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. Introduction
2. Overview of Linear Algebra
3. Univariate Distribution Theory
4. Multivariate Distribution Theory
5. Introduction to Calculus of Variation
6. Introduction to Control Theory
7. Optimal Control Theory
8. Numerical Solutions to Initial Value Problems
9. Numerical Solutions to Boundary Value Problems
10. Introduction to Semi-Lagrangian Advection Methods
11. Introduction to Finite Element Modeling
12. Numerical Modeling on the Sphere
13. Tangent Linear Modeling and Adjoints
14. Observations
15. Non-variational Sequential Data Assimilation Methods
16. Variational Data Assimilation
17. Subcomponents of Variational Data Assimilation
18. Observation Space Variational Data Assimilation Methods
19. Kalman Filter and Smoother
20. Ensemble-Based Data Assimilation
21. Non-Gaussian Variational Data Assimilation
22. Markov Chain Monte Carlo and Particle Filter Methods
23. Machine Learning Artificial Intelligence with Data Assimilation
24. Applications of Data Assimilation in the Geosciences
25. Solutions to Select Exercise