SIO 221B: Analysis of Physical Oceanographic Data
Winter 2024
SIOC 221B covers techniques for analysis of physical oceanographic data involving many simultaneous processes, including probability densities, sampling errors, spectral analysis, empirical orthogonal functions, correlation, linear estimation, objective mapping.
It is the second course in a series. Students who have not taken the first
course may want to look at the brief synopsis of key background that you should have at this web site.
Sarah Gille
Lectures: Wednesday/Friday 2:00-3:20, Spiess Hall 330
Problem session: Monday 2:00-2:50, Spiess Hall 330
SIO Office: Nierenberg Hall 348
Telephone: 822-4425 e-mail: sgille at ucsd.edu
Course website: http://pordlabs.ucsd.edu/sgille/sioc221b
Grading: S/U, based on homework and independent project. (See syllabus)
Final presentations: Monday, March 18, 3-6 pm
syllabus in pdf form
Reference List
This is the public website for the course. More information is available on the UCSD Canvas site for the course. When possible, schedule and topics will be posted here. Notes and topics for upcoming lectures are tentative and subject to extensive revision.
Lecture notes and handouts: (See Canvas for slides, since they may contain
copyrighted material.)
- Wednesday, January 10. Introduction to the course. Motivating a statistical framework. notes
- Friday, January 12. Probability density functions as a foundation. notes
- Wednesday, January 17. Joint probability density functions. notes
- Friday, January 19. Correlation, covariance, and random walks notes
- Wednesday, January 24. Random walks, diffusivity, and sampling error. notes
- Friday, January 26. Autocovariance and decorrelation time scales. notes
- Wednesday, January 31. Models and data: Least-squares fitting. notes
- Friday, February 2. Implementing least-squares fitting: Examples notes
- Wednesday, February 7. Uncertainties in least-squares solutions notes
- Friday, February 9. Weighted and constrained least squares. notes
- Wednesday, February 14. Eigenmodes and modal decomposition notes
- Friday, February 16. Singular value decomposition. notes
- Wednesday, February 21. Ocean Sciences Meeting: recorded lecture Linear algebra refresher: Orthogonality and invertibility. notes
- Friday, February 23. Ocean Sciences Meeting: recorded lecture More on empirical orthogonal functions and generalized inverse. notes
- Wednesday, February 28. Linear estimation theory. notes
- Friday, March 1. Linear estimation theory applied to the ocean. notes
- Wednesday, March 6. Objective mapping examples for gradients and velocity. notes
- Friday, March 8. Mapping very gappy data: From objective mapping to machine learning. notes, barebones python example
- Wednesday, March 13. Guest lecture, Steve Diggs, University of California Office of the President, Data management for the future
- Friday, March 15. Connecting course themes and thinking about data assimilation notes
Pitfalls people encounter in Matlab