Fundamentals of Ocean/Atmosphere Data Analysis

June 3, 2019 – June 7, 2019 all-day
Forskningsparken, Oslo

Responsible: Joe LaCasce / UiO
International lecturer: Jonathan Lilly / Theiss Research
Max. no. of participants: 12 CHESS students (total participants 24)
Credit points: 3 ECTS
Registration form here. (registration closed)
Submitted applicant list

Course description: This course introduces students to essential tools for analyzing any type of dataset from oceanography, atmospheric science, or climate.  The centerpiece, called “distributional data analysis”, is a simple yet powerful method for delving into a potentially large, multivariate dataset by examining its statistics in two-dimensional slices.  Elementary statistics for univariate or bivariate (e.g. velocity) datasets, simple filtering, data organization and manipulation techniques in Matlab, and data visualization strategies are all addressed.  The course also provides innovative training in the mental factors of curiosity, imagination, and objectivity that are essential for observationalists. Students apply techniques to their own datasets, and learn further through homework problems and group exercises.

This will be the third time the course is offered.  By popular demand, this iteration focuses on a greatly expanded version of the “low-tech” methods that form the foundation of the data analyst’s toolbox.

Structure: Lectures will be given in the mornings and lab sessions in the afternoons, allowing the students to apply the methods directly to data at once. In addition to a physical classroom in Oslo, lectures will also be available via video link. Students are expected to bring a dataset of any type that they would like to analyze for a course project. Multivariate datasets are encouraged. Model output is also acceptable. The students employ the statistical and time series analysis toolbox jLab developed by the instructor (  Course notes are available online at (specifically chapters 1-8).

Outcome: At the end of the course, students will be well-prepared to begin efficiently analyzing any dataset they might encounter, while avoiding common pitfalls.  Students gain practical experience through hands-on demonstrations and exercises in Matlab.