Articles | Volume 6, issue 3
https://doi.org/10.5194/gmd-6-583-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-6-583-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
03 May 2013
Development and technical paper |
| 03 May 2013
Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation
V. Yadav
Department of Global Ecology, Carnegie Institution for Science, Stanford, California, 94305, USA
A. M. Michalak
Department of Global Ecology, Carnegie Institution for Science, Stanford, California, 94305, USA
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Cited
35 citations as recorded by crossref.
- A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems V. Yadav et al. https://doi.org/10.1002/2016JD025642
- An efficient method to solve large linearizable inverse problems under Gaussian and separability assumptions A. Zunino & K. Mosegaard https://doi.org/10.1016/j.cageo.2018.09.005
- Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology H. Ghorbanidehno et al. https://doi.org/10.1016/j.jhydrol.2020.125266
- An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
- U.S. emissions of HFC‐134a derived for 2008–2012 from an extensive flask‐air sampling network L. Hu et al. https://doi.org/10.1002/2014JD022617
- Enhanced North American carbon uptake associated with El Niño L. Hu et al. https://doi.org/10.1126/sciadv.aaw0076
- Development of a Mesoscale Inversion System for Estimating Continental‐Scale CO2 Fluxes D. Wesloh et al. https://doi.org/10.1029/2019MS001818
- Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation A. Chatterjee & A. Michalak https://doi.org/10.5194/acp-13-11643-2013
- Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite S. Miller et al. https://doi.org/10.5194/gmd-13-1771-2020
- Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange C. Alden et al. https://doi.org/10.1111/gcb.13305
- 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
- Precise Monitoring and Source Analysis of Fugitive GHG Emissions: A Case Study of Nansha, Guangdong Y. Hu et al. https://doi.org/10.3390/pr14091344
- Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. https://doi.org/10.5194/acp-19-13911-2019
- Top-down constraints on N2O emissions from Canada C. Nevison et al. https://doi.org/10.1016/j.atmosenv.2023.120075
- Simulating out-of-sample atmospheric transport to enable flux inversions N. Dadheech & A. Turner https://doi.org/10.5194/acp-26-427-2026
- Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO<sub>2</sub> sampling on the inference of terrestrial biospheric sources and sinks J. Liu et al. https://doi.org/10.3402/tellusb.v66.22486
- Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT L. Kunik et al. https://doi.org/10.1525/elementa.375
- Remote Sensing Soil Freeze‐Thaw Status and North American N2O Emissions From a Regional Inversion C. Nevison et al. https://doi.org/10.1029/2023GB007759
- Greenhouse gas fluxes from Alaska's North Slope inferred from the Airborne Carbon Measurements campaign (ACME-V) J. Tadić et al. https://doi.org/10.1016/j.atmosenv.2021.118239
- U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations M. Zhang et al. https://doi.org/10.1021/acs.est.4c00380
- High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport N. Dadheech et al. https://doi.org/10.5194/acp-25-5159-2025
- Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions S. Jeong et al. https://doi.org/10.1021/acs.est.4c09395
- Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion Z. Liu et al. https://doi.org/10.1016/j.atmosenv.2024.120446
- Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework C. Nevison et al. https://doi.org/10.1002/2017GB005759
- A gap in nitrous oxide emission reporting complicates long-term climate mitigation S. Del Grosso et al. https://doi.org/10.1073/pnas.2200354119
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. https://doi.org/10.1021/acs.est.3c09642
- Development of the WRF-CO2 4D-Var assimilation system v1.0 T. Zheng et al. https://doi.org/10.5194/gmd-11-1725-2018
- Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network A. Ganesan et al. https://doi.org/10.5194/acp-15-6393-2015
- Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density A. Turner et al. https://doi.org/10.5194/acp-16-13465-2016
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. https://doi.org/10.5194/gmd-15-5547-2022
- 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
- Constraining urban fossil fuel CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling S. Sim & S. Jeong https://doi.org/10.5194/acp-25-18509-2025
- Continued emissions of carbon tetrachloride from the United States nearly two decades after its phaseout for dispersive uses L. Hu et al. https://doi.org/10.1073/pnas.1522284113
- A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. https://doi.org/10.1002/2016GB005419
- Inversion of CO emissions in Greater Bay Area over southern China using a WRF-STILT-Bayesian framework X. Lu et al. https://doi.org/10.1016/j.cacint.2026.100308
35 citations as recorded by crossref.
- A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems V. Yadav et al. https://doi.org/10.1002/2016JD025642
- An efficient method to solve large linearizable inverse problems under Gaussian and separability assumptions A. Zunino & K. Mosegaard https://doi.org/10.1016/j.cageo.2018.09.005
- Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology H. Ghorbanidehno et al. https://doi.org/10.1016/j.jhydrol.2020.125266
- An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
- U.S. emissions of HFC‐134a derived for 2008–2012 from an extensive flask‐air sampling network L. Hu et al. https://doi.org/10.1002/2014JD022617
- Enhanced North American carbon uptake associated with El Niño L. Hu et al. https://doi.org/10.1126/sciadv.aaw0076
- Development of a Mesoscale Inversion System for Estimating Continental‐Scale CO2 Fluxes D. Wesloh et al. https://doi.org/10.1029/2019MS001818
- Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation A. Chatterjee & A. Michalak https://doi.org/10.5194/acp-13-11643-2013
- Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite S. Miller et al. https://doi.org/10.5194/gmd-13-1771-2020
- Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange C. Alden et al. https://doi.org/10.1111/gcb.13305
- 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
- Precise Monitoring and Source Analysis of Fugitive GHG Emissions: A Case Study of Nansha, Guangdong Y. Hu et al. https://doi.org/10.3390/pr14091344
- Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. https://doi.org/10.5194/acp-19-13911-2019
- Top-down constraints on N2O emissions from Canada C. Nevison et al. https://doi.org/10.1016/j.atmosenv.2023.120075
- Simulating out-of-sample atmospheric transport to enable flux inversions N. Dadheech & A. Turner https://doi.org/10.5194/acp-26-427-2026
- Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO<sub>2</sub> sampling on the inference of terrestrial biospheric sources and sinks J. Liu et al. https://doi.org/10.3402/tellusb.v66.22486
- Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT L. Kunik et al. https://doi.org/10.1525/elementa.375
- Remote Sensing Soil Freeze‐Thaw Status and North American N2O Emissions From a Regional Inversion C. Nevison et al. https://doi.org/10.1029/2023GB007759
- Greenhouse gas fluxes from Alaska's North Slope inferred from the Airborne Carbon Measurements campaign (ACME-V) J. Tadić et al. https://doi.org/10.1016/j.atmosenv.2021.118239
- U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations M. Zhang et al. https://doi.org/10.1021/acs.est.4c00380
- High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport N. Dadheech et al. https://doi.org/10.5194/acp-25-5159-2025
- Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions S. Jeong et al. https://doi.org/10.1021/acs.est.4c09395
- Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion Z. Liu et al. https://doi.org/10.1016/j.atmosenv.2024.120446
- Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework C. Nevison et al. https://doi.org/10.1002/2017GB005759
- A gap in nitrous oxide emission reporting complicates long-term climate mitigation S. Del Grosso et al. https://doi.org/10.1073/pnas.2200354119
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. https://doi.org/10.1021/acs.est.3c09642
- Development of the WRF-CO2 4D-Var assimilation system v1.0 T. Zheng et al. https://doi.org/10.5194/gmd-11-1725-2018
- Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network A. Ganesan et al. https://doi.org/10.5194/acp-15-6393-2015
- Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density A. Turner et al. https://doi.org/10.5194/acp-16-13465-2016
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. https://doi.org/10.5194/gmd-15-5547-2022
- 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
- Constraining urban fossil fuel CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling S. Sim & S. Jeong https://doi.org/10.5194/acp-25-18509-2025
- Continued emissions of carbon tetrachloride from the United States nearly two decades after its phaseout for dispersive uses L. Hu et al. https://doi.org/10.1073/pnas.1522284113
- A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. https://doi.org/10.1002/2016GB005419
- Inversion of CO emissions in Greater Bay Area over southern China using a WRF-STILT-Bayesian framework X. Lu et al. https://doi.org/10.1016/j.cacint.2026.100308
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