Data Assimilation for the Geosciences

Steven J. Fletcher is a Research Scientist III at the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University, where he is the lead scientist on the development of non-Gaussian based data assimilation theory for variational, PSAS, and hybrid systems. He has worked extensively with the Naval Research Laboratory in Monterey in development of their data assimilation system, as well as working with the National Atmospheric and Oceanic Administration (NOAA)’s Environmental Prediction Centers (EMC) data assimilation system. Dr. Fletcher is extensively involved with the American Geophysical Union (AGU)’s Fall meeting planning committee, having served on the committee since 2013 as the representative of the Nonlinear Geophysics section. He has also been the lead organizer and science program committee member for the Joint Center for Satellite Data Assimilation Summer Colloquium on Satellite Data Assimilation since 2016. Dr. Fletcher is the author of Data Assimilation for the Geosciences: From Theory to Application (Elsevier, 2017). In 2017 Dr. Fletcher became a fellow of the Royal Meteorological Society.

1. Introduction2. Overview of Linear Algebra3. Univariate Distribution Theory4. Multivariate Distribution Theory5. Introduction to Calculus of Variation6. Introduction to Control Theory7. Optimal Control Theory8. Numerical Solutions to Initial Value Problems9. Numerical Solutions to Boundary Value Problems10. Introduction to Semi-Lagrangian Advection Methods11. Introduction to Finite Element Modeling12. Numerical Modeling on the Sphere13. Tangent Linear Modeling and Adjoints14. Observations15. Non-variational Sequential Data Assimilation Methods16. Variational Data Assimilation17. Subcomponents of Variational Data Assimilation18. Observation Space Variational Data Assimilation Methods19. Kalman Filter and Smoother20. Ensemble-Based Data Assimilation21. Non-Gaussian Variational Data Assimilation22. Markov Chain Monte Carlo and Particle Filter Methods23. Applications of Data Assimilation in the Geosciences24. Solutions to Select Exercise