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| Titel |
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction |
| VerfasserIn |
M. Buehner, J. Morneau, C. Charette |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 20, no. 5 ; Nr. 20, no. 5 (2013-09-24), S.669-682 |
| Datensatznummer |
250086048
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| Publikation (Nr.) |
 copernicus.org/npg-20-669-2013.pdf |
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| Zusammenfassung |
The goal of this study is to evaluate a version of the ensemble-variational
data assimilation approach (EnVar) for possible replacement of 4D-Var at
Environment Canada for global deterministic weather prediction. This
implementation of EnVar relies on 4-D ensemble covariances, obtained from an
ensemble Kalman filter, that are combined in a vertically dependent weighted
average with simple static covariances. Verification results are presented
from a set of data assimilation experiments over two separate 6-week periods
that used assimilated observations and model configuration very similar to
the currently operational system. To help interpret the comparison of EnVar
versus 4D-Var, additional experiments using 3D-Var and a version of EnVar
with only 3-D ensemble covariances are also evaluated. To improve the rate of
convergence for all approaches evaluated (including EnVar), an estimate of
the cost function Hessian generated by the quasi-Newton minimization
algorithm is cycled from one analysis to the next.
Analyses from EnVar (with 4-D ensemble covariances) nearly always produce
improved, and never degraded, forecasts when compared with 3D-Var.
Comparisons with 4D-Var show that forecasts from EnVar analyses have either
similar or better scores in the troposphere of the tropics and the winter
extra-tropical region. However, in the summer extra-tropical region the
medium-range forecasts from EnVar have either similar or worse scores than
4D-Var in the troposphere. In contrast, the 6 h forecasts from EnVar are
significantly better than 4D-Var relative to radiosonde observations for
both periods and in all regions. The use of 4-D versus 3-D ensemble
covariances only results in small improvements in forecast quality. By
contrast, the improvements from using 4D-Var versus 3D-Var are much larger.
Measurement of the fit of the background and analyzed states to the
observations suggests that EnVar and 4D-Var can both make better use of
observations distributed over time than 3D-Var. In summary, the results from
this study suggest that the EnVar approach is a viable alternative to
4D-Var, especially when the simplicity and computational efficiency of EnVar
are considered. Additional research is required to understand the seasonal
dependence of the difference in forecast quality between EnVar and 4D-Var in
the extra-tropics. |
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