By Seon Ki Park, Liang Xu
This ebook offers the newest achievements in facts assimilation in Geosciences, in particular with reference to meteorology, oceanography and hydrology. It spans either theoretical and utilized facets with a variety of methodologies together with variational, Kalman filter out, greatest probability ensemble clear out and different ensemble tools. along with info assimilation, different very important issues also are lined together with focusing on statement, parameter estimation, and distant sensing information retrieval. The ebook can be beneficial to person researchers in addition to graduate scholars as a reference within the box of knowledge assimilation.
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Extra info for Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications
2005) provided results obtained using the NCEP’ s regional weather prediction system in weak constraint VDA framework. Akella and Navon (2007) studied in depth the nature of modeling errors and suggested a decreasing, constant and increasing in time forms of ME. Implementation of these forms in a weak constraint VDA framework yielded a further reduction in forecast errors. When the number of observations is considerably smaller, the method of representers (Bennett 2002) provides a computationally efficient (in storage/ space requirements) formulation of VDA.
1 Weak Constraint 4D-Var The model can be imposed as a constraint in the cost function, in the same way as other sources of information: Φi (x) = xi − Mi−1 (78) Model error η is defined as: ηi (x) = xi − Mi−1 . The cost function becomes: J(x) = 1 n ∑ (H(xi ) − yi )T R−1 i (H(xi ) − yi ) 2 i=1 (79) 1 1 n + (x0 − xb )T B−1 (x0 − xb ) + ∑ ηiT Q−1 i η 2 2 i=1 Another issue requiring attention is that model error covariance matrix Q has to be defined. e. η = 0 (perfect model). Studies indicate that model error (ME) can severely impact forecast errors, see for instance Boer (1984), Dalcher and Kalnay (1987), Bloom and Shubert (1990) and Zupanski (1993).
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