Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1771-2020
© Author(s) 2020. 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-13-1771-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Apr 2020
Development and technical paper |
| 02 Apr 2020
| 02 Apr 2020
Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite
Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA
Arvind K. Saibaba
Department of Mathematics, North Carolina State University, Raleigh, NC, USA
Michael E. Trudeau
Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA
Marikate E. Mountain
Atmospheric and Environmental Research, Inc., Lexington, MA, USA
Arlyn E. Andrews
Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA
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Cited
28 citations as recorded by crossref.
- Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems K. Hall-Hooper et al. https://doi.org/10.1007/s10444-024-10208-6
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. https://doi.org/10.5194/gmd-15-5547-2022
- National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake B. Byrne et al. https://doi.org/10.5194/essd-15-963-2023
- Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements D. Wesloh et al. https://doi.org/10.1029/2023JG007526
- Airborne observations reveal the fate of the methane from the Nord Stream pipelines F. Reum et al. https://doi.org/10.1038/s41467-024-53780-7
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- Research in Inverse Problems and Training in Computational Science: A Reflection on the Importance of Community J. Chung https://doi.org/10.1109/MCSE.2021.3119432
- A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2) M. Sabaté Landman et al. https://doi.org/10.5194/gmd-17-8853-2024
- Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. https://doi.org/10.5194/amt-14-5521-2021
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. https://doi.org/10.3788/AOS231293
- 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 S. Maksyutov et al. https://doi.org/10.5194/acp-21-1245-2021
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. https://doi.org/10.5194/gmd-14-5331-2021
- A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management N. Parazoo et al. https://doi.org/10.1016/j.earscirev.2026.105493
- Data reduction for inverse modeling: an adaptive approach v1.0 X. Liu et al. https://doi.org/10.5194/gmd-14-4683-2021
- Augmented flexible Krylov subspace methods with applications to Bayesian inverse problems M. Sabaté Landman et al. https://doi.org/10.1016/j.laa.2024.05.007
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems J. Chung et al. https://doi.org/10.1137/22M1502197
- Solar‐Induced Fluorescence Helps Constrain Global Patterns in Net Biosphere Exchange, as Estimated Using Atmospheric CO2 Observations M. Zhang et al. https://doi.org/10.1029/2023JG007703
- Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. https://doi.org/10.5194/acp-22-9617-2022
- Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models Z. Chen et al. https://doi.org/10.5194/acp-21-6663-2021
- U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations M. Zhang et al. https://doi.org/10.1021/acs.est.4c00380
- Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models Z. Chen et al. https://doi.org/10.1088/1748-9326/abfac1
- Why make inverse modeling and which methods to use in agriculture? A review Y. Zhang et al. https://doi.org/10.1016/j.compag.2024.108624
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. https://doi.org/10.5194/gmd-15-45-2022
- Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires H. Chen et al. https://doi.org/10.1088/1748-9326/ad3cf7
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. https://doi.org/10.1029/2022JD037773
- Flexible Krylov methods for group sparsity regularization J. Chung & M. Sabaté Landman https://doi.org/10.1088/1402-4896/ad88af
- California dominates U.S. emissions of the pesticide and potent greenhouse gas sulfuryl fluoride D. Gaeta et al. https://doi.org/10.1038/s43247-024-01294-x
28 citations as recorded by crossref.
- Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems K. Hall-Hooper et al. https://doi.org/10.1007/s10444-024-10208-6
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. https://doi.org/10.5194/gmd-15-5547-2022
- National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake B. Byrne et al. https://doi.org/10.5194/essd-15-963-2023
- Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements D. Wesloh et al. https://doi.org/10.1029/2023JG007526
- Airborne observations reveal the fate of the methane from the Nord Stream pipelines F. Reum et al. https://doi.org/10.1038/s41467-024-53780-7
- Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021) M. Omara et al. https://doi.org/10.5194/essd-16-3973-2024
- Research in Inverse Problems and Training in Computational Science: A Reflection on the Importance of Community J. Chung https://doi.org/10.1109/MCSE.2021.3119432
- A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2) M. Sabaté Landman et al. https://doi.org/10.5194/gmd-17-8853-2024
- Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. https://doi.org/10.5194/amt-14-5521-2021
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. https://doi.org/10.3788/AOS231293
- 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 S. Maksyutov et al. https://doi.org/10.5194/acp-21-1245-2021
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. https://doi.org/10.5194/gmd-14-5331-2021
- A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management N. Parazoo et al. https://doi.org/10.1016/j.earscirev.2026.105493
- Data reduction for inverse modeling: an adaptive approach v1.0 X. Liu et al. https://doi.org/10.5194/gmd-14-4683-2021
- Augmented flexible Krylov subspace methods with applications to Bayesian inverse problems M. Sabaté Landman et al. https://doi.org/10.1016/j.laa.2024.05.007
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems J. Chung et al. https://doi.org/10.1137/22M1502197
- Solar‐Induced Fluorescence Helps Constrain Global Patterns in Net Biosphere Exchange, as Estimated Using Atmospheric CO2 Observations M. Zhang et al. https://doi.org/10.1029/2023JG007703
- Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. https://doi.org/10.5194/acp-22-9617-2022
- Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models Z. Chen et al. https://doi.org/10.5194/acp-21-6663-2021
- U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations M. Zhang et al. https://doi.org/10.1021/acs.est.4c00380
- Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models Z. Chen et al. https://doi.org/10.1088/1748-9326/abfac1
- Why make inverse modeling and which methods to use in agriculture? A review Y. Zhang et al. https://doi.org/10.1016/j.compag.2024.108624
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. https://doi.org/10.5194/gmd-15-45-2022
- Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires H. Chen et al. https://doi.org/10.1088/1748-9326/ad3cf7
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. https://doi.org/10.1029/2022JD037773
- Flexible Krylov methods for group sparsity regularization J. Chung & M. Sabaté Landman https://doi.org/10.1088/1402-4896/ad88af
- California dominates U.S. emissions of the pesticide and potent greenhouse gas sulfuryl fluoride D. Gaeta et al. https://doi.org/10.1038/s43247-024-01294-x
Saved (final revised paper)
Latest update: 21 Sep 2026
Short summary
New observations of greenhouse gases from satellites and aircraft provide an unprecedented window into global carbon sources and sinks. However, these new datasets also present enormous computational challenges due to the sheer number of observations. In this article, we discuss the challenges of estimating greenhouse gas source and sinks using very large atmospheric datasets and evaluate several strategies for overcoming these challenges.
New observations of greenhouse gases from satellites and aircraft provide an unprecedented...
