References
- Andersson, E. and Järvinen, H. 1999. Variational quality con-trol. Q. J. R. Meteorol. Soc. 125, 697–722.
- Bergthorsson, P. and Diiiis, B. 1955. Numerical weather map analysis. Tellus 7, 229–240.
- Daley, R. 1991. Atmospheric data analysis. Cambridge University Press, Cambridge, 457 pp.
- Daley, R. and Barker, E. 2001. NAVDAS: Formulation and Diagnostics. Mon. Wea. Rev. 129, 129–883.
- Dennis, J. E., Gay, D. M. and Welsch, R.E. 1981. An adaptive nonlinear least-squares algorithm. ACM Trans. Math. Software, 7, 369–383.
- Dennis, J. E. Jr. and Schnabel, R. B. 1996. Numerical methods for unconstrained optimization and nonlinear equa-tions. SIAM Classics in Applied Mathematics, vol. 16. 378 pp.
- Epstein, E. 1969. Stochastic dynamic prediction. Tellus 21, 739–759.
- Huang, X.-Yu. 2000. Variational analysis using spatial filters. Mon. Wea. Rev. 128, 128–2600.
- Lewis, J. M. and Derber J. C. 1985. The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus 37A, 309–322.
- Le Dimet, F. X. and Talagrand, O. 1986. Variational algorithms for analysis and assimilation of meteorological ob-servations: theoretical aspects. Tellus 38A, 97–110.
- Le Dimet, E X., Navon, I. M. and Descau, D. N. 2002 Second-order information in data assimilation. Mon. Wea. Rev. 130, 629–648.
- Lorenc, A. C. 1986. Analysis methods for numerical weather prediction. Quart. J. R. MeteoroL Soc. 112, 1177–1194.
- Lorenz, E. N. 1960. Maximum simplification of the dynamic equations. Tellus 12, 243–254.
- Nash, G. and Sofer, A. 1996. Linear and nonlinear program-ming. McGraw Hill, New York, 692 pp.
- Press, W., Teukolsky, S., Vetterling, W. and Flannery, B. 1992. Numerical recipes in FORTRAN (art of scientific comput-ing). Cambridge University Press, Cambridge, 963 pp.
- Sasaki, Y. 1958. An objective analysis based on variational methods. J. MeteoroL Soc. Jpn. 36, 77–88.
- Talagrand, O. and Courtier, P. 1987. Variational assimilation of meteorological observations with adjoint voracity equation. I: Theory. Q. J. R. MeteoroL Soc. 113, 113–1328.
- Tarantola, A. 1987. Inverse problem theory: Methods for data fitting and model parameter estimation. Elsevier, Amster-dam, 613 pp.
- Thi´ebaux, H. J. and Pedder, M. A. 1987. Spatial objective analysis. Academic Press, New York, 299 pp.
- Thompson, P. D.1957. Uncertainty of initial state as a factor in the predictability of large scale atmosphere pattern. Tellus 9, 275–295.
- Wang, Z., Navon, I., Le Dimet, F. and Zhou, X. 1992. The second-order adjoint analysis: theory and application. Me-teoroL Atmos. Phys. 50, 50–20.
- Wang, Z., Navon, I., Zhou, X. and LeDimet, F. 1995. A truncated Newton optimization algorithm in meteorological application with analytic Hessian/vector products. Corn-put. Optim. Appl. 4, 4–262.
- Wang, Z., Droegemeier, K., White, L. and Navon, I. 1997. Application of a new adjoint Newton algorithm to the 3D ARPS storm scale model using simulated data. Mon. Wea. Rev. 125, 125–2478.
- Wiin-Nielsen, A. 1991. The birth of numerical weather pre-diction. Tellus 43A, 36–52.