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STAT41150

Academic Year 2026/2027

Extremes for Climate (STAT41150)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Professor Andrew Parnell
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module covers the use of Extreme Value Theory (EVT) with climate and weather examples. Students are introduced to the fundamentals and theory of EVT in weekly lectures, reinforced with computer classes and in-class assessments.

About this Module

Learning Outcomes:

- A good understanding of EVT probability distributions and how to use them with weather and climate data.
- A knowledge and appreciation of different modelling approaches and when each would be appropriate.
- A good understanding of computer software used for fitting EVT models.
- An understanding of the state of the art in extreme value modelling in climate and weather science.

Indicative Module Content:

The full list of topics changes from year to year but may include:
- The Generalised Extreme Value Distribution
- The Generalised Pareto Distribution
- Including covariates in EVT models
- Spatial EVT models
- Copulas and multivariate extremes
- Bayesian extreme value modelling using Stan
- Use of AI and generative models in Extremes

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

Take urgent action to combat climate change and its impacts 1
 

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

20
Autonomous Student Learning

50
Lectures

24
Computer Aided Lab

20
Total

114

Approaches to Teaching and Learning:
Weekly lectures, weekly computer labs, in-class assignments.

Requirements, Exclusions and Recommendations
Learning Recommendations:

Students should have a foundational understanding of linear algebra and calculus, with modules taken at undergraduate level in each of these subjects. Familiarity with basic statistical modelling concepts such as linear regression and manipulating probability distributions is expected. Some familiarity with Python programming environments such as VS Code or Google Colab is recommended.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Exam (In-person): 2 hour end of trimester exam End of trimester
Duration:
2 hr(s)
 Other No
80
 No
Quizzes/Short Exercises: Weekly in-class assignments Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Other No
20
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 13:00 - 13:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 Tues 12:00 - 12:50
Spring Computer Aided Lab Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 Wed 13:00 - 13:50
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