Articles | Volume 14, issue 8
https://doi.org/10.5194/acp-14-3855-2014
© 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/acp-14-3855-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Apr 2014
Research article |
| 17 Apr 2014
Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
A. L. Ganesan
Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
now at: School of Chemistry, University of Bristol, Bristol, UK
School of Chemistry, University of Bristol, Bristol, UK
A. Zammit-Mangion
School of Geographical Sciences, University of Bristol, Bristol, UK
A. J. Manning
Hadley Centre, Met Office, Exeter, UK
R. G. Prinn
Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
P. J. Fraser
Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia
C. M. Harth
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
K.-R. Kim
GIST College, Gwangju Institute of Science and Technology, Kwangju, South Korea
P. B. Krummel
Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia
S. Li
Research Institute of Oceanography, Seoul National University, Seoul, South Korea
J. Mühle
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
S. J. O'Doherty
School of Chemistry, University of Bristol, Bristol, UK
S. Park
Department of Oceanography, Kyungpook National University, Sangju, South Korea
P. K. Salameh
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
L. P. Steele
Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia
R. F. Weiss
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
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- Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion A. Zammit-Mangion et al. 10.1016/j.chemolab.2015.09.006
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- Emissions and Marine Boundary Layer Concentrations of Unregulated Chlorocarbons Measured at Cape Point, South Africa D. Say et al. 10.1021/acs.est.0c02057
- Enhanced Methane Emissions during Amazonian Drought by Biomass Burning M. Saito et al. 10.1371/journal.pone.0166039
- A large increase in U.S. methane emissions over the past decade inferred from satellite data and surface observations A. Turner et al. 10.1002/2016GL067987
- Hierarchical Bayesian calibration of nitrous oxide (N2O) and nitrogen monoxide (NO) flux module of an agro-ecosystem model: ECOSSE X. Li et al. 10.1016/j.ecolmodel.2015.07.020
- Site-specific (Multi-scenario) validation of ensemble Kalman filter-based source inversion through multi-direction wind tunnel experiments S. Sun et al. 10.1016/j.jenvrad.2018.12.005
- Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite S. Miller et al. 10.5194/gmd-13-1771-2020
- Atmospheric observations show accurate reporting and little growth in India’s methane emissions A. Ganesan et al. 10.1038/s41467-017-00994-7
- Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling A. Nickless et al. 10.5194/acp-18-4765-2018
- Anthropogenic Chloroform Emissions from China Drive Changes in Global Emissions M. An et al. 10.1021/acs.est.3c01898
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. 10.5194/gmd-15-5547-2022
- Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network A. Ganesan et al. 10.5194/acp-15-6393-2015
- Data assimilation using an ensemble of models: a hierarchical approach P. Rayner 10.5194/acp-20-3725-2020
- impulseest: A Python package for non-parametric impulse response estimation with input–output data L. Fiorio et al. 10.1016/j.softx.2021.100761
- Sources and fate of atmospheric microplastics revealed from inverse and dispersion modelling: From global emissions to deposition N. Evangeliou et al. 10.1016/j.jhazmat.2022.128585
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