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ACM41100

Academic Year 2026/2027

Computing for Weather app (ACM41100)

Subject:
Applied & Computational Maths
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 AI and computational approaches for weather forecasting and climate change. Topics change year to year as this field advances rapidly. Students will learn via 2 weekly 1-hour lectures and computer classes.

About this Module

Learning Outcomes:

- A good understanding of computational and visualisation techniques for weather forecasting
- A knowledge of computational and reproducible pipelines for research in AI-based weather forecasting
- An ability to create suitable visualisations and outputs from AI and machine learning models in weather and climate modelling

Indicative Module Content:

Topics change from year to year but may include:
- Computational techniques for fitting AI weather forecasting models
- Git and other tools for reproducible research
- Best practice for high performance computing in AI weather forecasting
- Visualisation tools for machine learning and AI modelling
- Creating publication-ready figures and visulisation best-practice

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 4
 

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 written examination End of trimester
Duration:
2 hr(s)
 Other No
80
 No
Quizzes/Short Exercises: In-class assessments 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.

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