By William Lahoz, Boris Khattatov, Richard Menard
Half A Theory.- 1. information Assimilation and Information.- 2. Mathematical strategies of information Assimilation.- three. Variational Assimilation.- four. Ensemble Kalman clear out: prestige and Potential.- five. mistakes information in info Assimilation: Estimation and Modelling.- 6. Bias Estimation.- 7. the primary of lively Consistency in facts Assimilation.- eight. review of Assimilation Algorithms.- .9 Initialization.- half B Observations.- 10.The worldwide watching System.- eleven. Assimilation of Operational Data.- 12. examine Satellites.- half C Meteorology and Atmospheric Dynmaics.- thirteen. common innovations in Meteorology and Dynamics.- 14. The function of the version within the facts Assimilation System.- 15. Numerical climate Prediction.- half D Atmospheric Chemistry.- sixteen. creation to Atmospheric Chemistry and Constituent Transport.- 17. illustration and Modelling of Uncertainties in Chemistry and delivery Models.- 18. Constituent Assimilation.- 19. Inverse Modelling and mixed State-source Estimation for Chemical Weather.- half E Wider Applications.- 20. Ocean facts Assimilation.- 21. Land floor facts Assimilation.- 22. Assimilation of GPS Soundings in Ionospheric Models.- half F The Longer View.- 23. Reanalysis: facts Assimilation for clinical research of Climate.- 24. watching procedure Simulation Experiments.- 25 information Assimilation for different Planets,- Appendix.- Index
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Efficient new approximation methods based on proper orthogonal decomposition (POD) have also been developed recently for constructing the optimal projection operators (Willcox and Peraire 2002). Other new approaches aim to solve the full non-linear variational problem in a low dimensional subspace spanned by basis functions generated using POD schemes from control theory or other similar methods (see Cao et al. 2007, and references therein). The accuracy and efficiency of these methods depends on how well the dynamics of the system can be captured in the low dimensional space.
Statistical interpolation by means of successive corrections. Tellus, 38A, 439–447. , D. Kubalinska, G. Vossen and D. Wilczek, 2007. h2-norm optimal model reduction for large-scale discrete dynamical MIMO systems, Universitaet Bremen, Zentrum fur Technomathematik, Technical Report 07-04. J. van Leeuwen and G. Evensen, 1998. Analysis scheme in the ensemble Kalman filter. Mon. , 126, 1719–1724. , J. M. Navon and Z. Luo, 2007. A reduced-order approach to four-dimensional variational data assimilatio using proper orthogonal decomposition.
In practice, model errors do not, however, satisfy the statistical assumptions made here. The model error is expected to depend on the model state and hence to be systematic and correlated in time. A more general form of the model error that includes both systematic and random elements is described in the next subsection. 3 Systematic Model Error and State Augmentation The problem of accounting for systematic model errors in a cost-effective way has recently received more attention. Techniques for treating bias errors in the forecast using sequential and four-dimensional variational assimilation schemes (Dee and da Silva 1998; Derber 1989; chapter Bias Estimation, Ménard) and for treating time-correlated stochastic errors (Zupanski 1997) have been investigated.
Data assimilation : making sense of observations by William Lahoz, Boris Khattatov, Richard Menard
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