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Presents a comprehensive treatment of data analysis and assimilation. With practical applications from a range of fields, including climatology, this book introduces the optimal control mathematical framework for the data assimilation concepts of state vector, control space, observations, and departures.
This self-contained book presents interdisciplinary methods for integrating experimental and computational information in many scientific and engineering areas. It 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. 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.
This self-contained book presents interdisciplinary methods for integrating experimental and computational information in many scientific and engineering areas. It 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. 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.