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| Titel |
Bayesian statistical modeling of spatially correlated error structure in atmospheric tracer inverse analysis |
| VerfasserIn |
C. Mukherjee, P. S. Kasibhatla, M. West |
| 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 ; 11, no. 11 ; Nr. 11, no. 11 (2011-06-09), S.5365-5382 |
| Datensatznummer |
250009808
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| Publikation (Nr.) |
 copernicus.org/acp-11-5365-2011.pdf |
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| Zusammenfassung |
| We present and discuss the use of Bayesian modeling and computational methods for
atmospheric chemistry inverse analyses that incorporate evaluation of spatial structure in
model-data residuals. Motivated by problems of refining bottom-up estimates of
source/sink fluxes of trace gas and aerosols based on satellite retrievals of atmospheric chemical
concentrations, we address the need for formal modeling of spatial residual error structure in global
scale inversion models. We do this using analytically and
computationally tractable conditional autoregressive (CAR) spatial models as components of a global
inversion framework. We develop Markov chain Monte Carlo methods to
explore and fit these spatial structures in an overall statistical
framework that simultaneously estimates source fluxes. Additional
aspects of the study extend the statistical framework to utilize
priors on source fluxes in a physically realistic manner, and to formally address
and deal with missing data in satellite retrievals. We demonstrate
the analysis in the context of inferring carbon monoxide (CO) sources
constrained by satellite retrievals of column CO from the Measurement
of Pollution in the Troposphere (MOPITT) instrument on the TERRA
satellite, paying special attention to evaluating performance of the
inverse approach using various statistical diagnostic metrics. This
is developed using synthetic data generated to resemble MOPITT data to
define a proof-of-concept and model assessment, and then in analysis
of real MOPITT data. These studies demonstrate the ability of these simple
spatial models to substantially improve over standard non-spatial models in terms of
statistical fit, ability to recover sources in synthetic examples, and predictive match with
real data. |
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