MTMW12-Introduction to Numerical Modelling

Module Provider: Meteorology
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites: A-level mathematics and modules in mathematics in undergraduate degree.
Co-requisites:
Modules excluded:
Current from: 2020/1

Email: h.weller@reading.ac.uk

Type of module:

Summary module description:
We will derive and analyse a number of numerical methods for solving the type of equations used in atmosphere and ocean modelling. Students will implement some of these methods using the Python programming language.

Aims:

The aim of this module is to familiarise the students with a range of concepts and techniques used in the numerical modelling of atmospheric and oceanic fluid flows.  This will include mathematical analysis, modelling and some good programming practices.

Assessable learning outcomes:

By the end of this module students should be able  to:

• Derive finite difference approximations using Taylor series;


• Explain the concept of stability and perform a basic stability analysis; 


• Implement and test the behaviour of numerical schemes using  Python;


• Recognise sources of numerical error and derive and measure order of accuracy; Use Fourier series for analysing both numerical methods an d climate  data;


• Use functions and loops in Python and avoid code duplication; 


• Describe various properties of numerical methods such as conservation and boundedness;


• Collaborate on writing code in groups;


• Design experiments to test the properties of numerical methods.

Additional outcomes:

Students will develop skills of working to deadlines and preparing clear, concise written reports.

Outline content:

The lecture content covers:

• Derive finite difference approximations using Taylor series;


• Differential equations with time and space derivatives;


• Techniques for solving the diffusion equation and the advection equation;


• Use of Fourier series:


• Python including use of functions and testing:

The practical classes cover:

• Introduction to Python;


• Implementation of numerical schemes and demonstration of their behaviour.

Brief description of teaching and learning methods:

Lectures, computing practical classes, written reports on practicals and peer instruction.  A list of background reading is supplied with the lecture notes.

Summative assessment- Examinations:
1 hour 50 minute class test at the end of the module during the Autumn term. Answer all 4 questions.

Summative assessment- Coursework and in-class tests:
Written exam worth 40%. 55% is made up of 2 assignments involving programming and report writing worth 20% and 35%. The 35% assignment will involve team work.

Formative assessment methods:
Students receive 5% of the final module total for participating in a peer assessed assignment.

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

A mark of 50% overall.

Reassessment arrangements:
For candidates who have failed, an opportunity to take a resit examination will be provided within the lifetime of the course.

Additional Costs (specified where applicable):

1) Required text books: 2) Specialist equipment or materials: 3) Specialist clothing, footwear or headgear: 4) Printing and binding: 5) Computers and devices with a particular specification: 6) Travel, accommodation and subsistence:

Last updated: 4 April 2020