Stochastic methods for sequential data assimilation in strongly nonlinear systems

Monthly Weather Review

Volumn 129, Issue 5, 2001, Pages 1194-1207

  Pham, D T ^a  

^a GRENOBLE INSTITUTE OF TECHNOLOGY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COSTS; DATA ACQUISITION; FILTRATION; MONTE CARLO METHODS; RANDOM PROCESSES;

SELECTIVE ERROR COLLECTION;

NONLINEAR SYSTEMS;

DATA ASSIMILATION; KALMAN FILTER; NONLINEARITY; STOCHASTICITY;

EID: 0035335074     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2     Document Type: Article

References (24)

1. Analysis scheme in the ensemble Kalman filter
2. Résolution particulaire et traitement non-linéaire du signal: Application radar/ sonar
3. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics
4. Advanced data assimilation in strongly nonlinear dynamics
5. Application of ensemble integration for predictability studies and data assimilation
6. Assimilation of geostat altimeter data for the Agulhas Current using the ensemble Kalman filter with a quasigeostrophic model
7. An ensemble Kalman smoother for nonlinear dynamics
8. A week contraint variational inverse for the Lorenz equation using subtitution methods
9. Assimilation of simulated wind lidar data with a Kalman filter
10. Data assimilation in meteorology and oceanography
11. Unified notation for data assimilation: Operational, sequential and variational
12. Stochastic and filtering theory
13. Approximation to optimal nonlinear filters
14. Deterministic non-periodic flows
15. Advanced data assimilation in strongly nonlinear dynamical systems
16. Data assimilation into non-linear stochastics models
17. Dimension, predictability and reduced rank Kalman filtering in data assimilation
18. Filtres de Kalman singuliers évolutif pour l' assimilation de données en océnographie
19. A singular evolutive extended Kalman filter for data assimilation in oceanography
20. Data assimilation and inverse methods in terms of a probabilistic formulation