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| Titel |
Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation |
| VerfasserIn |
A. Chatterjee, A. M. Michalak |
| 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 ; 13, no. 23 ; Nr. 13, no. 23 (2013-12-03), S.11643-11660 |
| Datensatznummer |
250085847
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| Publikation (Nr.) |
 copernicus.org/acp-13-11643-2013.pdf |
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| Zusammenfassung |
| Data assimilation (DA) approaches, including variational and the ensemble
Kalman filter methods, provide a computationally efficient framework for
solving the CO2 source–sink estimation problem. Unlike DA applications for
weather prediction and constituent assimilation, however, the advantages and
disadvantages of DA approaches for CO2 flux estimation have not been
extensively explored. In this study, we compare and assess estimates from two
advanced DA approaches (an ensemble square root filter and a variational
technique) using a batch inverse modeling setup as a benchmark, within the
context of a simple one-dimensional advection–diffusion prototypical inverse
problem that has been designed to capture the nuances of a real CO2 flux
estimation problem. Experiments are designed to identify the impact of the
observational density, heterogeneity, and uncertainty, as well as operational
constraints (i.e., ensemble size, number of descent iterations) on the DA
estimates relative to the estimates from a batch inverse modeling scheme. No
dynamical model is explicitly specified for the DA approaches to keep the
problem setup analogous to a typical real CO2 flux estimation problem.
Results demonstrate that the performance of the DA approaches depends on a
complex interplay between the measurement network and the operational
constraints. Overall, the variational approach (contingent on the
availability of an adjoint transport model) more reliably captures the
large-scale source–sink patterns. Conversely, the ensemble square root filter
provides more realistic uncertainty estimates. Selection of one approach over
the other must therefore be guided by the carbon science questions being
asked and the operational constraints under which the approaches are being
applied. |
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