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.
The course covers the following topics:
- Analysis of variance – ANOVA
- Applications to weather forecasting
- Artificial neural networks
- Basic concepts of probability and statistics
- Empirical orthogonal functions and extensions
- Linear regression
- Spectral analysis
- Statistical significance and hypothesis testing
- Supervised learning (classification)
- Time series analysis
- Unsupervised learning (clustering algorithms).
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 once you have registered for the course.
Assessment
Assignment in the form of a written project.
Examiner
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.
Note that the course literature can be changed up to two months before the start of the course.
Course reports are displayed for the three most recent course instances.
