47th online course in climate time series analysis

Deadline for registration

24 November 2023

About

This Online Course in Climate Time Series Analysis is specifically tailored to the needs of PhD students and postdocs, who wish to learn about an important combination of disciplines (climate change and time series 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.

We aim for easy accessibility also for those students who are early in their career. The online version emerged in response partly to the Covid-19 situation (which started in 2020), but also to the general upward trend in need of electronic high-level quality education.

What distinguishes this from other online courses? First, the course provides videos that have been designed, recorded and edited with care. You can go repeatedly through the videos and make breaks as you need. You receive and can study again the delivered course slides. Second, daily chat meetings via a video platform over the full course duration allow you to prepare questions beforehand and get extensive response. Third, own-developed software, specifically designed to get the most out of "dirty" climate time series data, will enhance your arsenal of analytical tools. Fourth, the individual feedback period of two months post-course (via email and, possibly, online meetings) preserves the interactive mode of joint data analysis, it allows to go in depth through real applications — perhaps on 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. Access to the course videos is provided through a streaming host. Data, software, the lecture as PDF, additional reading material (articles as PDF), the statistical tools and (optionally) an e-book version of the textbook (Mudelsee, 2014, Climate Time Series Analysis, 2nd edition, Springer, 454 pp) are included in the fee. You receive the link to the course slides already several days before the start to optimally prepare yourself. During the course days, you can participate daily in an online chat on the material delivered on that day. After the course, you are offered an individual two-month feedback period where you have the chance to shown (more) own data, tell me about the questions (the data are asking), receive support on the software and general statistical advice. We communicate by email during this period and, if needed, via one-to-one online meetings.

Participants are strongly encouraged to bring their own data for discussion and analysis during the course. The number of participants is limited to twenty to allow in-depth individual consultation with the course holder and textbook author, Manfred Mudelsee.

You want to have a free look? Please try Module 01 Introduction (Lecture), which is in the public domain.

Registration

• Twenty participants maximum: first come, first serve
• Deadline for registration: 24 November 2023
• Information requested: name, email, academic title(s), affiliation(s) and professional status(es), research field(s) and computer operating system(s) please also indicate whether you wish own data to be analysed during the course or during the post-course feedback period; finally, please attach a short CV including list of recent papers, abstracts and links since this helps to better prepare for your needs — please put all this into the registration form (PDF)
• Send the information (i.e., the completed registration form) by email to the course email address [email protected]
• After receiving your registration email, an official payment request is sent to you electronically, which you kindly let your institution pay; this payment completes the registration. It is preferable to us if you pay via credit card. Afterwards, the course invoice will be sent electronically to the paying institution. The aim is to have the payment done before course start.