion for surface ozone simulations: A focus on CMIP6 models Z. Sun & A. Archibald 10.1016/j.ese.2021.100124
- Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data P. Karamchandani et al. 10.5194/acp-17-5643-2017
- 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
- Evaluation and uncertainty estimation of the impact of air quality modelling on crop yields and premature deaths using a multi-model ensemble E. Solazzo et al. 10.1016/j.scitotenv.2018.03.317
- Sensitivity of Simulated Aerosol Properties Over Eastern North America to WRF‐Chem Parameterizations P. Crippa et al. 10.1029/2018JD029900
- Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown E. Solazzo et al. 10.5194/acp-17-3001-2017
- Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3 U. Im et al. 10.5194/acp-18-5967-2018
- Systematic bias in evaluating chemical transport models with maximum daily 8 h average (MDA8) surface ozone for air quality applications: a case study with GEOS-Chem v9.02 K. Travis & D. Jacob 10.5194/gmd-12-3641-2019
- Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model A. Manders et al. 10.5194/gmd-10-4145-2017
- Air quality modeling for accountability research: Operational, dynamic, and diagnostic evaluation L. Henneman et al. 10.1016/j.atmosenv.2017.07.049
- Long-range transport of Siberian biomass burning emissions to North America during FIREX-AQ M. Johnson et al. 10.1016/j.atmosenv.2021.118241
- Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework E. Solazzo et al. 10.5194/acp-17-10435-2017
- Assessing the manageable portion of ground-level ozone in the contiguous United States H. Luo et al. 10.1080/10962247.2020.1805375
- Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States M. Astitha et al. 10.1016/j.atmosenv.2017.05.020
- Acid wet-deposition modeling sensitivity to WRF-CMAQ planetary boundary layer schemes and exceedance of critical loads over an industrializing coastal valley in northwestern British Columbia, Canada C. Onwukwe & P. Jackson 10.1016/j.apr.2020.09.014
- Evaluating cloud properties in an ensemble of regional online coupled models against satellite observations R. Baró et al. 10.5194/acp-18-15183-2018
- Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing L. Yuan et al. 10.1007/s11869-021-01041-7
- The effects of forest canopy shading and turbulence on boundary layer ozone P. Makar et al. 10.1038/ncomms15243
- Dynamic evaluation of modeled ozone concentrations in Germany with four chemistry transport models M. Thürkow et al. 10.1016/j.scitotenv.2023.167665
- Recommendations on statistics and benchmarks to assess photochemical model performance C. Emery et al. 10.1080/10962247.2016.1265027
- Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space–Time Bayesian Neural Network Downscaler H. Sun et al. 10.1021/acs.est.1c04797
- Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble L. Yuan et al. 10.5194/acp-22-8617-2022
- Gridded bias correction of modeled PM2.5 for exposure assessment, and estimation of background concentrations over a coastal valley region of northwestern British Columbia, Canada C. Onwukwe & P. Jackson 10.1080/10962247.2020.1844342
- Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data I. Kioutsioukis et al. 10.5194/acp-16-15629-2016
- Exploring sources of uncertainty in premature mortality estimates from fine particulate matter: the case of China P. Giani et al. 10.1088/1748-9326/ab7f0f
- Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations E. Chianese et al. 10.1016/j.ecolmodel.2018.07.019
- Top–down quantification of NO<sub><i>x</i></sub> emissions from traffic in an urban area using a high-resolution regional atmospheric chemistry model F. Kuik et al. 10.5194/acp-18-8203-2018
- An alternative way to evaluate chemistry-transport model variability L. Menut et al. 10.5194/gmd-10-1199-2017
- Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States C. Hogrefe et al. 10.5194/acp-18-3839-2018
- Multi-model study of mercury dispersion in the atmosphere: vertical and interhemispheric distribution of mercury species J. Bieser et al. 10.5194/acp-17-6925-2017
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Latest update: 26 Apr 2024
Short summary
A new technique to assess the quality of model results from a regional-scale air quality model is presented. The techniques are based on standard statistical parameters but work on spectral decomposition of model and measurement time series. This allows for the identification of scale-related processes for which the largest divergency between model and observed data is found. The technique is applied to the data collected during the second phase of the AQMEI Initiative.
A new technique to assess the quality of model results from a regional-scale air quality model...
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