Articles | Volume 17, issue 11
https://doi.org/10.5194/acp-17-7067-2017
© Author(s) 2017. 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-17-7067-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
15 Jun 2017
Research article |
| 15 Jun 2017
Assimilation of satellite NO2 observations at high spatial resolution using OSSEs
Xueling Liu
Department of Earth and Planetary Science, University of California at Berkeley, Berkeley, CA, USA
Arthur P. Mizzi
Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder,
CO, USA
Jeffrey L. Anderson
Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA
Inez Y. Fung
Department of Earth and Planetary Science, University of California at Berkeley, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California at Berkeley, Berkeley, CA, USA
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
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- Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
- Assimilation of OCO-2 retrievals with WRF-Chem/DART: A case study for the Midwestern United States Q. Zhang et al. 10.1016/j.atmosenv.2020.118106
- An Observing System Simulation Experiment Analysis of How Well Geostationary Satellite Trace‐Gas Observations Constrain NOx Emissions in the US C. Hsu et al. 10.1029/2023JD039323
- Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements Q. He et al. 10.1088/1748-9326/abc7df
- Evaluation of high‐resolution regional CO2 data assimilation–forecast system in East Asia using observing system simulation experiment and effect of observation network on simulated CO2 concentrations M. Seo & H. Kim 10.1002/qj.4987
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- Downwind Ozone Changes of the 2019 Williams Flats Wildfire: Insights From WRF‐Chem/DART Assimilation of OMI NO2, HCHO, and MODIS AOD Retrievals A. Pouyaei et al. 10.1029/2022JD038019
- A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al. 10.5194/gmd-16-6161-2023
- An inversion of NOx and non-methane volatile organic compound (NMVOC) emissions using satellite observations during the KORUS-AQ campaign and implications for surface ozone over East Asia A. Souri et al. 10.5194/acp-20-9837-2020
- New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals Q. Zhang et al. 10.3390/rs13152996
- CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations Y. Huang et al. 10.3390/rs14051133
- Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0 T. Sekiya et al. 10.5194/gmd-11-959-2018
- Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health Y. Liou et al. 10.3390/rs15020530
- Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. 10.5194/acp-20-931-2020
- First Top‐Down Estimates of Anthropogenic NOx Emissions Using High‐Resolution Airborne Remote Sensing Observations A. Souri et al. 10.1002/2017JD028009
- The potential for geostationary remote sensing of NO2 to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
- New insights into the column CH2O/NO2 ratio as an indicator of near‐surface ozone sensitivity J. Schroeder et al. 10.1002/2017JD026781
21 citations as recorded by crossref.
- Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
- First observation of tropospheric nitrogen dioxide from the Environmental Trace Gases Monitoring Instrument onboard the GaoFen-5 satellite C. Zhang et al. 10.1038/s41377-020-0306-z
- Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
- Assimilation of OCO-2 retrievals with WRF-Chem/DART: A case study for the Midwestern United States Q. Zhang et al. 10.1016/j.atmosenv.2020.118106
- An Observing System Simulation Experiment Analysis of How Well Geostationary Satellite Trace‐Gas Observations Constrain NOx Emissions in the US C. Hsu et al. 10.1029/2023JD039323
- Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements Q. He et al. 10.1088/1748-9326/abc7df
- Evaluation of high‐resolution regional CO2 data assimilation–forecast system in East Asia using observing system simulation experiment and effect of observation network on simulated CO2 concentrations M. Seo & H. Kim 10.1002/qj.4987
- Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments P. Ma et al. 10.1080/00401706.2018.1524791
- The potential of drone observations to improve air quality predictions by 4D-Var H. Erraji et al. 10.5194/acp-24-13913-2024
- Downwind Ozone Changes of the 2019 Williams Flats Wildfire: Insights From WRF‐Chem/DART Assimilation of OMI NO2, HCHO, and MODIS AOD Retrievals A. Pouyaei et al. 10.1029/2022JD038019
- A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al. 10.5194/gmd-16-6161-2023
- An inversion of NOx and non-methane volatile organic compound (NMVOC) emissions using satellite observations during the KORUS-AQ campaign and implications for surface ozone over East Asia A. Souri et al. 10.5194/acp-20-9837-2020
- New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals Q. Zhang et al. 10.3390/rs13152996
- CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations Y. Huang et al. 10.3390/rs14051133
- Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0 T. Sekiya et al. 10.5194/gmd-11-959-2018
- Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health Y. Liou et al. 10.3390/rs15020530
- Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. 10.5194/acp-20-931-2020
- First Top‐Down Estimates of Anthropogenic NOx Emissions Using High‐Resolution Airborne Remote Sensing Observations A. Souri et al. 10.1002/2017JD028009
- The potential for geostationary remote sensing of NO2 to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
1 citations as recorded by crossref.
Latest update: 24 Apr 2025
Short summary
We describe a chemical ensemble data assimilation system with high spatial and temporal resolution that simultaneously adjusts meteorological and chemical variables and NOx emissions. We investigate the sensitivity of emission inversions to the accuracy and uncertainty of the wind analyses and the emission update scheme. The results provide insight into optimal uses of the observations from future geostationary satellite missions that will observe atmospheric composition.
We describe a chemical ensemble data assimilation system with high spatial and temporal...
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