Articles | Volume 11, issue 8
https://doi.org/10.5194/gmd-11-3515-2018
© 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/gmd-11-3515-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
30 Aug 2018
Model description paper |
| 30 Aug 2018
| 30 Aug 2018
CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations
International Institute for Earth System Science, Nanjing University,
Nanjing, China
Center for Isotope Research (CIO), Energy and Sustainability Research
Institute Groningen (ESRIG), University of Groningen, Groningen, 9747 AG,
the Netherlands
Ivar R. van der Velde
Global Monitoring Division, NOAA Earth System Research
Laboratory, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado, Boulder, Colorado, USA
Arlyn E. Andrews
Global Monitoring Division, NOAA Earth System Research
Laboratory, Boulder, Colorado, USA
Colm Sweeney
Global Monitoring Division, NOAA Earth System Research
Laboratory, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado, Boulder, Colorado, USA
John Miller
Global Monitoring Division, NOAA Earth System Research
Laboratory, Boulder, Colorado, USA
Pieter Tans
Global Monitoring Division, NOAA Earth System Research
Laboratory, Boulder, Colorado, USA
Ingrid T. van der Laan-Luijkx
Department of Meteorology and Air Quality, Wageningen University,
Wageningen, the Netherlands
Thomas Nehrkorn
Atmospheric and Environmental Research, Lexington, MA, USA
Marikate Mountain
Atmospheric and Environmental Research, Lexington, MA, USA
Weimin Ju
International Institute for Earth System Science, Nanjing University,
Nanjing, China
Wouter Peters
Center for Isotope Research (CIO), Energy and Sustainability Research
Institute Groningen (ESRIG), University of Groningen, Groningen, 9747 AG,
the Netherlands
Department of Meteorology and Air Quality, Wageningen University,
Wageningen, the Netherlands
Center for Isotope Research (CIO), Energy and Sustainability Research
Institute Groningen (ESRIG), University of Groningen, Groningen, 9747 AG,
the Netherlands
Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado, Boulder, Colorado, USA
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Cited
13 citations as recorded by crossref.
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell M. Steiner et al. 10.5194/acp-24-2759-2024
- CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. 10.1029/2022JD037154
- 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
- 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
- Atmospheric Simulations of Total Column CO2 Mole Fractions from Global to Mesoscale within the Carbon Monitoring System Flux Inversion Framework M. Butler et al. 10.3390/atmos11080787
- Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China S. Feng et al. 10.1016/j.scitotenv.2022.154522
- Optimizing a dynamic fossil fuel CO<sub>2</sub> emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO<sub>2</sub>, CO, NO<sub><i>x</i></sub>, and SO<sub>2</sub> I. Super et al. 10.5194/gmd-13-2695-2020
- Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake? W. He et al. 10.1029/2022MS003150
- Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint S. Maksyutov et al. 10.5194/acp-21-1245-2021
- China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables W. He et al. 10.1029/2021JG006644
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
- A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. 10.3389/fenvs.2024.1394281
13 citations as recorded by crossref.
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell M. Steiner et al. 10.5194/acp-24-2759-2024
- CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. 10.1029/2022JD037154
- 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
- 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
- Atmospheric Simulations of Total Column CO2 Mole Fractions from Global to Mesoscale within the Carbon Monitoring System Flux Inversion Framework M. Butler et al. 10.3390/atmos11080787
- Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China S. Feng et al. 10.1016/j.scitotenv.2022.154522
- Optimizing a dynamic fossil fuel CO<sub>2</sub> emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO<sub>2</sub>, CO, NO<sub><i>x</i></sub>, and SO<sub>2</sub> I. Super et al. 10.5194/gmd-13-2695-2020
- Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake? W. He et al. 10.1029/2022MS003150
- Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint S. Maksyutov et al. 10.5194/acp-21-1245-2021
- China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables W. He et al. 10.1029/2021JG006644
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
- A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. 10.3389/fenvs.2024.1394281
Latest update: 01 Nov 2024
Short summary
We have implemented a regional, high-resolution, and computationally attractive carbon dioxide data assimilation system. This system, named CTDAS-Lagrange, is capable of simultaneously optimizing terrestrial biosphere fluxes and the lateral boundary conditions. The CTDAS-Lagrange system can be easily extended to assimilate an additional tracer, e.g., carbonyl sulfide (COS or OCS), for regional estimates of both net and gross carbon fluxes.
We have implemented a regional, high-resolution, and computationally attractive carbon dioxide...
