701-1253-00L Analysis of Climate and Weather Data
| Semester | Autumn Semester 2021 |
| Lecturers | C. Frei |
| Periodicity | yearly recurring course |
| Course | Does not take place this semester. |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |
|---|---|---|---|---|
| 701-1253-00 G | Analysis of Climate and Weather Data Does not take place this semester. | 2 hrs | C. Frei |
Catalogue data
| Abstract | An introduction into methods of statistical data analysis in meteorology and climatology. Applications of hypothesis testing, extreme value analysis, evaluation of deterministic and probabilistic predictions, principal component analysis. Participants understand the theoretical concepts and purpose of methods, can apply them independently and know how to interpret results professionally. |
| Learning objective | Students understand the theoretical foundations and probabilistic concepts of advanced analysis tools in meteorology and climatology. They can conduct such analyses independently, and they develop an attitude of scrutiny and an awareness of uncertainty when interpreting results. Participants improve skills in understanding technical literature that uses modern statistical data analyses. |
| Content | The course introduces several advanced methods of statistical data analysis frequently used in meteorology and climatology. It introduces the thoretical background of the methods, illustrates their application with example datasets, and discusses complications from assumptions and uncertainties. Generally, the course shall empower students to conduct data analysis thoughtfully and to interprete results critically. Topics covered: exploratory methods, hypothesis testing, analysis of climate trends, measuring the skill of deterministic and probabilistic predictions, analysis of extremes, principal component analysis and maximum covariance analysis. The course is divided into lectures and computer workshops. Hands-on experimentation with example data shall encourage students in the practical application of methods and train professional interpretation of results. R (a free software environment for statistical computing) will be used during the workshop. A short introduction into R will be provided during the course. |
| Lecture notes | Documentation and supporting material: - slides used during the lecture - excercise sets and solutions - R-packages with software and example datasets for workshop sessions All material is made available via the lecture web-page. |
| Literature | For complementary reading: - Wilks D.S., 2011: Statistical Methods in the Atmospheric Science. (3rd edition). Academic Press Inc., Elsevier LTD (Oxford) - Coles S., 2001: An introduction to statistical modeling of extreme values. Springer, London. 208 pp. |
| Prerequisites / Notice | Prerequisites: Basics in exploratory data analysis, probability calculus and statistics (incl linear regression) (e.g. Mathematik IV: Statistik (401-0624-00L) and Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften (701-0105-00L)). Some experience in programming (ideally in R). Some elementary background in atmospheric physics and climatology. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 3 credits |
| Examiners | C. Frei |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
| Mode of examination | written 90 minutes |
| Written aids | handwritten summary, maximum 4 pages (2 sheets) |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| Main link | Analysis of Climate and Weather Data |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
