Description of the course

The course covers basic statistical tools that are used to analyze weather and/or climate data, in time series or gridded fields. After taking the course the student should be able to analyse weather/climate time series for trends, power spectra, probability distributions and potential relationships between different times series vis, e.g. simple and multiple regression, and to evaluate significance and perform   hypothesis testing. For gridded fields, the student should be able to identify and interpret the modes of variability and/or propagating patterns and their associated explained variance.

Topics covered are:

  • Basic concepts of probability and statistics in weather and climate,
  • Stationary time series
  • Statistical significance and hypothesis testing
  • Spectral analysis
  • Regression analysis
  • Empirical orthogonal functions and extensions
  • Analysis of variance – ANOVA
  • Extreme value analysis and MCMC estimation

How is the course organized?

The course is based on an intensive 100%-pace series of lectures over a 2-3 weeks, plus a compulsory computer lab.


Submission of a written report on an assigned project.


Starting directly after summer; start date TBD

Course Literature

V. Storch and F Zwiers, 1999: Statistical Analysis in Climate Research. Springer.
Hannachi et al., 2007: EOFs and related techniques in atmospheric Science. Int. J. Climatol., 27, 1119-1152.
Hannachi, A., 2020: Statistical Climatology, PhD course.

Responsible teacher - contact

Abdel Hannachi