- Title Pages
- [UNTITLED]
- Previous sessions
- Preface
- List of participants
-
1 4D-VAR: four-dimensional variational assimilation -
2 Four-dimensional variational data assimilation -
3 Introduction to the Kalman filter -
4 Smoothers -
5 Observation influence diagnostic of a data assimilation system -
6 Observation impact on the short-range forecast -
7 Background error covariances -
8 Observation error specifications -
9 Errors. A posteriori diagnostics -
10 Error dynamics in ensemble Kalman-filter systems -
11 Short-range error statistics in an ensemble Kalman filter -
12 Error dynamics in ensemble Kalman filter systems -
13 Particle filters for the geosciences -
14 Second-order methods for error propagation in variational data assimilation -
15 Adjoints by automatic differentiation -
16 Assimilation of images -
17 Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation -
18 Selected topics in multiscale data assimilation -
19 Data assimilation in meteorology -
20 An introduction to inverse modelling and parameter estimation for atmosphere and ocean sciences -
21 Greenhouse gas flux inversion -
22 Data assimilation in atmospheric chemistry and air quality -
23 Combining models and data in large-scale oceanography -
24 Data assimilation in coastal oceanography -
25 Data assimilation in glaciology
Error dynamics in ensemble Kalman-filter systems
Error dynamics in ensemble Kalman-filter systems
localization
- Chapter:
- (p.255) 10 Error dynamics in ensemble Kalman-filter systems
- Source:
- Advanced Data Assimilation for Geosciences
- Author(s):
P. Houtekamer
- Publisher:
- Oxford University Press
In this chapter, an experimental environment built around the Lorenz III toy model is used to demonstrate some points concerning localization. In an ensemble Kalman filter, localization is almost always necessary because of restrictions on the size of the ensembles. In fact, localization is the key technique that makes the ensemble approximation to the Kalman filter computationally feasible. How localization is best applied depends on aspects of the model dynamics and the observational network. A reasonable choice often leads to a substantial improvement in performance. Fortunately, as shown in this chapter, the statistics from the ensemble itself can provide guidance in the selection of a reasonable localization method.
Keywords: localization, ensemble Kalman filter, toy model, model dynamics, observational network
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- Title Pages
- [UNTITLED]
- Previous sessions
- Preface
- List of participants
-
1 4D-VAR: four-dimensional variational assimilation -
2 Four-dimensional variational data assimilation -
3 Introduction to the Kalman filter -
4 Smoothers -
5 Observation influence diagnostic of a data assimilation system -
6 Observation impact on the short-range forecast -
7 Background error covariances -
8 Observation error specifications -
9 Errors. A posteriori diagnostics -
10 Error dynamics in ensemble Kalman-filter systems -
11 Short-range error statistics in an ensemble Kalman filter -
12 Error dynamics in ensemble Kalman filter systems -
13 Particle filters for the geosciences -
14 Second-order methods for error propagation in variational data assimilation -
15 Adjoints by automatic differentiation -
16 Assimilation of images -
17 Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation -
18 Selected topics in multiscale data assimilation -
19 Data assimilation in meteorology -
20 An introduction to inverse modelling and parameter estimation for atmosphere and ocean sciences -
21 Greenhouse gas flux inversion -
22 Data assimilation in atmospheric chemistry and air quality -
23 Combining models and data in large-scale oceanography -
24 Data assimilation in coastal oceanography -
25 Data assimilation in glaciology
