Learning outcomes

After taking the course the student should be able to analyse weather/climate time series for trends, power spectra, probability distributions and relationships between times series vis, e.g. simple and multiple regression, and evaluate significance and hypothesis testing. For weather/climate fields, the student should be able to identify and interpret the modes of variability and/or propagating patterns and their associated explained variance.

Content

• 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

Compulsory elements

Computer lab

Examination

Assignment in the form of a written project

Teaching

Lectures and computer lab

Schedule

Starting directly after summer; start date August 2, 2021.

Literature

•  Hannachi, A., 2021: Statistical Climatology, PhD course.
•  Hannachi A., 2021: Pattern Identification and Data Mining in Weather and Climate. Springer
•  Hannachi et al., 2007: EOFs and related techniques in atmospheric Science. Int. J. Climatol., 27, 1119-1152.
•  V. Storch and F Zwiers, 1999: Statistical Analysis in Climate Research. Springer.

Responsible teacher - contact

Abdel Hannachi (a.hannachi@misu.su.se)