Articles | Volume 21, issue 12
https://doi.org/10.5194/acp-21-9545-2021
© Author(s) 2021. 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-21-9545-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jun 2021
Research article |
| 24 Jun 2021
| 24 Jun 2021
Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH4 data with weak-constraint four-dimensional variational data assimilation
Ilya Stanevich
CORRESPONDING AUTHOR
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Dylan B. A. Jones
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Kimberly Strong
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Martin Keller
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Daven K. Henze
Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
California Institute of Technology, Pasadena, CA, USA
Robert J. Parker
Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
National Centre for Earth Observation (NCEO), University of Leicester, Leicester, UK
Hartmut Boesch
Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
National Centre for Earth Observation (NCEO), University of Leicester, Leicester, UK
Debra Wunch
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Justus Notholt
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Christof Petri
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Thorsten Warneke
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Ralf Sussmann
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
Matthias Schneider
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Frank Hase
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Rigel Kivi
Finnish Meteorological Institute, Sodankylä, Finland
Nicholas M. Deutscher
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, NSW, Australia
Voltaire A. Velazco
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, NSW, Australia
Kaley A. Walker
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Feng Deng
Department of Physics, University of Toronto, Toronto, Ontario, Canada
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Cited
14 citations as recorded by crossref.
- Spatial-temporal variation in XCH4 during 2009–2021 and its driving factors across the land of the Northern Hemisphere X. Cao et al. 10.1016/j.atmosres.2023.106811
- CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. 10.5194/gmd-16-4793-2023
- Uncertainty in parameterized convection remains a key obstacle for estimating surface fluxes of carbon dioxide A. Schuh & A. Jacobson 10.5194/acp-23-6285-2023
- Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
- Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge Z. Qu et al. 10.1073/pnas.2402730121
- A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
- Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. 10.1007/s00376-022-1434-6
- Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. 10.3390/atmos14040758
- Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. 10.5194/acp-21-14159-2021
- How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observing system simulation experiments with the GEOS-Chem adjoint model (v35) X. Yu et al. 10.5194/gmd-14-7775-2021
- Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
- A decade of GOSAT Proxy satellite CH<sub>4</sub> observations R. Parker et al. 10.5194/essd-12-3383-2020
- Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model I. Stanevich et al. 10.5194/gmd-13-3839-2020
- The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery B. Byrne et al. 10.1029/2021AV000469
11 citations as recorded by crossref.
- Spatial-temporal variation in XCH4 during 2009–2021 and its driving factors across the land of the Northern Hemisphere X. Cao et al. 10.1016/j.atmosres.2023.106811
- CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. 10.5194/gmd-16-4793-2023
- Uncertainty in parameterized convection remains a key obstacle for estimating surface fluxes of carbon dioxide A. Schuh & A. Jacobson 10.5194/acp-23-6285-2023
- Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
- Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge Z. Qu et al. 10.1073/pnas.2402730121
- A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
- Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. 10.1007/s00376-022-1434-6
- Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. 10.3390/atmos14040758
- Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. 10.5194/acp-21-14159-2021
- How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observing system simulation experiments with the GEOS-Chem adjoint model (v35) X. Yu et al. 10.5194/gmd-14-7775-2021
- Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
3 citations as recorded by crossref.
- A decade of GOSAT Proxy satellite CH<sub>4</sub> observations R. Parker et al. 10.5194/essd-12-3383-2020
- Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model I. Stanevich et al. 10.5194/gmd-13-3839-2020
- The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery B. Byrne et al. 10.1029/2021AV000469
Latest update: 19 Nov 2024
Short summary
We explore the utility of a weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation scheme for mitigating systematic errors in methane simulation in the GEOS-Chem model. We use data from the Greenhouse Gases Observing Satellite (GOSAT) and show that, compared to the traditional 4D-Var approach, the WC scheme improves the agreement between the model and independent observations. We find that the WC corrections to the model provide insight into the source of the errors.
We explore the utility of a weak-constraint (WC) four-dimensional variational (4D-Var) data...
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