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Original Articles

Mixed Gaussian-lognormal four-dimensional data assimilation

Pages 266-287 | Received 08 Jul 2009, Accepted 15 Feb 2010, Published online: 15 Dec 2016

References

  • Beaulieu, N. C. and Rajwani, N. 2004. Highly accurate simple closed form approximations to lognormal sum distributions and densities. IEEE Comm. Lett. 9,709–711.
  • Clarke, G. M. and Cooke, D. 2004. A Basic Course in Statistics. Oxford University Press, New York, USA, 734 pp.
  • Cohn, S. E. 1997. An introduction to estimation theory J. Meteor Soc.
  • Courtier, P. and Talagrand, O. 1990. Variational assimilation of meteorological observations with the direct and adjoint shallow-water As with the Gaussian and lognormal constant bias case weequations. Tellus 42A, 531–549.
  • Daley, R. 1992. The effects of serially correlated observation and model errors on atmospheric data assimilation. Mon. Wea. Rev. 164, 120–177.
  • Fletcher, S. J. and Zupanski, M 2006a. A data assimilation method for log-normally distributed observational errors. Quart. J. Roy. Meteor Soc. 132, 2505–2519.
  • Fletcher, S. J. and Zupanski, M 2006b. A hybrid normal and lognormal distribution for data assimilation. Atmos. Sci. Lett. 7, 43–46.
  • Fletcher, S. J. and Zupanski, M 2007. Implications and impacts of trans-forming lognormal variables into normal variables in VAR. Meteorol-ogische Zeitschrift 16, 755–765.
  • Le Dimet, E-X. and Talagrand, O. 1986. Variational algorithm for anal-ysis and assimilation adjustment problem with advective constraints. Tellus 38A, 97–110.
  • Lewis, J. M. and Derber, J. C. 1985. The use of adjoints equations to solve a variational adjustment problem with advective constraints. Tellus 37A, 309–322.
  • Lorene, A. C. 1986. Analysis methods for numerical weather prediction. Q. J. Roy. Meteor. Soc 112, 1177–1194.
  • Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141.
  • Mielke, P. W., Williams, J. S. and Wu, S.-C. 1977. Covariance analysis techniques based upon bivariate lognormal distribution with modifi-cation application. J. Appl. Met. 16, 183–187.
  • Miles, M. L., Verlinde, J. and Clothiaux, E. E. 2000. Cloud droplet size distribution in low-level stratiform clouds. J. Atmos. Sci. 57,295–311.
  • Pearl, J. 2007. Causality, Models, Reasoning and Inference Cambridge University Press., New York, USA., 384 pp.
  • Pedlosky, J. 1987. Geophysical Fluid Dynamics Springer, New York, USA., 710 pp.
  • Polavarapu, S., Ren, S., Rochen, Y., Sankey, D., Ek, N. and co-authors. 2005. Data assimilation with the Canadian middle atmosphere model. Atmos.-Ocean 43 (1), 77–100.
  • Sasaki, Y. 1970 Some basic formalisms in numerical variational analysis. Mon. Wea. Rev., 98, 875–883.
  • Sengupta, M., Clothiaux, E. E. and Ackerman, T. P. 2002 Climatology of warm boundary layers clouds at the ARM SGP site and their comparisons to models. J. Clim., 17, 4760–4782.
  • Talagrand, O. 1988 Four-dimensional variational data assimilation. ECMWF Seminar Proceedings on Data Assimilation and the use of Satellite Data., ECMWF, Reading, U.K. 1–30.
  • Thépaut, J.-N. and Courtier, P., 1991. Four-dimensional variational data assimilation using the adjoint of a multilevel primitive equation model. Quart. J. Roy. Meteor. Soc., 117, 1225–1254.
  • Thépaut, J.-N., Hoffman, R. N. and Courtier, P., 1993. Interactions of dy-namics and observations in four-dimensional variational assimilation. Mon Wea. Rev., 121, 3393–3414.
  • Van Leeuwen, P. J. and Evensen, G. 1996. Data assimilation and inverse methods in terms of a probabilistic formulation. Mon. Wea. Rev., 124, 2898–2913.
  • Zupanlcsi, D. 1997. A general weak constraint applicable to opera-tional 4DVAR data assimilation systems. Mon. Wea. Rev, 125, 2274–2292.