Articles | Volume 21, issue 2
https://doi.org/10.5194/acp-21-1245-2021
© 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-21-1245-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
29 Jan 2021
Technical note |
| 29 Jan 2021
| 29 Jan 2021
Technical note: A high-resolution inverse modelling technique for estimating surface CO2 fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint
National Institute for Environmental Studies, Tsukuba, Japan
Tomohiro Oda
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
Makoto Saito
National Institute for Environmental Studies, Tsukuba, Japan
Rajesh Janardanan
National Institute for Environmental Studies, Tsukuba, Japan
Dmitry Belikov
National Institute for Environmental Studies, Tsukuba, Japan
now at: Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Johannes W. Kaiser
Deutscher Wetterdienst, Offenbach, Germany
Ruslan Zhuravlev
Central Aerological Observatory, Dolgoprudny, Russia
Alexander Ganshin
Central Aerological Observatory, Dolgoprudny, Russia
Vinu K. Valsala
Indian Institute of Tropical Meteorology, Pune, India
Arlyn Andrews
Earth System Research Laboratory, NOAA, Boulder, CO, USA
Lukasz Chmura
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland
Edward Dlugokencky
Earth System Research Laboratory, NOAA, Boulder, CO, USA
László Haszpra
Research Centre for Astronomy and Earth Sciences, Sopron, Hungary
Ray L. Langenfelds
Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, VIC, Australia
Toshinobu Machida
National Institute for Environmental Studies, Tsukuba, Japan
Takakiyo Nakazawa
Center for Atmospheric and Oceanic Studies, Tohoku University, Sendai, Japan
Michel Ramonet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL, Gif-sur-Yvette, France
Colm Sweeney
Earth System Research Laboratory, NOAA, Boulder, CO, USA
Douglas Worthy
Environment and Climate Change Canada, Climate Research Division,
Toronto, Ontario, Canada
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- Toward High‐Resolution Global Atmospheric Inverse Modeling Using Graphics Accelerators F. Chevallier et al. 10.1029/2022GL102135
- Comparison of СO<sub>2</sub> Content in the Atmosphere of St. Petersburg According to Numerical Modelling and Observations G. Nerobelov et al. 10.31857/S0002351523020050
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- High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India S. Sijikumar et al. 10.1016/j.atmosenv.2023.119868
- Comparison of CO2 Content in the Atmosphere of St. Petersburg According to Numerical Modeling and Observations G. Nerobelov et al. 10.1134/S0001433823020056
- Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations R. Janardanan et al. 10.1088/1748-9326/ad2436
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- Method of measuring atmospheric CO<sub>2</sub> based on Fabry-Perot interferometer S. Wang et al. 10.7498/aps.73.20231224
- Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
- The Global Methane Budget 2000–2017 M. Saunois et al. 10.5194/essd-12-1561-2020
- Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. 10.1038/s41598-022-19462-4
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23 citations as recorded by crossref.
- Bottom–Up Inventory of Residential Combustion Emissions in Poland for National Air Quality Modelling: Current Status and Perspectives L. Gawuc et al. 10.3390/atmos12111460
- Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions Z. Deng et al. 10.5194/essd-14-1639-2022
- Assumptions about prior fossil fuel inventories impact our ability to estimate posterior net CO2 fluxes that are needed for verifying national inventories T. Oda et al. 10.1088/1748-9326/ad059b
- Regional estimation of methane emissions over the peninsular India using atmospheric inverse modelling A. Raju et al. 10.1007/s10661-022-10323-1
- National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- Simulating CO2 profiles using NIES TM and comparison with HIAPER Pole-to-Pole Observations C. Song et al. 10.1007/s11707-022-0997-y
- Spatial Cross-Correlation of GOSAT CO2 Concentration with Repeated Heat Wave-Induced Photosynthetic Inhibition in Europe from 2009 to 2017 Y. Hwang et al. 10.3390/rs14184536
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. 10.1088/1748-9326/ad0f74
- The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability B. Nathan et al. 10.5194/acp-21-14089-2021
- The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019 A. Petrescu et al. 10.5194/essd-15-1197-2023
- Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Soil Dryness‐Shaped Spatial Pattern but Temperature‐dominated Total Flux J. Wang et al. 10.1029/2023GL103379
- Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions K. Tibrewal et al. 10.1038/s43247-023-01190-w
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- Quantification of Enhancement in Atmospheric CO2 Background Due to Indian Biospheric Fluxes and Fossil Fuel Emissions S. Halder et al. 10.1029/2021JD034545
- Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. 10.1016/j.rse.2022.112966
- Toward High‐Resolution Global Atmospheric Inverse Modeling Using Graphics Accelerators F. Chevallier et al. 10.1029/2022GL102135
- Comparison of СO<sub>2</sub> Content in the Atmosphere of St. Petersburg According to Numerical Modelling and Observations G. Nerobelov et al. 10.31857/S0002351523020050
- Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019 S. Wittig et al. 10.5194/acp-23-6457-2023
- High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India S. Sijikumar et al. 10.1016/j.atmosenv.2023.119868
- Comparison of CO2 Content in the Atmosphere of St. Petersburg According to Numerical Modeling and Observations G. Nerobelov et al. 10.1134/S0001433823020056
- Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations R. Janardanan et al. 10.1088/1748-9326/ad2436
- The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017 A. Petrescu et al. 10.5194/essd-13-2307-2021
- Method of measuring atmospheric CO<sub>2</sub> based on Fabry-Perot interferometer S. Wang et al. 10.7498/aps.73.20231224
4 citations as recorded by crossref.
- Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
- The Global Methane Budget 2000–2017 M. Saunois et al. 10.5194/essd-12-1561-2020
- Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. 10.1038/s41598-022-19462-4
- Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ B. Gaubert et al. 10.5194/acp-20-14617-2020
Latest update: 24 Apr 2024
Short summary
In order to improve the top-down estimation of the anthropogenic greenhouse gas emissions, a high-resolution inverse modelling technique was developed for applications to global transport modelling of carbon dioxide and other greenhouse gases. A coupled Eulerian–Lagrangian transport model and its adjoint are combined with surface fluxes at 0.1° resolution to provide high-resolution forward simulation and inverse modelling of surface fluxes accounting for signals from emission hot spots.
In order to improve the top-down estimation of the anthropogenic greenhouse gas emissions, a...
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