This Course in Climate Time Series and Risk Analysis is specifically tailored to the needs of PhD students and postdocs, who wish to learn about an important combination of disciplines (climate change, time series analysis, and risk analysis), but have had so far not much exposure to in-depth statistical teaching. It will also attract professional researchers, who wish to update their knowledge or to learn new statistical techniques. We assume that participants come from somewhere in the range of climatology, ecology, econometrics, environmental sciences, geosciences, hydrology, meteorology, or physics.
What distinguishes this from our previous courses: it puts more weight on risk analysis of climate extremes. This is reflected by new material from an upcoming book on climate extremes by Manfred Mudelsee.
A significant part of the course part is devoted to training in “Open Sessions”, where we recapitulate concepts, answer basic and advanced questions, do exercises, discuss applications and devote time to in-depth analyses of your own data.
We just assume that do not run away when you see a mathematical formula: all the basics, and the advanced methods as well, you will learn here. You get the required statistical tools and extensive hands-on training to become able to optimally analyse your data and answer the associated questions about the climate. You acquire the theoretical basis for understanding the tools and interpreting the results. You learn to quantify the various sources of uncertainty in data, climate models and statistical estimation.
Climate case studies serve to illustrate the usefulness of the tools: how to make the most of your data by means of statistics—and how to publish it in a thesis or a research paper. Examples include:
- Trend estimation techniques for the quantification of global warming (Mudelsee 2019 Earth Science Reviews 190:310)
- Modelled river runoff and river floods during the past decades and centuries (Mudelsee et al. 2003 Nature 425:166, St. George and Mudelsee 2019 Journal of Flood Risk Management)
- Paleohurricane risk during the past millennium from proxy series (Besonen et al. 2008 Geophysical Research Letters 35:L14705)
The course instructor, Dr. Manfred Mudelsee, trained in physics, geology and statistics, has a long-standing expertise in teaching statistical methods to non-specialists.
What you get. The course consists of lectures and extensive hands-on training in computer tutorials. Data, software, the lecture as PDF, the statistical tools and a printed copy of the textbook (Mudelsee, 2014, Climate Time Series Analysis, 2nd edition, Springer, 454 pp) are included in the fee. You get the link to the course PDF already one week before the start to optimally prepare yourself. During the course, you also get new material from the upcoming book (Mudelsee, 2020, Statistical Analysis of Climate Extremes, Cambridge University Press, in press). After the course, you are offered a one-month period where you may receive support on the software and general statistical advice.
Participants are strongly encouraged to bring their own data for discussion and analysis during the course. The number of participants is limited to eight to allow in-depth individual consultation with the course holder and textbook author, Manfred Mudelsee.
An ice-breaker meal, a course dinner, daily lunch, snacks, coffee and tea are included: high-quality organic and, upon request, also vegan or vegetarian. A shuttle service to/from the railway station and your accomodation in Bad Gandersheim is also covered by the registration fee.
Participants of past courses have praised the intellectual, co-operative atmosphere during the courses, the family-like setting and the nice rural landscape around here: please see their comments below. Heckenbeck is a little village of high ecological awareness, which belongs to the medieval town of Bad Gandersheim.
The new lecture house, without barriers, has opened in summer 2015.
Past participants: country list
Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Chile, Croatia, Czech Republic, Denmark, Finland, France, Germany, Indonesia, Ireland, Italy, Latvia, Nepal, Netherlands, Nigeria, Norway, Poland, Portugal, South Korea, Spain, Switzerland, UK, USA
