Conjugate gradients versus multigrid solvers for diffusion‐based correlation models in data assimilation
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[1] William L. Briggs,et al. A multigrid tutorial, Second Edition , 2000 .
[2] S. McCormick,et al. A multigrid tutorial (2nd ed.) , 2000 .
[3] A. Weaver,et al. Representation of correlation functions in variational assimilation using an implicit diffusion operator , 2010 .
[4] H. Ngodock,et al. Background-error correlation model based on the implicit solution of a diffusion equation , 2010 .
[5] M. Hestenes,et al. Methods of conjugate gradients for solving linear systems , 1952 .
[6] F. L. Dimet,et al. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .
[7] P. Courtier,et al. Correlation modelling on the sphere using a generalized diffusion equation , 2001 .
[8] John Derber,et al. A Global Oceanic Data Assimilation System , 1989 .
[9] Gene H. Golub,et al. Matrix computations (3rd ed.) , 1996 .
[10] John E. Dennis,et al. Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.
[11] P. Courtier,et al. A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .
[12] Gene H. Golub,et al. Matrix computations , 1983 .
[13] R. Bannister. A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics , 2008 .
[14] Carl D. Meyer,et al. Matrix Analysis and Applied Linear Algebra , 2000 .
[15] Serge Gratton,et al. Range-Space Variants and Inexact Matrix-Vector Products in Krylov Solvers for Linear Systems Arising from Inverse Problems , 2011, SIAM J. Matrix Anal. Appl..