Course description
This is a 4-day intensive course on statistical modelling concepts for climate scientists. Since it is impossible in such a short course to go into any great depth, this course aims instead to convey the fundamental modelling concepts in statistics and an understanding and ability of how to use them correctly to interpret climate data. The course will consist of eight lectures interspersed with multiple hands-on computer sessions.
Learning Outcomes
- Deeper appreciation of statistical modelling
- Awareness of some relevant areas of advanced statistics
- Ability to apply methods intelligently using the R statistical language
Registration
There is only a few places left. Registration is done as soon as possible through the CHESS web pages.
Costs
The course costs will be covered for DEEP members. This includes travel and living costs if you have to travel to participate.
Programme
Tuesday 22 Aug: Introduction
- 10:00-11:00 1. Exploratory Data Analysis (Prof David Stephenson)
- 11:00-11:15 Coffee break
- 11:15-12:15 Interactive R session
- 12:15-13:15 Lunch
- 13:15-14:15 2. Probability (Dr Stefan Siegert)
- 14:15-15:30 Interactive R session
Wednesday 23 Aug: Statistical Modelling
- 10:00-11:00 3. Statistical Modelling (Prof David Stephenson)
- 11:00-11:15 Coffee break
- 11:15-12:15 Interactive R session
- 12:15-13:15 Lunch
- 13:15-14:15 4. Statistical Inference (Dr Stefan Siegert)
- 14:15-15:30 Interactive R session/Seminar on uncertainty in NAO predictability
Thursday 24 Aug: Multivariate and Spatial Statistics
- 10:00-11:00 5. Multivariate Statistics (Prof David Stephenson)
- 11:00-11:15 Coffee break
- 11:15-12:15 Interactive R session
- 12:15-13:15 Lunch
- 13.15-14:15 6. Spatial Statistics (Dr Stefan Siegert)
- 14:15-15:30 Interactive R session
Friday 25 Aug: Modelling Temporal and Extremal Processes
- 10:00-11:00 7. Time series modelling (Dr Stefan Siegert)
- 11:00-11:15 Coffee break
- 11:15-12:15 Interactive R session
- 12:15-13:15 Lunch
- 13:15-14:15 8. Extreme value modelling (Prof David Stephenson)
- 14:15-15:30 Interactive R session and Final Discussion
*All lectures will be at BCCR lecture room 4060 and all practical sessions at G-Lab.*
Useful references
- R Language (free download, documentation, etc.) www.r-project.org
- Lecture notes on basic statistical concepts: http://secamlocal.ex.ac.uk/people/staff/dbs202/cag/courses/MT37C/course-d.pdf
- Daniel S. Wilks, Statistical Methods in the Atmospheric Sciences, 3rd edition.
- Faraway, J. (2004) Linear Models with R, Chapman and Hall/CRC, 240pp.
- Draper NR, Smith H. Applied Regression Analysis (3rd edition). New York: Wiley 1998.
- A.C. Davison, Statistical Models, Cambridge University Press 2003.
- M.J. Crawley, Statistics: An Introduction Using R, Wiley and Sons 2005.
- Chatfield, The Analysis of Time-series: An Introduction, CRC press, 6th edition 2003.
- Chatfield, Time series forecasting, Chapman and Hall/CRC press, 2000.
- Bloomfield, Fourier Analysis of Time Series: An Introduction (Wiley Series in Probability & Statistics), 2000.
- Coles, An Introduction to Statistical Modelling of Extreme Values, 224 pages, Springer, 2001