Data evaluation and data combination require the use of a wide
range of probability theory concepts and tools, from deductive
statistics mainly concerning frequencies and sample tallies to
inductive inference for assimilating non-frequency data and a
priori knowledge. Computational Methods for Data Evaluation and
Assimilation presents interdisciplinary methods for integrating
experimental and computational information. This self-contained
book shows how the methods can be applied in many scientific and
engineering areas. After presenting the fundamentals underlying the
evaluation of experimental data, the book explains how to estimate
covariances and confidence intervals from experimental data. It
then describes algorithms for both unconstrained and constrained
minimization of large-scale systems, such as time-dependent
variational data assimilation in weather prediction and similar
applications in the geophysical sciences. The book also discusses
several basic principles of four-dimensional variational
assimilation (4D VAR) and highlights specific difficulties in
applying 4D VAR to large-scale operational numerical weather
prediction models.
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