Articles | Volume 7, issue 1
https://doi.org/10.5194/gmd-7-303-2014
© Author(s) 2014. 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-7-303-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
13 Feb 2014
Development and technical paper |
| 13 Feb 2014
Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions
S. M. Miller
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
A. M. Michalak
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
P. J. Levi
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
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Cited
39 citations as recorded by crossref.
- A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. https://doi.org/10.1002/2016GB005419
- Spatiotemporal optimization of NOx and VOC emissions using a hybrid inversion framework and implications for ozone sensitivity-regime diagnosis J. Moon et al. https://doi.org/10.5194/acp-26-10379-2026
- 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
- Estimating sources of elemental and organic carbon and their temporal emission patterns using a least squares inverse model and hourly measurements from the St. Louis–Midwest supersite B. de Foy et al. https://doi.org/10.5194/acp-15-2405-2015
- A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems V. Yadav et al. https://doi.org/10.1002/2016JD025642
- Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 J. Maasakkers et al. https://doi.org/10.5194/acp-19-7859-2019
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. https://doi.org/10.5194/gmd-15-45-2022
- 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
- Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions S. Jeong et al. https://doi.org/10.1029/2017JD028166
- Inverse modelling of New Zealand's carbon dioxide balance estimates a larger than expected carbon sink B. Bukosa et al. https://doi.org/10.5194/acp-25-6445-2025
- Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations D. Pendergrass et al. https://doi.org/10.5194/acp-25-14353-2025
- Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. https://doi.org/10.5194/acp-16-14371-2016
- Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide N. Cressie https://doi.org/10.1080/01621459.2017.1419136
- ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application P. Bosman & M. Krol https://doi.org/10.5194/gmd-16-47-2023
- Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo M. Lunt et al. https://doi.org/10.5194/gmd-9-3213-2016
- Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations X. Lu et al. https://doi.org/10.5194/acp-22-395-2022
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. https://doi.org/10.5194/gmd-15-5547-2022
- Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics X. Lu et al. https://doi.org/10.1073/pnas.2217900120
- U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations M. Zhang et al. https://doi.org/10.1021/acs.est.4c00380
- Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework C. Nevison et al. https://doi.org/10.1002/2017GB005759
- Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion A. Zammit-Mangion et al. https://doi.org/10.1016/j.chemolab.2015.09.006
- Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations L. Estrada et al. https://doi.org/10.5194/gmd-18-3311-2025
- Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and a regional Bayesian inversion A. Luhar et al. https://doi.org/10.5194/acp-20-15487-2020
- Atmospheric inverse modeling via sparse reconstruction N. Hase et al. https://doi.org/10.5194/gmd-10-3695-2017
- 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
- Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. https://doi.org/10.1029/2018GB006065
- Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin Y. Cui et al. https://doi.org/10.1002/2014JD023002
- On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART O. Tichý et al. https://doi.org/10.5194/gmd-13-5917-2020
- 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
- Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity V. Yadav et al. https://doi.org/10.1029/2018JD030062
- Very Strong Atmospheric Methane Growth in the 4 Years 2014–2017: Implications for the Paris Agreement E. Nisbet et al. https://doi.org/10.1029/2018GB006009
- Sparse optimization for inverse problems in atmospheric modelling L. Adam & M. Branda https://doi.org/10.1016/j.envsoft.2016.02.002
- Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion A. Zammit-Mangion et al. https://doi.org/10.1016/j.spasta.2016.06.005
- Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. https://doi.org/10.5194/acp-19-4345-2019
- 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
- Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01 J. Thanwerdas et al. https://doi.org/10.5194/gmd-18-1505-2025
- Evaluating the effectiveness of a geostatistical approach with groundwater flow modeling for three-dimensional estimation of a contaminant plume S. Takai et al. https://doi.org/10.1016/j.jconhyd.2022.104097
- Methane (CH4) emission estimates for South Korea using Lagrangian-based inversion modeling (2010–2021) S. Kenea et al. https://doi.org/10.1016/j.aeaoa.2026.100440
- Estimating methane emissions in California's urban and rural regions using multitower observations S. Jeong et al. https://doi.org/10.1002/2016JD025404
39 citations as recorded by crossref.
- A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. https://doi.org/10.1002/2016GB005419
- Spatiotemporal optimization of NOx and VOC emissions using a hybrid inversion framework and implications for ozone sensitivity-regime diagnosis J. Moon et al. https://doi.org/10.5194/acp-26-10379-2026
- 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
- Estimating sources of elemental and organic carbon and their temporal emission patterns using a least squares inverse model and hourly measurements from the St. Louis–Midwest supersite B. de Foy et al. https://doi.org/10.5194/acp-15-2405-2015
- A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems V. Yadav et al. https://doi.org/10.1002/2016JD025642
- Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 J. Maasakkers et al. https://doi.org/10.5194/acp-19-7859-2019
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. https://doi.org/10.5194/gmd-15-45-2022
- 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
- Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions S. Jeong et al. https://doi.org/10.1029/2017JD028166
- Inverse modelling of New Zealand's carbon dioxide balance estimates a larger than expected carbon sink B. Bukosa et al. https://doi.org/10.5194/acp-25-6445-2025
- Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations D. Pendergrass et al. https://doi.org/10.5194/acp-25-14353-2025
- Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. https://doi.org/10.5194/acp-16-14371-2016
- Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide N. Cressie https://doi.org/10.1080/01621459.2017.1419136
- ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application P. Bosman & M. Krol https://doi.org/10.5194/gmd-16-47-2023
- Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo M. Lunt et al. https://doi.org/10.5194/gmd-9-3213-2016
- Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations X. Lu et al. https://doi.org/10.5194/acp-22-395-2022
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. https://doi.org/10.5194/gmd-15-5547-2022
- Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics X. Lu et al. https://doi.org/10.1073/pnas.2217900120
- U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations M. Zhang et al. https://doi.org/10.1021/acs.est.4c00380
- Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework C. Nevison et al. https://doi.org/10.1002/2017GB005759
- Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion A. Zammit-Mangion et al. https://doi.org/10.1016/j.chemolab.2015.09.006
- Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations L. Estrada et al. https://doi.org/10.5194/gmd-18-3311-2025
- Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and a regional Bayesian inversion A. Luhar et al. https://doi.org/10.5194/acp-20-15487-2020
- Atmospheric inverse modeling via sparse reconstruction N. Hase et al. https://doi.org/10.5194/gmd-10-3695-2017
- 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
- Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. https://doi.org/10.1029/2018GB006065
- Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin Y. Cui et al. https://doi.org/10.1002/2014JD023002
- On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART O. Tichý et al. https://doi.org/10.5194/gmd-13-5917-2020
- 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
- Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity V. Yadav et al. https://doi.org/10.1029/2018JD030062
- Very Strong Atmospheric Methane Growth in the 4 Years 2014–2017: Implications for the Paris Agreement E. Nisbet et al. https://doi.org/10.1029/2018GB006009
- Sparse optimization for inverse problems in atmospheric modelling L. Adam & M. Branda https://doi.org/10.1016/j.envsoft.2016.02.002
- Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion A. Zammit-Mangion et al. https://doi.org/10.1016/j.spasta.2016.06.005
- Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. https://doi.org/10.5194/acp-19-4345-2019
- 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
- Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01 J. Thanwerdas et al. https://doi.org/10.5194/gmd-18-1505-2025
- Evaluating the effectiveness of a geostatistical approach with groundwater flow modeling for three-dimensional estimation of a contaminant plume S. Takai et al. https://doi.org/10.1016/j.jconhyd.2022.104097
- Methane (CH4) emission estimates for South Korea using Lagrangian-based inversion modeling (2010–2021) S. Kenea et al. https://doi.org/10.1016/j.aeaoa.2026.100440
- Estimating methane emissions in California's urban and rural regions using multitower observations S. Jeong et al. https://doi.org/10.1002/2016JD025404
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