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Statistical Learning for Atmospheric and Climate Science
Last Updated: 2026-02-05 16:02:25
Abstract
The course will consist of overview lectures, hands-on practical exercises on (1) the basics of statistical learning and (2) with a focus on applications for atmospheric and climate science. Lectures will cover theoretical basics of statistical learning (advanced regression, nonlinear methods) and an overview of applications of statistical learning in the atmospheric and climate sciences.
Objective
- Understanding elements and principals of statistical learning - Ability to select the appropriate statistical learning tools to tackle atmospheric and climate research problems - Ability to apply methods of statistical learning to atmospheric and climate research
Content
- Data in atmospheric and climate research (data types, observations, models) - Exploring properties of atmospheric and climate data (data in space and time, multivariate data) - Concepts of supervised learning (bias variance trade-off, overfitting, cross-validation) - Advanced linear regression (multiple linear regression, regularization) - Non-linear regression (tree based methods, neural networks) - Un-supervised learning (dimension reduction, clustering) - High-level applications of statistical learning for atmospheric and climate research (keynote speakers)
Resources
Literature
Hastie, T., Tibshirani, R. & Friedman, J. (2009). The elements of statistical learning (Ed. 2). New York: Springer series in statistics. (Link to book: https://web.stanford.edu/~hastie/Papers/ESLII.pdf , book homepage: http://web.stanford.edu/~hastie/ElemStatLearn/ ) James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: springer. (Link to book: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf , book homepage (exercises, etc.): http://www-bcf.usc.edu/~gareth/ISL/ )
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 30
- Signup Start
- 19.09.2022
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Statistical Learning for Atmospheric and Climate Science |
|
2 h weekly |