- 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
Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation
Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation
- Chapter:
- (p.395) 17 Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation
- Source:
- Advanced Data Assimilation for Geosciences
- Author(s):
L. Debreu
E. Neveu
E. Simon
F.-X. Le Dimet
- Publisher:
- Oxford University Press
This chapter looks at the use of multigrid methods and local mesh refinement algorithms in the context of the variational data assimilation method. Firstly, the chapter looks back at basic properties of the traditional variational data assimilation method and considers on the role of the background error covariance matrix. The next section shows how multigrid algorithms can efficiently solve the resulting system. Then the chapter deals with local mesh refinements and the final part of the chapter gives some ideas on how to couple the two approaches in the view of local multigrid algorithms.
Keywords: multigrid methods, mesh refinement, variational data, data assimilation method, background error covariance matrix
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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
