Statistical methods for climate science
The course covers the basic tools from statistics and machine learning that are used to analyze weather and climate data, in time series or gridded fields.
Climate may be defined as “the statistics of weather". In this course you will learn the basic concepts of statistics and machine learning, and apply them to atmospheric and oceanographic data. The course covers the statistical analysis of time series, and the analysis of spatially distributed fields by using empirical orthogonal functions (EOFs). It also covers artificial neural networks and the algorithms of supervised and unsupervised learning.
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Course structure
The course covers the following topics:
- Basic concepts of probability and statistics
- Time series analysis
- Statistical significance and hypothesis testing
- Spectral analysis
- Linear regression
- Empirical orthogonal functions and extensions
- Analysis of variance – ANOVA
- Supervised learning (classification)
- Unsupervised learning (clustering algorithms)
- Artificial neural networks
- Applications to weather forecasting
Teaching format
Lectures and computer lab
Course materials
Grading criteria, course literature and other material and correspondence related to the course will be available on the course Athena site at https://athena.itslearning.com once you have registered for the course.
Assessment
Assignment in the form of a written project
Examiner
Here is a link to a list of course coordinators and examiners.
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.You can search for schedules from previous years in TimeEdit, by entering the course code.
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Course literature
Note that the course literature can be changed up to two months before the start of the course.
- Hannachi, A., 2022: Statistical Climatology. Compendium. (Provided by MISU).
- Hannachi A., 2021: Patterns Identification and Data Mining in Weather and Climate, Springer-Verlag, 600pp. ISBN 978-3-030-67072-6. (Provided by MISU)
- von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, Cambridge. (Provided by MISU)
- Shah C. 2022: A Hands-On Introduction to Machine Learning. Cambridge University Press, Cambridge. ISBN: 9781009123303.
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Course reports
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Contact
Study counselor
