Titlebook: Data Assimilation for the Earth System; Richard Swinbank,Victor Shutyaev,William Albert La Conference proceedings 2003 Springer Science+Bu

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https://doi.org/10.1007/978-981-19-0221-5points, but rather averaged values related to some not fully certain weight functions. The third reason is that, actually, the operator of the inverse remote sensing problem does not maintain some constant magnitudes in spatial and temporal coordinates, but, really, it is subjected by disturbances o

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Data Assimilation: Aims and Basic Concepts,accurate forecasts of future dynamical behaviour can be determined. Complete information defining all of the states of the system at a specified time are, however, rarely available. Moreover, both the models and the measured data contain inaccuracies and random noise. In this case, observations of t

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Fundamental Control Functions and Error Analysis,y (.) to the Hilbert space ., . ∈ ., . is a nonlinear operator mapping . into .. Let . = ..(0, ., .), ‖ · ‖. = (·,·)., . ∈ .. Let us introduce the functional . where α = const ≥ 0, .. ∈ ., .. are prescribed functions (observational data), .. is a Hilbert space (observational space), . : . → .. a lin

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, Validation of Assimilation Algorithms, the chapter .. (which will hereafter be referred to as Part I). We recall eq. (4.1) of that chapter, which links the data vector ., belonging to data space ., with dimension ., to the state vector . to be determined, belonging to state space ., with dimension .