<p>Seminar, three hours; discussion, one hour. Determining causality in earth system data is challenging because of strong coupling between different variables. Study of state-of-art statistical approaches that are designed to infer causality between variables that are strongly coupled on different time scales--for example, ocean-atmospheric coupling and land-vegetation-atmospheric coupling, and for nonlinear coupling. Methods include but not limited to Granger causality, generalized equilibrium feedback assessment, step-wise generalized equilibrium feedback assessment, empirical dynamic modeling, and area weighted connectivity. Offers stimulating group learning experience through reading papers and discussion, and if possible, application of some of methods to earth system data. S/U grading.</p>

Atmospheric and Oceanic Sciences

Combining rigorous, inclusive pedagogy with opportunities for co-curricular activities—including research, internships, and community engagement—the College provides students with diverse learning experiences that helps them thrive, become critical thinkers, and transform society.

College of Letters and Science

The department offers a broad curriculum in dynamic and synoptic meteorology, atmospheric physics and chemistry, and upper atmosphere and space physics.

General Catalog

Machine Learning Approaches for Determining Causality in Coupled Earth System Data

A&O SCI 287

3 units