By William Lahoz, Boris Khattatov, Richard Menard
Data assimilation tools have been mostly built for operational climate forecasting, yet in recent times were utilized to an expanding variety of earth technology disciplines. This ebook will set out the theoretical foundation of information assimilation with contributions by way of best overseas specialists within the box. a number of facets of knowledge assimilation are mentioned together with: idea; observations; versions; numerical climate prediction; assessment of observations and types; evaluation of destiny satellite tv for pc missions; software to parts of the Earth method. References are made to contemporary advancements in facts assimilation thought (e.g. Ensemble Kalman filter), and to novel functions of the knowledge assimilation technique (e.g. ionosphere, Mars info assimilation).
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Q. J. R. Meteorol. , 119, 845–880. , H. Järvinen, E. -F. Mahfouf and A. Simmons, 2000. The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Q. J. R. Meteorol. , 126, 1143–1170. , Y. Trémolet, E. Andersson, L. Isaksen, E. Hólm and M. Janiscova, 2005. Diagnostics of linear and incremental approximations in 4D-Var revisted for highter resolution analysis, ECMWF Technical Memorandum, No. 467, European Centre for Medium-Range Weather Forecasts, Reading, UK.
Similar techniques, which are adjoint free, have been developed for parameter estimation and model calibration (Vermeulen and Heemink 2006). Research in this area is currently active. In summary, four-dimensional variational data assimilation schemes are in operational use at major numerical weather forecasting centres and new theory and new implementation techniques for these schemes continue to be major areas for research. Examples illustrating the use of these schemes on simplified models can be found in Griffith (1997) and Lawless et al.
M. J. Alexander and G. Johnson, 2003. Ensemble filtering for non-linear dynamics. Mon. , 131, 2586–2594. K. , S. K. Nichols, 2005. An investigation of incremental 4D-Var using non-tangent linear models. Q. J. R. Meteorol. , 131, 459–476. S. K. Nichols, 2006. Inner loop stopping criteria for incremental fourdimensional variational data assimilation. Mon. , 134, 3425–3435. K. P. Ballard, 2003. A comparison of two methods for developing the linearization of a shallow water model. Q. J. R. Meteorol.