Fundamentals of Ocean/Atmosphere Data Analysis

When: 20-24 June 2022, 10 AM — 3 PM each day
Where: UiO, Oslo
Credits: 2 ECTS
Lecturer: Jonathan Lilly / Planetary Science Institute
Organizer: Joe LaCasce / UiO
Max no. of participants: 24

(The course is open to non-CHESS participants and students from outside of Norway are welcome to attend.  All non-CHESS students would be responsible for their own travel, accommodation, and meals.)

Registration

Registration deadline: 13 May 2022


Course description

This course introduces participants to essential statistical and conceptual tools for analyzing any type of dataset from oceanography, atmospheric science, or climate.
Participants will learn how to use creativity together with simple statistical tools to delve into datasets, uncovering whatever information they may contain, and how to shape that information into stories. In particular, a powerful method called “distributional data analysis” allows us to deconstruct potentially large, multivariate datasets by examining their statistics in two-dimensional slices. Careful attention is given to the variance ellipse, the fundamental second-order statistical quantity for bivariate data such as velocity. Data organization and manipulation techniques, visualization strategies, and healthy coding habits are all also addressed. Finally, the course provides innovative training in the essential mental factors of curiosity, imagination, and objectivity.
Participants will apply these techniques to a dataset of their own choosing using either Matlab or Python, and will learn further through homework problems and group exercises.
This will be the sixth time a version of this course is offered in Oslo, and the third time focusing on the “low-tech” methods that form the foundation of the data analyst’s toolbox.

Please be advised that the course will be a full-time activity, as there will also be homework and other activities outside of class time. Participants should plan to clear their schedules during that time to the extent possible. Planning to keep up with other major responsibilities during the course time (e.g. research deadlines or teaching) is not realistic.

Course notes, lab exercises, and associated code are all available online at http://www.jmlilly.net/course/index.html.