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ACM40900

Academic Year 2023/2024

Weather & Clim Num Modelling (ACM40900)

Subject:
Applied & Computational Maths
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Conor Sweeney
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 will study the processes that act in the Earth's atmosphere and drive our weather and climate, and explain how computer models are used to model the weather and climate.

We will consider the laws that govern the atmosphere, using some fundamental ideas and relationships from thermodynamics. We will also study the processes that control climate and how it evolves. We will examine the global energy balance, and study the interactions between the atmosphere, the ocean and the land. The forcing and feedback mechanisms involved in global climate change will be investigated.

We will consider how these physical process are represented in modern computer models. We will use the WRF Numerical Weather Prediction (NWP) model to run our own weather forecasts on a computer server, and use python to analyse and visualise the forecast data.

Students should have successfully completed a module in Partial Differential Equations, and know how to code in python.

About this Module

Learning Outcomes:

On successful completion of this module, students will be able to:
- explain the physical processes which drive the weather and climate
- describe the way computer models simulate weather and climate
- create weather forecasts using an NWP model on a compute cluster
- analyse and visualise weather and climate data using python

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Computer Aided Lab

12
Autonomous Student Learning

70
Total

106

Approaches to Teaching and Learning:
Lectures
Computer lab work
Active/task-based learning

Requirements, Exclusions and Recommendations
Learning Requirements:

Students should be familiar with Partial Differential Equations, and have successfully completed a module equivalent to ACM20150 Vector Integral & Differential Calculus.

Student should be able to code in Python, and have successfully completed a module equivalent to ACM20030 Computational Science.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Class Test: Two in-class written tests Varies over the Trimester n/a Standard conversion grade scale 40% No
50
 No
Continuous Assessment: Online quizzes Varies over the Trimester n/a Standard conversion grade scale 40% No
30
 No
Project: Individual project Varies over the Trimester n/a Standard conversion grade scale 40% No
20
 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

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