SIO 221B:  Analysis of Physical Oceanographic Data

Winter 2025

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 17, 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 8. Introduction to the course. Motivating a statistical framework. notes
• Friday, January 10. Probability density functions as a foundation. notes
• Wednesday, January 15. Joint probability density functions. notes
• Friday, January 17. Correlation, covariance, and random walks notes
• Wednesday, January 22. Random walks, diffusivity, and sampling error. notes
• Friday, January 24. Autocovariance and decorrelation time scales. notes
• Wednesday, January 29. Models and data: Least-squares fitting. notes
• Friday, January 31. Implementing least-squares fitting: Examples notes
• Wednesday, February 5. Uncertainties in least-squares solutions notes
• Friday, February 7. Weighted and constrained least squares. notes
• Wednesday, February 12. Eigenmodes and modal decomposition notes
• Friday, February 14. Linear algebra review notes
• Wednesday, February 19. Singular value decomposition. notes
• Friday, February 21. More on empirical orthogonal functions and generalized inverse. notes
• Wednesday, February 26. Linear estimation theory. notes
• Friday, February 28. Linear estimation theory applied to the ocean. notes
• Wednesday, March 5. Objective mapping examples for gradients and velocity. notes
• Friday, March 7. Mapping very gappy data: From objective mapping to machine learning. notes, barebones python example
• Wednesday, March 12. Guest lecture, Steve Diggs, University of California Office of the President, Data management for the future
• Friday, March 14. Connecting course themes and thinking about data assimilation notes

Pitfalls people encounter in Matlab