Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By...
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| Format: | Članak |
| Jezik: | en_US |
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Copernicus GmbH
2014
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| Online pristup: | http://hdl.handle.net/1721.1/88009 https://orcid.org/0000-0001-5925-3801 |
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| author | Rigby, M. Zammit-Mangion, A. Manning, Alistair J. Fraser, P. J. Harth, C. M. Kim, K.-R. Krummel, P. B. Li, S. O'Doherty, Simon Park, S. Salameh, P. K. Steele, L. P. Weiss, R. F. Ganesan, Anita Lakshmi Prinn, Ronald G. Muhle, Jens |
| author2 | Massachusetts Institute of Technology. Center for Global Change Science |
| author_facet | Massachusetts Institute of Technology. Center for Global Change Science Rigby, M. Zammit-Mangion, A. Manning, Alistair J. Fraser, P. J. Harth, C. M. Kim, K.-R. Krummel, P. B. Li, S. O'Doherty, Simon Park, S. Salameh, P. K. Steele, L. P. Weiss, R. F. Ganesan, Anita Lakshmi Prinn, Ronald G. Muhle, Jens |
| author_sort | Rigby, M. |
| collection | MIT |
| description | We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF[subscript 6]) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF[subscript 6] case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods. |
| first_indexed | 2024-09-23T12:54:58Z |
| format | Article |
| id | mit-1721.1/88009 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:54:58Z |
| publishDate | 2014 |
| publisher | Copernicus GmbH |
| record_format | dspace |
| spelling | mit-1721.1/880092022-09-28T10:52:46Z Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods Rigby, M. Zammit-Mangion, A. Manning, Alistair J. Fraser, P. J. Harth, C. M. Kim, K.-R. Krummel, P. B. Li, S. O'Doherty, Simon Park, S. Salameh, P. K. Steele, L. P. Weiss, R. F. Ganesan, Anita Lakshmi Prinn, Ronald G. Muhle, Jens Massachusetts Institute of Technology. Center for Global Change Science Ganesan, Anita Lakshmi Prinn, Ronald G. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF[subscript 6]) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF[subscript 6] case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods. United States. National Aeronautics and Space Administration (Grant NNX11AF17G) United States. National Aeronautics and Space Administration (Grant NNX11AF16G) United States. National Aeronautics and Space Administration (Grant NNX11AF15G) 2014-06-16T19:06:05Z 2014-06-16T19:06:05Z 2014-04 2014-02 Article http://purl.org/eprint/type/JournalArticle 1680-7324 1680-7316 http://hdl.handle.net/1721.1/88009 Ganesan, A. L., M. Rigby, A. Zammit-Mangion, A. J. Manning, R. G. Prinn, P. J. Fraser, C. M. Harth, et al. “Characterization of Uncertainties in Atmospheric Trace Gas Inversions Using Hierarchical Bayesian Methods.” Atmospheric Chemistry and Physics 14, no. 8 (April 17, 2014): 3855–3864. https://orcid.org/0000-0001-5925-3801 en_US http://dx.doi.org/10.5194/acp-14-3855-2014 Atmospheric Chemistry and Physics Creative Commons Attribution http://creativecommons.org/licenses/by/3.0/ application/pdf Copernicus GmbH Copernicus Publications |
| spellingShingle | Rigby, M. Zammit-Mangion, A. Manning, Alistair J. Fraser, P. J. Harth, C. M. Kim, K.-R. Krummel, P. B. Li, S. O'Doherty, Simon Park, S. Salameh, P. K. Steele, L. P. Weiss, R. F. Ganesan, Anita Lakshmi Prinn, Ronald G. Muhle, Jens Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| title | Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| title_full | Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| title_fullStr | Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| title_full_unstemmed | Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| title_short | Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| title_sort | characterization of uncertainties in atmospheric trace gas inversions using hierarchical bayesian methods |
| url | http://hdl.handle.net/1721.1/88009 https://orcid.org/0000-0001-5925-3801 |
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