## SIO 221B: Analysis of Physical Oceanographic Data

## Winter 2023

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 20, 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.

Lecture notes and handouts: (See Canvas for slides, since they may contain
copyrighted material.)

- Wednesday, January 11. Introduction to the course. Motivating a statistical framework. notes
- Friday, January 13. Probability density functions as a foundation. notes
- Wednesday, January 18. Joint probability density functions. notes
- Friday, January 20. Conditional probability and correlation. notes
- Wednesday, January 25. Covariance and rotated variance ellipses. notes
- Friday, January 27. Random walk diffusivity and decorrelation. notes
- Wednesday, February 1. Autocorrelations and decorrelation scales. notes
- Friday, February 3. Models and data: Basics of least-squares fitting notes
- Wednesday, February 8. Least-squares fitting examples notes
- Friday, February 10. Weighted and constrained least squares. notes
- Wednesday, February 15. Weighted, constrained least squares examples---working with a prior. notes
- Friday, February 17. Linear algebra refresher: Orthogonality and invertibility. notes
- Wednesday, February 22. Singular value decomposition. notes
- Friday, February 24. Empirical orthogonal functions. notes
- Wednesday, March 1. Generalized matrix inversion and the singular value decomposition. notes
- Friday, March 3. Linear estimation theory. notes + guest lecture by Steve Diggs on data management
- Wednesday, March 8. Guest lecture by Lauren Hoffman on machine learning
- Friday, March 10. Linear estimation theory applied to the ocean. notes
- Wednesday, March 15. Objective mapping examples notes
- Friday, March 17. Connecting course themes and thinking about data assimilation notes