701-1253-00L  Analysis of Climate and Weather Data

SemesterAutumn Semester 2016
LecturersC. Frei
Periodicityyearly recurring course
Language of instructionEnglish


AbstractObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
ObjectiveObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
ContentIntroduction into the theoretical background and the practical application of methods of data analysis in meteorology and climatology.

Topics: exploratory methods, hypothesis tests, analysis of climate trends, measuring the skill of climate and forecasting models, analysis of extreme events, principal component analysis and maximum covariance analysis.

The lecture also provides an introduction into R, a programming language and graphics tool frequently used for data analysis in meteorology and climatology. During hands-on computer exercises the student will become familiar with the practical application of the methods.
Lecture notesDocumentation and supporting material include:
- documented view graphs used during the lecture
- excercise sets and solutions
- R-packages with software and example datasets for exercise sessions

All material is made available via the lecture web-page.
LiteratureSuggested literature:
- Wilks D.S., 2005: Statistical Methods in the Atmospheric Science. (2nd edition). International Geophysical Series, Academic Press Inc. (London)
- Coles S., 2001: An introduction to statistical modeling of extreme values. Springer, London. 208 pp.
Prerequisites / NoticePrerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren.