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| Titel |
Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods |
| VerfasserIn |
A. L. Ganesan, M. Rigby, A. Zammit-Mangion, A. J. Manning, R. G. Prinn, P. J. Fraser, C. M. Harth, K.-R. Kim, P. B. Krummel, S. Li, J. Mühle, S. J. O'Doherty, S. Park, P. K. Salameh, L. P. Steele, R. F. Weiss |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 8 ; Nr. 14, no. 8 (2014-04-17), S.3855-3864 |
| Datensatznummer |
250118621
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| Publikation (Nr.) |
 copernicus.org/acp-14-3855-2014.pdf |
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| Zusammenfassung |
| 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 (SF6)
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 SF6 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. |
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