References
- Bergot, T., S. Malardel and A. Joly. 1996. Sensitivity and singular vectors calculation in the operational context of FASTEX.In: 7th Conf. on Mesoscale Proc., pp. 366–368. Reading, UK.
- Bouttier, F. 1993. The dynamics of error covariances in a barotropic model. Tellus. 45A, 408–423.
- Budgell, W. 1986. Nonlinear data assimilation for shallow water equations in branched channels. J. Geoph. Res. 91 C9, 10, 633–10, 644.
- Dee, D. P. 1995. On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev. 123, 1128–1145.
- Eady, E. 1949. Long waves and cyclone waves. Tellus, 1, 33–52.
- Farrell, B. F. 1988. Optimal excitation of neutral Rossby waves. J. Atmos. Sci. 45, 163–172.
- Farrell, B. F. 1990. Small error dynamics and the predictability of atmospheric flows. J. Atmos. Sci. 47, 2409–2416.
- Fischer, C. 1997. Estimation d’erreurs de prevision et d’analyse dans un modele semi-geostrophique a l’aide d’un filtre de Kalman. Technical report, Météo-France/ CNRM. Note de centre GMME no. 48.
- Fischer, C. 1998. Linear amplification and error growth in the 2D Eady problem with uniform potential vorti-city. J. Atmos. Sci., in press.
- Gandin, L. 1963. Objective analysis of meteorological fields. Israel program for scientific translations, 242 pp., translated from Russian, 1965 edition.
- Gauthier, P., P. Courtier and P. Moll, 1993. Assimilation of simulated wind lidar data with a Kalman filter. Mon. Wea. Rev. 121, 1803–1820.
- Ghil, M. 1989. Meteorological data assimilation for oceanographers. Part I: description and theoretical framework. Dyn. Atmos. Oc. 13, 171–218.
- Ghil, M., S. Cohn, T. J. K. Bube, and E. Isaacson. 1981. Applications of estimation theory to numerical weather predictions. In: Dynamic meteorology: data assimilation methods, L. Bengtsson, M. Ghil, E. Kallen (eds.), New York: Springer Verlag, pp. 139–224.
- Hoskins, B. 1975. The geostrophic momentum approxi-mation and the semi-geostrophic equations. J. Atmos. Sci. 32, 233–242.
- Hoskins, B. J. and F. P. Bretherton, 1972. Atmospheric frontogenesis models: mathematical formulation and solution. J. Atmos. Sci. 29, 11–36.
- Houtekamer, P. 1995. The construction of optimal perturbations. Mon. Wea. Rev. 123, 2888–2898.
- Joly, A. 1995. The stability of steady fronts and the adjoint method: nonmodal frontal waves. J. Atmos. Sci. 52, 3081–3108.
- Joly, A. and A. J. Thorpe. 1991. The stability of time-dependent flows: an application to fronts in developing baroclinic waves. J. Atmos. Sci. 48, 163–182.
- Kalman, R. 1960. A new approach to linear filtering and prediction problems. Trans. ASME 82D, 35–45.
- Kalman, R. and R. Bucy. 1961. New results in linear filtering and prediction theory. Trans. ASME 83D, 95–108.
- Lacarra, J.-F. and O. Talagrand, 1988. Short-range evolution of small perturbations in a barotropic model. Tellus 40A, 81-95.
- Langland, R. H. and G. D. Rohany. 1996. Adjoint-based targeting of observations for FASTEX cyclones.In: Seventh Conf. on Mesoscale Proc., pp. 369–371. Read-ing, U.K.
- Menard, R. and R. Daley, 1996. The application of Kalman smoother theory to the estimation of 4DVAR error statistics. Tellus 48A, 221–237.
- Palmer, T. N., R. Gelaro, J. Barkmeijer and R. Buizza. 1998. Singular vectors, metrics and adaptive observations. J. Atmos. Sc i. 55, 633–653.
- Rabier, F., E. Klinker, P. Courtier and A. Hollingsworth. 1996. Sensitivity of forecast errors to initial conditions. Quart. J. Roy. Meteor. Soc. 122, 121–150.
- Rutherford, I. 1972. Data assimilation by statistical inter-polation of forecast error fields. J. Atmos. Sc i. 29, 809–815.
- Snyder, C. 1996. Summary of an informal workshop on adaptive observations and FASTEX. Bull. Am. Meteor. Soc. 77, 953-961 (meeting summary).