Articles | Volume 23, issue 6
https://doi.org/10.5194/acp-23-3829-2023
© Author(s) 2023. 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-23-3829-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
31 Mar 2023
Technical note |
| 31 Mar 2023
| 31 Mar 2023
Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020
Anna Agustí-Panareda
CORRESPONDING AUTHOR
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Jérôme Barré
Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, CO, USA
Sébastien Massart
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Antje Inness
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Ilse Aben
SRON Netherlands Institute for Space Research, Utrecht, the
Netherlands
Melanie Ades
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Bianca C. Baier
Cooperative Institute for Research in Environmental Sciences,
University of Colorado Boulder, Boulder, CO, USA
NOAA, Global Monitoring Laboratory, Boulder, CO, USA
Gianpaolo Balsamo
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Tobias Borsdorff
SRON Netherlands Institute for Space Research, Utrecht, the
Netherlands
Nicolas Bousserez
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Souhail Boussetta
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Michael Buchwitz
Institute of Environmental Physics (IUP), University of Bremen, 28334 Bremen, Germany
Luca Cantarello
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Cyril Crevoisier
Laboratoire de Météorologie Dynamique (LMD/IPSL), CNRS, Ecole polytechnique, 91128 Palaiseau CEDEX, France
Richard Engelen
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Henk Eskes
Royal Netherlands Meteorological Institute, Utrechtseweg 297,
3731 GA De Bilt, the Netherlands
Johannes Flemming
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Sébastien Garrigues
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Otto Hasekamp
SRON Netherlands Institute for Space Research, Utrecht, the
Netherlands
Vincent Huijnen
Royal Netherlands Meteorological Institute, Utrechtseweg 297,
3731 GA De Bilt, the Netherlands
Luke Jones
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Zak Kipling
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Bavo Langerock
Royal Belgian Institute for Space Aeronomy, Avenue Circulaire 3,
1180 Uccle, Belgium
Joe McNorton
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Nicolas Meilhac
Laboratoire de Météorologie Dynamique (LMD/IPSL), CNRS, Ecole polytechnique, 91128 Palaiseau CEDEX, France
Stefan Noël
Institute of Environmental Physics (IUP), University of Bremen, 28334 Bremen, Germany
Mark Parrington
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Vincent-Henri Peuch
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Michel Ramonet
Laboratoire des Sciences du Climat et de l'Environnement
(LSCE-IPSL), CEA-CNRS-UVSQ, Université Paris-Saclay, 91191
Gif-sur-Yvette, France
Miha Razinger
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Maximilian Reuter
Institute of Environmental Physics (IUP), University of Bremen, 28334 Bremen, Germany
Roberto Ribas
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Martin Suttie
European Centre for Medium Range Weather Forecasts, Shinfield Park,
Reading RG2 9AX, United Kingdom
Colm Sweeney
NOAA, Global Monitoring Laboratory, Boulder, CO, USA
Jérôme Tarniewicz
Laboratoire des Sciences du Climat et de l'Environnement
(LSCE-IPSL), CEA-CNRS-UVSQ, Université Paris-Saclay, 91191
Gif-sur-Yvette, France
Lianghai Wu
Remote Sensing Unit, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium
Viewed
Total article views: 5,293 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Jul 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,090 | 1,116 | 87 | 5,293 | 95 | 53 | 68 |
- HTML: 4,090
- PDF: 1,116
- XML: 87
- Total: 5,293
- Supplement: 95
- BibTeX: 53
- EndNote: 68
Total article views: 4,264 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 31 Mar 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,437 | 763 | 64 | 4,264 | 95 | 38 | 51 |
- HTML: 3,437
- PDF: 763
- XML: 64
- Total: 4,264
- Supplement: 95
- BibTeX: 38
- EndNote: 51
Total article views: 1,029 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Jul 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 653 | 353 | 23 | 1,029 | 15 | 17 |
- HTML: 653
- PDF: 353
- XML: 23
- Total: 1,029
- BibTeX: 15
- EndNote: 17
Viewed (geographical distribution)
Total article views: 5,293 (including HTML, PDF, and XML)
Thereof 5,306 with geography defined
and -13 with unknown origin.
Total article views: 4,264 (including HTML, PDF, and XML)
Thereof 4,272 with geography defined
and -8 with unknown origin.
Total article views: 1,029 (including HTML, PDF, and XML)
Thereof 1,034 with geography defined
and -5 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO2 Concentrations in the Yangtze River Delta, China Y. Wang et al. 10.1029/2023JD038512
- Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features H. He et al. 10.3389/feart.2024.1352498
- Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China J. Wang et al. 10.3390/rs16050900
- COCCON Measurements of XCO2, XCH4 and XCO over Coal Mine Aggregation Areas in Shanxi, China, and Comparison to TROPOMI and CAMS Datasets Q. Tu et al. 10.3390/rs16214022
- Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion W. Wu et al. 10.3390/rs16030578
- Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning T. Te et al. 10.1016/j.jag.2024.104117
- Small Fires, Big Impact: Evaluating Fire Emission Estimates in Southern Africa Using New Satellite Imagery of Burned Area and Carbon Monoxide I. van der Velde et al. 10.1029/2023GL106122
- Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018–2021 J. Zhong et al. 10.1016/j.scib.2023.08.039
- Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites Q. Ni et al. 10.3390/rs15184499
- Multi-Frequency Differential Absorption LIDAR (DIAL) System for Aerosol and Cloud Retrievals of CO2/H2O and CH4/H2O J. Stroud et al. 10.3390/rs15235595
- Technical note: Evaluation of the Copernicus Atmosphere Monitoring Service Cy48R1 upgrade of June 2023 H. Eskes et al. 10.5194/acp-24-9475-2024
- Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. 10.5194/essd-15-3597-2023
- Ship- and aircraft-based XCH4 over oceans as a new tool for satellite validation A. Müller et al. 10.5194/amt-17-1297-2024
- A Review of Satellite-Based CO2 Data Reconstruction Studies: Methodologies, Challenges, and Advances K. Hu et al. 10.3390/rs16203818
- Liquid solvent direct air capture’s cost and carbon dioxide removal vary with ambient environmental conditions P. Shorey & A. Abdulla 10.1038/s43247-024-01773-1
- Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system V. Thilakan et al. 10.5194/acp-24-5315-2024
- An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data K. Hu et al. 10.3390/rs16111907
- Trends and variability in methane concentrations over the Southeastern Arabian Peninsula D. Francis et al. 10.3389/fenvs.2023.1177877
17 citations as recorded by crossref.
- Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO2 Concentrations in the Yangtze River Delta, China Y. Wang et al. 10.1029/2023JD038512
- Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features H. He et al. 10.3389/feart.2024.1352498
- Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China J. Wang et al. 10.3390/rs16050900
- COCCON Measurements of XCO2, XCH4 and XCO over Coal Mine Aggregation Areas in Shanxi, China, and Comparison to TROPOMI and CAMS Datasets Q. Tu et al. 10.3390/rs16214022
- Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion W. Wu et al. 10.3390/rs16030578
- Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning T. Te et al. 10.1016/j.jag.2024.104117
- Small Fires, Big Impact: Evaluating Fire Emission Estimates in Southern Africa Using New Satellite Imagery of Burned Area and Carbon Monoxide I. van der Velde et al. 10.1029/2023GL106122
- Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018–2021 J. Zhong et al. 10.1016/j.scib.2023.08.039
- Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites Q. Ni et al. 10.3390/rs15184499
- Multi-Frequency Differential Absorption LIDAR (DIAL) System for Aerosol and Cloud Retrievals of CO2/H2O and CH4/H2O J. Stroud et al. 10.3390/rs15235595
- Technical note: Evaluation of the Copernicus Atmosphere Monitoring Service Cy48R1 upgrade of June 2023 H. Eskes et al. 10.5194/acp-24-9475-2024
- Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. 10.5194/essd-15-3597-2023
- Ship- and aircraft-based XCH4 over oceans as a new tool for satellite validation A. Müller et al. 10.5194/amt-17-1297-2024
- A Review of Satellite-Based CO2 Data Reconstruction Studies: Methodologies, Challenges, and Advances K. Hu et al. 10.3390/rs16203818
- Liquid solvent direct air capture’s cost and carbon dioxide removal vary with ambient environmental conditions P. Shorey & A. Abdulla 10.1038/s43247-024-01773-1
- Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system V. Thilakan et al. 10.5194/acp-24-5315-2024
- An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data K. Hu et al. 10.3390/rs16111907
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
We present a global dataset of atmospheric CO2 and CH4, the two most important human-made greenhouse gases, which covers almost 2 decades (2003–2020). It is produced by combining satellite data of CO2 and CH4 with a weather and air composition prediction model, and it has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. This dataset can be used for scientific studies in the field of climate change and the global carbon cycle.
We present a global dataset of atmospheric CO2 and CH4, the two most important human-made...
Altmetrics
Final-revised paper
Preprint