 |
| Titel |
An improved Kalman Smoother for atmospheric inversions |
| VerfasserIn |
L. M. P. Bruhwiler, A. M. Michalak, W. Peters, D. F. Baker, P. Tans |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 5, no. 10 ; Nr. 5, no. 10 (2005-10-18), S.2691-2702 |
| Datensatznummer |
250003101
|
| Publikation (Nr.) |
 copernicus.org/acp-5-2691-2005.pdf |
|
|
|
|
|
| Zusammenfassung |
| We explore the use of a fixed-lag Kalman smoother for sequential estimation
of atmospheric carbon dioxide fluxes. This technique takes advantage of the
fact that most of the information about the spatial distribution of sources
and sinks is observable within a few months to half of a year of emission.
After this period, the spatial structure of sources is diluted by transport
and cannot significantly constrain flux estimates. We therefore describe an
estimation technique that steps through the observations sequentially, using
only the subset of observations and modeled transport fields that most
strongly constrain the fluxes at a particular time step. Estimates of each
set of fluxes are sequentially updated multiple times, using measurements
taken at different times, and the estimates and their uncertainties are shown
to quickly converge. Final flux estimates are incorporated into the
background state of CO2 and transported forward in time, and the final
flux uncertainties and covariances are taken into account when estimating the
covariances of the fluxes still being estimated. The computational demands of
this technique are greatly reduced in comparison to the standard Bayesian
synthesis technique where all observations are used at once with transport
fields spanning the entire period of the observations. It therefore becomes
possible to solve larger inverse problems with more observations and for
fluxes discretized at finer spatial scales. We also discuss the differences
between running the inversion simultaneously with the transport model and
running it entirely off-line with pre-calculated transport fields. We find
that the latter can be done with minimal error if time series of transport
fields of adequate length are pre-calculated. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|