 |
| Titel |
Data assimilation of two-dimensional geophysical flows with a Variational Ensemble Kalman Filter |
| VerfasserIn |
Z. Mussa, I. Amour, A. Bibov, T. Kauranne |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
2198-5634
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 1 ; Nr. 1, no. 1 (2014-03-31), S.403-446 |
| Datensatznummer |
250115081
|
| Publikation (Nr.) |
 copernicus.org/npgd-1-403-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
The Variational Ensemble Kalman Filter (VEnKF), a recent data assimilation
method that combines a variational assimilation of the Bayesian estimation
problem with an ensemble of forecasts, is demonstrated in two-dimensional
geophysical flows using a Quasi-Geostrophic (QG) model and a shallow water
model. Using a synthetic experiment, a two layer QG model with model bias is
solved on a cylindrical 40 x 20 domain. The performance of VEnKF on
the QG model with increasing ensemble size is compared with the classical
Extended Kalman Filter (EKF). It is shown that although convergence can be
achieved with just 20 ensemble members, increasing the number of members
results in a better estimate that approaches the one produced by EKF.
In the second test case, a 2-D shallow water model is described using a real
dam-break experiment. The VEnKF algorithm was used to assimilate observations
obtained from a modified laboratory dam-break experiment with a
two-dimensional setup of sensors at the downstream end. The wave meters are
placed parallel to the direction of the flow alongside the flume walls to
capture both cross flow and stream flow.
In both test cases, VEnKF was able to predict genuinely two-dimensional flow
patterns when the sensors had a two-dimensional geometry and was stable
against model bias in the first test case.
In the second test case, the experiments are complemented with an empirical
study of the impact of observation interpolation on the stability of the
VEnKF filter. In this study, a novel Courant–Friedrichs–Lewy type filter
stability condition is observed that relates ensemble variance to the time
interpolation distance between observations.
The results of the two experiments shows that VEnKF is a good candidate for
data assimilation problems and can be implemented in higher dimensional
nonlinear models. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|