MT24CNU-Numerical Methods for Environmental Science

Module Provider: Meteorology
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Prof Paul Williams
Email: p.d.williams@reading.ac.uk

Type of module:

Summary module description:

A module based around computer practicals and lectures, introducing students to numerical algorithms for solving the equations relevant to environmental science.

The module lead at NUIST is Dr Chaman Gul (Chaman@nuist.edu.cn).

Aims:

To introduce students to the computational techniques needed to solve physical problems arising in environmental science and to develop the programming skills necessary to implement the techniques.

Assessable learning outcomes:

By the end of the module, the student should be able to: Develop numerical algorithms for solving basic differential equations commonly encountered in environmental science problems and implement them as computer programs. Use numerical analysis to evaluate the results produced by the programs and design ways to improve them. Relate the computational solutions to their anticipated nature from theory, including perturbation growth and propagation, and chaotic behaviour.

Additional outcomes:

Students will enhance their problem-solving skills, general IT skills, the production of scientific figures and their inclusion in reports, and written presentation skills. Programming ability will enhance the quality of students’ dissertation projects and will be a key transferable skill from their degree.

Outline content:

• Numerical solution of algebraic equations.


• Numerical solution of ordinary and partial differential equations, especially finite difference methods.


• Multi-dimensional systems and chaos theory.


• The physical behaviour of solutions to common equations in environmental science, such as diffusion and advective transport by winds or currents.


• Accuracy, stability, and convergence of numerical algorithms.

Brief description of teaching and learning methods:

Lectures: Theory will be presented, reinforced by short exercises carried out by the students. Computing practicals: Students will develop programming skills using the Python language and apply the theory to problems set in assignments.

Contact hours:

	Autumn	Spring	Summer
Lectures		10
Practicals classes and workshops		30
Guided independent study:		60
Total hours by term	0	100	0
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Report	50
Set exercise	50

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

A number of set exercises and a report.

Formative assessment methods:

Assignments in weekly practical classes, plus short exercises in the lectures.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

40% overall.

Reassessment arrangements:

Resubmitted and/or alternative project report.

Additional Costs (specified where applicable):

Last updated: 8 April 2021