MT2NSMNU: Numerical and Statistical Methods for Weather and Climate Science

Module code: MT2NSMNU

Module provider: Meteorology; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Level 2 (Intermediate)

When you'll be taught: Semester 1

Module convenor: Professor Paul Williams, email: p.d.williams@reading.ac.uk

Module co-convenor: Professor Ted Shepherd, email: theodore.shepherd@reading.ac.uk

NUIST module lead: Charman Gul, email: 600090@nuist.edu.cn

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE MT1SES OR TAKE MT12C OR TAKE MT1SESNU OR TAKE MT12CNU (Compulsory)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 21 May 2024

Overview

Module aims and purpose

This module comprises both a lecture and a computer practical component, which together introduce students to the numerical and statistical methods that are used in weather and climate science. The aim is to provide students with an understanding of the basic theoretical principles involved in both kinds of methods, their appropriate use for weather and climate science, and experience with their practical implementation using computer programs. Because numerical and statistical methods underlie pretty much any application in weather and climate science, this knowledge is essential for correctly deriving or interpreting scientific results.

Getachew Dubache (gechdubache@yahoo.com) also teaches on this module.

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

1. Develop numerical algorithms for solving equations and implement them as computer programs.
2. Use numerical analysis to evaluate the results produced by the programs and design ways to improve them.
3. Describe the main concepts in statistical science and use statistical software.
4. Critically analyse data and draw correct inferences using appropriate statistical methods.

Module content

• Introduction to numerical methods
• Numerical solution of algebraic equations
• Numerical solution of ordinary and partial differential equations via finite difference methods
• Multi-dimensional systems and chaos theory
• The physical behaviour of solutions to the advection and diffusion equations
• Accuracy, stability, and convergence of numerical algorithms
• Introduction to statistics, history and controversies
• Exploratory data analysis, forecast verification
• Linear and multiple regression
• Probability theory, probability distributions
• Parameter estimation
• Hypothesis testing

Structure

Teaching and learning methods

Lectures are followed by computer practical sessions which are designed to illustrate and give students hands-on experience with the theoretical concepts presented in the lectures.

Study hours

At least 96 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

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

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Short interactive exercises/quizzes in each lecture, plus informal feedback in each computer practical session. 

Reassessment

Additional costs