Principles of Data Assimilation

Seon Ki Park is Professor of Meteorology at Ewha Womans University, Seoul, Korea. His research focuses on storm-scale to meso-scale analysis, parameter estimation, and data assimilation to improve numerical weather and climate prediction. He co-edited a series of four volumes titled Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (2009, 2013, 2017, 2021). Milija Zupanski is Senior Research Scientist at Colorado State University, Fort Collins. He is a principal developer of two four-dimensional variational data assimilation systems and the Maximum Likelihood Ensemble Filter. His research focuses on data assimilation development and applications, including the atmosphere, land surface, aerosols, and combustion.

• Part I. General Background: 1. Data assimilation: general background; 2. Probability and Bayesian approach; 3. Filters and smoothers; Part I.: Practical Tools: 4. Tangent linear and adjoint model; 5. Automatic differentiation; 6. Numerical minimization process; Part III. Methods and Issues: 7. Variational data assimilation; 8. Ensemble and hybrid data assimilation; 9. Coupled data assimilation; 10. Dynamics and data assimilation; Part IV. Applications: 11. Sensitivity analysis and adaptive observation; 12. Satellite data assimilation; Index.

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