Articles | Volume 18, issue 23
https://doi.org/10.5194/acp-18-16863-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-16863-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of physical parameterization on prediction of ethane concentrations for oil and gas emissions in WRF-Chem
Maryam Abdi-Oskouei
CORRESPONDING AUTHOR
Center for Global and Regional Environmental Research (CGRER), University of Iowa, Iowa City, Iowa, USA
Gabriele Pfister
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Frank Flocke
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Negin Sobhani
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Pablo Saide
Department of Atmospheric and Oceanic Sciences, University of California Los Angeles (UCLA), Los Angeles, California, USA
Alan Fried
Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA
Dirk Richter
Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA
Petter Weibring
Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA
James Walega
Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA
Gregory Carmichael
Center for Global and Regional Environmental Research (CGRER), University of Iowa, Iowa City, Iowa, USA
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Cited
9 citations as recorded by crossref.
- Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
- Quantifying Methane and Ozone Precursor Emissions from Oil and Gas Production Regions across the Contiguous US C. Francoeur et al. 10.1021/acs.est.0c07352
- Elucidating the impacts of COVID-19 lockdown on air quality and ozone chemical characteristics in India B. Roozitalab et al. 10.1039/D2EA00023G
- Assessing the impact of oil and gas activities on ambient hydrocarbon concentrations in North Texas: A retrospective analysis from 2000 to 2022 J. Kanayankottupoyil & K. John 10.1016/j.atmosenv.2024.120907
- A grid independence study to select computational parameters in dust storm prediction models: A sensitive analysis S. Hosseini Dehshiri & B. Firoozabadi 10.1016/j.uclim.2023.101534
- A new optimized hybrid approach combining machine learning with WRF-CHIMERE model for PM10 concentration prediction Y. Chelhaoui et al. 10.1007/s40808-024-02086-0
- Source sector and region contributions to black carbon and PM<sub>2.5</sub> in the Arctic N. Sobhani et al. 10.5194/acp-18-18123-2018
- Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China M. Zhang et al. 10.5194/gmd-14-6155-2021
- Air quality impacts from oil and natural gas development in Colorado D. Helmig et al. 10.1525/elementa.398
9 citations as recorded by crossref.
- Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
- Quantifying Methane and Ozone Precursor Emissions from Oil and Gas Production Regions across the Contiguous US C. Francoeur et al. 10.1021/acs.est.0c07352
- Elucidating the impacts of COVID-19 lockdown on air quality and ozone chemical characteristics in India B. Roozitalab et al. 10.1039/D2EA00023G
- Assessing the impact of oil and gas activities on ambient hydrocarbon concentrations in North Texas: A retrospective analysis from 2000 to 2022 J. Kanayankottupoyil & K. John 10.1016/j.atmosenv.2024.120907
- A grid independence study to select computational parameters in dust storm prediction models: A sensitive analysis S. Hosseini Dehshiri & B. Firoozabadi 10.1016/j.uclim.2023.101534
- A new optimized hybrid approach combining machine learning with WRF-CHIMERE model for PM10 concentration prediction Y. Chelhaoui et al. 10.1007/s40808-024-02086-0
- Source sector and region contributions to black carbon and PM<sub>2.5</sub> in the Arctic N. Sobhani et al. 10.5194/acp-18-18123-2018
- Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China M. Zhang et al. 10.5194/gmd-14-6155-2021
- Air quality impacts from oil and natural gas development in Colorado D. Helmig et al. 10.1525/elementa.398
Latest update: 14 Dec 2024
Short summary
This study presents a quantification of model uncertainties due to configurations and errors in the emission inventories. The analysis includes performing simulations with different configurations and comparisons with airborne and ground-based observations with a focus on capturing transport and emissions from the oil and gas sector. The presented results reflect the challenges that one may face when attempting to improve emission inventories by contrasting measured with modeled concentrations.
This study presents a quantification of model uncertainties due to configurations and errors in...
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