°ÄÃÅÁùºÏ²Ê¿ª½±¼Ç¼
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.Ìý This will include mathematical analysis, modelling and some good programming practices.
Assessable learning outcomes:
By the end of this module students should be ableÌý to:
- Derive finite difference approximations using TaylorÌýseries;
- Explain the concept of stability and perform a basic stability analysis;Ìý
- Implement and test the behaviour of numerical schemes usingÌý Python;
- Recognise sources of numerical error and derive and measure order of accuracy; Use Fourier series for analysing both numerical methods an d climateÌý data;
- Use functions and loops in Python and avoid code duplication;Ìý
- 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.Ìý A list of background reading is supplied with the lecture notes.
Ìý | Autumn | Spring | Summer |
Lectures | 14 | ||
Practicals classes and workshops | 18 | ||
Guided independent study: | 68 | ||
Ìý | Ìý | Ìý | Ìý |
Total hours by term | 0 | 0 | |
Ìý | Ìý | Ìý | Ìý |
Total hours for module | 100 |
Method | Percentage |
Report | 60 |
Class test administered by School | 40 |
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
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.