Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-2119-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2119-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
16 Jul 2015
Development and technical paper |
| 16 Jul 2015
Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions
R. Žabkar
CORRESPONDING AUTHOR
University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia
Center of Excellence SPACE-SI, Ljubljana, Slovenia
L. Honzak
Center of Excellence SPACE-SI, Ljubljana, Slovenia
now at: BO-MO d.o.o., Ljubljana, Slovenia
G. Skok
University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia
Center of Excellence SPACE-SI, Ljubljana, Slovenia
R. Forkel
Karlsruher Institut für Technologie, Institut für Meteorologie und Klimaforschung, Atmosphärische Umweltforschung, Garmisch-Partenkirchen, Germany
J. Rakovec
University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia
Center of Excellence SPACE-SI, Ljubljana, Slovenia
A. Ceglar
Center of Excellence SPACE-SI, Ljubljana, Slovenia
University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia
now at: Institute for Environment and Sustainability, Joint Research Centre, Ispra, Italy
University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia
Center of Excellence SPACE-SI, Ljubljana, Slovenia
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Cited
25 citations as recorded by crossref.
- Influences of planetary boundary layer mixing parameterization on summertime surface ozone concentration and dry deposition over North China Y. Zhao et al. https://doi.org/10.1016/j.atmosenv.2019.116950
- Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery M. Ganjirad & H. Bagheri https://doi.org/10.1016/j.ecoinf.2024.102498
- Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach Y. Rybarczyk & R. Zalakeviciute https://doi.org/10.1029/2020GL091202
- The influence of dry deposition on surface ozone simulations under different planetary boundary layer schemes over eastern China D. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120514
- Setting-up a Real-Time Air Quality Forecasting system for Serbia: a WRF-Chem feasibility study with different horizontal resolutions and emission inventories Z. Podrascanin https://doi.org/10.1007/s11356-019-05140-y
- Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al. https://doi.org/10.1016/j.atmosenv.2024.120952
- Optimized neural network for daily-scale ozone prediction based on transfer learning W. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.154279
- Selection of the data time interval for the prediction of maximum ozone concentrations J. Kocijan et al. https://doi.org/10.1007/s00477-017-1468-y
- Improving of local ozone forecasting by integrated models D. Gradišar et al. https://doi.org/10.1007/s11356-016-6989-2
- Improving air quality assessment using physics-inspired deep graph learning L. Li et al. https://doi.org/10.1038/s41612-023-00475-3
- Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave L. Palacios-Peña et al. https://doi.org/10.5194/acp-20-9679-2020
- The 1-way on-line coupled model system MECO(n) – Part 4: Chemical evaluation (based on MESSy v2.52) M. Mertens et al. https://doi.org/10.5194/gmd-9-3545-2016
- Combined Effect of High-Resolution Land Cover and Grid Resolution on Surface NO2 Concentrations C. Silveira et al. https://doi.org/10.3390/cli10020019
- Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan F. Cheng et al. https://doi.org/10.1016/j.atmosenv.2020.117909
- Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data H. Ly et al. https://doi.org/10.3390/s19224941
- High-Resolution Modeling of Air Quality in Abidjan (Côte d’Ivoire) Using a New Urban-Scale Inventory S. Gnamien et al. https://doi.org/10.3390/atmos15070758
- Ozone air quality simulations with WRF-Chem (v3.5.1) over Europe: model evaluation and chemical mechanism comparison K. Mar et al. https://doi.org/10.5194/gmd-9-3699-2016
- Uso do Modelo WRF-CHEM para a Simulação da Dispersão de Gases no Centro de Lançamento de Alcântara. P. Iriart & G. Fisch https://doi.org/10.1590/0102-7786312314b20150105
- Evaluation of air quality forecasting system FORAIR-IT over Europe and Italy at high resolution for year 2017 M. Adani et al. https://doi.org/10.1016/j.apr.2022.101456
- High-resolution multi-scale air pollution system: Evaluation of modelling performance and emission control strategies D. Lopes et al. https://doi.org/10.1016/j.jes.2023.02.046
- Integrating Unsupervised and Supervised Machine Learning for Meteorology-Based Ozone Estimation in an Industrial Zone: A Case Study Q. Wang et al. https://doi.org/10.3390/su18147229
- Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States X. Chen et al. https://doi.org/10.5194/gmd-14-3969-2021
- A Hybrid Neural Network Model for Predicting Air Quality under the Coupling of Ground Atmosphere Z. Li et al. https://doi.org/10.1007/s41748-025-00960-0
- Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab A. Sharma et al. https://doi.org/10.1016/j.envpol.2024.124707
- PM2.5 and O3 concentration estimation based on interpretable machine learning S. Wang et al. https://doi.org/10.1016/j.apr.2023.101866
25 citations as recorded by crossref.
- Influences of planetary boundary layer mixing parameterization on summertime surface ozone concentration and dry deposition over North China Y. Zhao et al. https://doi.org/10.1016/j.atmosenv.2019.116950
- Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery M. Ganjirad & H. Bagheri https://doi.org/10.1016/j.ecoinf.2024.102498
- Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach Y. Rybarczyk & R. Zalakeviciute https://doi.org/10.1029/2020GL091202
- The influence of dry deposition on surface ozone simulations under different planetary boundary layer schemes over eastern China D. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120514
- Setting-up a Real-Time Air Quality Forecasting system for Serbia: a WRF-Chem feasibility study with different horizontal resolutions and emission inventories Z. Podrascanin https://doi.org/10.1007/s11356-019-05140-y
- Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al. https://doi.org/10.1016/j.atmosenv.2024.120952
- Optimized neural network for daily-scale ozone prediction based on transfer learning W. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.154279
- Selection of the data time interval for the prediction of maximum ozone concentrations J. Kocijan et al. https://doi.org/10.1007/s00477-017-1468-y
- Improving of local ozone forecasting by integrated models D. Gradišar et al. https://doi.org/10.1007/s11356-016-6989-2
- Improving air quality assessment using physics-inspired deep graph learning L. Li et al. https://doi.org/10.1038/s41612-023-00475-3
- Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave L. Palacios-Peña et al. https://doi.org/10.5194/acp-20-9679-2020
- The 1-way on-line coupled model system MECO(n) – Part 4: Chemical evaluation (based on MESSy v2.52) M. Mertens et al. https://doi.org/10.5194/gmd-9-3545-2016
- Combined Effect of High-Resolution Land Cover and Grid Resolution on Surface NO2 Concentrations C. Silveira et al. https://doi.org/10.3390/cli10020019
- Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan F. Cheng et al. https://doi.org/10.1016/j.atmosenv.2020.117909
- Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data H. Ly et al. https://doi.org/10.3390/s19224941
- High-Resolution Modeling of Air Quality in Abidjan (Côte d’Ivoire) Using a New Urban-Scale Inventory S. Gnamien et al. https://doi.org/10.3390/atmos15070758
- Ozone air quality simulations with WRF-Chem (v3.5.1) over Europe: model evaluation and chemical mechanism comparison K. Mar et al. https://doi.org/10.5194/gmd-9-3699-2016
- Uso do Modelo WRF-CHEM para a Simulação da Dispersão de Gases no Centro de Lançamento de Alcântara. P. Iriart & G. Fisch https://doi.org/10.1590/0102-7786312314b20150105
- Evaluation of air quality forecasting system FORAIR-IT over Europe and Italy at high resolution for year 2017 M. Adani et al. https://doi.org/10.1016/j.apr.2022.101456
- High-resolution multi-scale air pollution system: Evaluation of modelling performance and emission control strategies D. Lopes et al. https://doi.org/10.1016/j.jes.2023.02.046
- Integrating Unsupervised and Supervised Machine Learning for Meteorology-Based Ozone Estimation in an Industrial Zone: A Case Study Q. Wang et al. https://doi.org/10.3390/su18147229
- Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States X. Chen et al. https://doi.org/10.5194/gmd-14-3969-2021
- A Hybrid Neural Network Model for Predicting Air Quality under the Coupling of Ground Atmosphere Z. Li et al. https://doi.org/10.1007/s41748-025-00960-0
- Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab A. Sharma et al. https://doi.org/10.1016/j.envpol.2024.124707
- PM2.5 and O3 concentration estimation based on interpretable machine learning S. Wang et al. https://doi.org/10.1016/j.apr.2023.101866
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