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Seon Ki Park, Liang Xu's Data Assimilation for Atmospheric, Oceanic and Hydrologic PDF

By Seon Ki Park, Liang Xu

ISBN-10: 3540710558

ISBN-13: 9783540710554

ISBN-10: 3540710566

ISBN-13: 9783540710561

This publication provides the newest achievements in facts assimilation in Geosciences, specially with regard to meteorology, oceanography and hydrology. It spans either theoretical and utilized features with a number of methodologies together with variational, Kalman clear out, greatest probability ensemble clear out and different ensemble tools. along with facts assimilation, different vital themes also are lined together with focusing on statement, parameter estimation, and distant sensing information retrieval. The e-book could be worthwhile to person researchers in addition to graduate scholars as a reference within the box of knowledge assimilation.

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Additional resources for Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications

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In addition, most of the physical processes and their interactions in the atmosphere are parameterized and a complete mathematical modeling of the boundary conditions and forcing terms can never be achieved. Usually all of these modeling drawbacks are collectively addressed by the term model error (ME). The model equations do not represent the system behavior exactly and model errors arise due to lack of resolution as well as inaccuracies occurring in physical parameters, boundary conditions and forcing terms.

US Dept Commerce, NOAA, Maryland pp 89 Rabier F, Liu Z (2003) Variational data assimilation: theory and overview. Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, 8–12 September 2003. European Centre for Medium-Range Weather Forecasts (ECMWF), 29–43. [Avail from ECMWF, Shinfield Park, Reading RG29AX, United Kingdom]. Richardson L (1922) Weather Prediction by Numerical Process. Cambridge Univ. Press (Reprinted by Dover 1965) pp 236 Sasaki Y (1955) A fundamental study of the numerical prediction based on the variational principle.

Errors also occur due to numerical discrete approximations. A way to take these errors into account is to use the weak constraint 4D-Var. M. Navon Here x is the 4D state of the atmosphere over the assimilation window, H is a 4D observation operator, accounting for the time dimension. Φ represents remaining theoretical knowledge after background information has been accounted for (such as balance relations or digital filtering initialization introduced by Lynch and Huang (1992)). One can see that model M verified exactly although it is not perfect.

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Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications by Seon Ki Park, Liang Xu


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