Articles | Volume 15, issue 12
https://doi.org/10.5194/acp-15-7039-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-15-7039-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Jun 2015
Research article |
| 30 Jun 2015
Balancing aggregation and smoothing errors in inverse models
School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
D. J. Jacob
School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, USA
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39 citations as recorded by crossref.
- Estimation of Anthropogenic CH4 and CO2 Emissions in Taiyuan‐Jinzhong Region: One of the World's Largest Emission Hotspots C. Hu et al. 10.1029/2022JD037915
- 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 CH<sub>4</sub> ObsPack) and satellite (GOSAT) atmospheric observations X. Lu et al. 10.5194/acp-22-395-2022
- Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area J. Sheng et al. 10.1088/1748-9326/ac24d1
- Observed changes in China’s methane emissions linked to policy drivers Y. Zhang et al. 10.1073/pnas.2202742119
- Development of the WRF-CO2 4D-Var assimilation system v1.0 T. Zheng et al. 10.5194/gmd-11-1725-2018
- Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US J. Sheng et al. 10.5194/amt-11-6379-2018
- Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations D. Varon et al. 10.5194/gmd-15-5787-2022
- Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. 10.5194/amt-14-5521-2021
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane J. Maasakkers et al. 10.5194/acp-21-4339-2021
- Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0 G. Monteil & M. Scholze 10.5194/gmd-14-3383-2021
- High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources J. Sheng et al. 10.5194/acp-18-6483-2018
- Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al. 10.5194/acp-22-10809-2022
- Optimal and scalable methods to approximate the solutions of large‐scale Bayesian problems: theory and application to atmospheric inversion and data assimilation N. Bousserez & D. Henze 10.1002/qj.3209
- Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action Z. Chen et al. 10.5194/acp-23-5945-2023
- Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
- Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions X. Yu et al. 10.5194/acp-21-951-2021
- Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. 10.5194/acp-16-14371-2016
- Remote sensing evidence of decadal changes in major tropospheric ozone precursors over East Asia A. Souri et al. 10.1002/2016JD025663
- East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis R. Liang et al. 10.5194/acp-23-8039-2023
- Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo M. Lunt et al. 10.5194/gmd-9-3213-2016
- Constraints on methane emissions in North America from future geostationary remote-sensing measurements N. Bousserez et al. 10.5194/acp-16-6175-2016
- Assessing the Iterative Finite Difference Mass Balance and 4D‐Var Methods to Derive Ammonia Emissions Over North America Using Synthetic Observations C. Li et al. 10.1029/2018JD030183
- Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics X. Lu et al. 10.1073/pnas.2217900120
- Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data A. Turner et al. 10.5194/acp-15-7049-2015
- Extreme events driving year-to-year differences in gross primary productivity across the US A. Turner et al. 10.5194/bg-18-6579-2021
- First Top‐Down Estimates of Anthropogenic NOx Emissions Using High‐Resolution Airborne Remote Sensing Observations A. Souri et al. 10.1002/2017JD028009
- 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. 10.5194/acp-19-7859-2019
- An inversion of NO<sub><i>x</i></sub> and non-methane volatile organic compound (NMVOC) emissions using satellite observations during the KORUS-AQ campaign and implications for surface ozone over East Asia A. Souri et al. 10.5194/acp-20-9837-2020
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- Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. 10.5194/acp-18-8265-2018
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
- National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al. 10.1038/s41467-023-40671-6
- On the representation error in data assimilation T. Janjić et al. 10.1002/qj.3130
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- Revisiting global fossil fuel and biofuel emissions of ethane Z. Tzompa‐Sosa et al. 10.1002/2016JD025767
- Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework M. Bertolacci et al. 10.1214/23-AOAS1790
3 citations as recorded by crossref.
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- Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions A. Babenhauserheide et al. 10.5194/acp-15-9747-2015
- Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign A. Souri et al. 10.1016/j.atmosenv.2016.02.020
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