Articles | Volume 9, issue 3
https://doi.org/10.5194/gmd-9-965-2016
© 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/gmd-9-965-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
04 Mar 2016
Development and technical paper |
| 04 Mar 2016
Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system
Arthur P. Mizzi
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Atmospheric
Chemistry Observation and Modeling Laboratory, Boulder, CO,
USA
Avelino F. Arellano Jr.
University of Arizona, Department of Hydrology and Atmospheric Science,
Tucson, AZ, USA
David P. Edwards
National Center for Atmospheric Research, Atmospheric
Chemistry Observation and Modeling Laboratory, Boulder, CO,
USA
Jeffrey L. Anderson
National Center for Atmospheric Research, Institute for
Applied Mathematics, Boulder, CO, USA
Gabriele G. Pfister
National Center for Atmospheric Research, Atmospheric
Chemistry Observation and Modeling Laboratory, Boulder, CO,
USA
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Cited
25 citations as recorded by crossref.
- Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities Q. Zhu et al. 10.1021/acs.est.1c05636
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- 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
- Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model C. Liu et al. 10.1016/j.scitotenv.2021.145580
- Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution using OSSEs X. Liu et al. 10.5194/acp-17-7067-2017
- TCCON and NDACC X<sub>CO</sub> measurements: difference, discussion and application M. Zhou et al. 10.5194/amt-12-5979-2019
- The Environment and Climate Change Canada Carbon Assimilation System (EC-CAS v1.0): demonstration with simulated CO observations V. Khade et al. 10.5194/gmd-14-2525-2021
- A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ C. Huang et al. 10.3390/rs15061711
- 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
- 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
- Data assimilation experiments over Europe with the Chemical Transport Model FARM M. Adani & F. Uboldi 10.1016/j.atmosenv.2023.119806
- 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
- Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles A. Mizzi et al. 10.5194/gmd-11-3727-2018
- Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. 10.1007/s00376-017-7179-y
- 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
- Comparison between the assimilation of IASI Level 2 ozone retrievals and Level 1 radiances in a chemical transport model E. Emili et al. 10.5194/amt-12-3963-2019
- Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 A. Petersen et al. 10.5194/gmd-12-1241-2019
- Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and buffers ozone depletion C. Ma et al. 10.1126/sciadv.adn3657
- Wildfire CO2 Emissions in the Conterminous United States from 2015 to 2018 as Estimated by the WRF-Chem Assimilation System from OCO-2 XCO2 Retrievals J. Jin et al. 10.3390/atmos15020186
- The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
- The potential for geostationary remote sensing of NO<sub>2</sub> to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends D. Pasetto et al. 10.1111/2041-210X.13018
- 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
25 citations as recorded by crossref.
- Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities Q. Zhu et al. 10.1021/acs.est.1c05636
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- 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
- Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model C. Liu et al. 10.1016/j.scitotenv.2021.145580
- Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution using OSSEs X. Liu et al. 10.5194/acp-17-7067-2017
- TCCON and NDACC X<sub>CO</sub> measurements: difference, discussion and application M. Zhou et al. 10.5194/amt-12-5979-2019
- The Environment and Climate Change Canada Carbon Assimilation System (EC-CAS v1.0): demonstration with simulated CO observations V. Khade et al. 10.5194/gmd-14-2525-2021
- A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ C. Huang et al. 10.3390/rs15061711
- 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
- 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
- Data assimilation experiments over Europe with the Chemical Transport Model FARM M. Adani & F. Uboldi 10.1016/j.atmosenv.2023.119806
- 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
- Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles A. Mizzi et al. 10.5194/gmd-11-3727-2018
- Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. 10.1007/s00376-017-7179-y
- 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
- Comparison between the assimilation of IASI Level 2 ozone retrievals and Level 1 radiances in a chemical transport model E. Emili et al. 10.5194/amt-12-3963-2019
- Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 A. Petersen et al. 10.5194/gmd-12-1241-2019
- Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and buffers ozone depletion C. Ma et al. 10.1126/sciadv.adn3657
- Wildfire CO2 Emissions in the Conterminous United States from 2015 to 2018 as Estimated by the WRF-Chem Assimilation System from OCO-2 XCO2 Retrievals J. Jin et al. 10.3390/atmos15020186
- The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
- The potential for geostationary remote sensing of NO<sub>2</sub> to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends D. Pasetto et al. 10.1111/2041-210X.13018
- 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
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Latest update: 13 Dec 2024
Short summary
This paper introduces (i) WRF-Chem/DART – a state-of-the-art chemical transport/data assimilation system, and (ii) compact phase space retrievals (CPSRs). WRF-Chem/DART is NCAR's regional chemical weather forecasting prototype. Such systems require assimilation of chemical composition observations, such as trace gas retrievals. Retrievals are expensive to assimilate. CPSRs reduce those assimilation costs (~ 35 % for MOPITT CO) without loss in forecast skill by removing redundant information.
This paper introduces (i) WRF-Chem/DART – a state-of-the-art chemical transport/data...
