MT4YC-Numerical Weather Prediction

Module Provider: Meteorology
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites: MT24C Numerical Methods for Environmental Science
Non-modular pre-requisites: MT12C $£Skills for Environmental Science' highly desirable. Students must possess a level of competence in python programming such that they can confidently convert a short mathematical algorithm into a working python code and plot the results.
Co-requisites:
Modules excluded: MT38C Numerical Weather Prediction
Current from: 2023/4

Module Convenor: Dr Tom Frame
Email: t.h.a.frame@reading.ac.uk

Type of module:

Summary module description:
In this module we will examine the components that make up a numerical weather forecast.

Aims:
The aim of this module is to develop an understanding of the methods used in numerical models for operational weather prediction, climate simulation, and climate change prediction.

Assessable learning outcomes:

By the end of this module the student should be able to: Understand and discuss in some detail all the components of a numerical weather forecast including data assimilation and initialization, numerical implementation, parameterizations, uncertainty.Ìý

This module will be assessed to a greater depth than the excluded module MT38C.

Additional outcomes:

The student will also develop an understanding and appreciation of some basic dynamical systems theory as applied to weather prediction. ÌýDuring the course the students will further develop their programming skills and their skill in experimenting as they incrementally develop their own implementation of a practical numerical weather prediction model using python.Ìý

Outline content:

History of weather forecasting

Equations of motion

Finite difference discretisation of partial differential equations

The barotropic and equivalent barotropic vorticity equations

Other numerical techniques for pde’s

Parametrisation in NWP models

Data assimilation and initialization

Chaos and uncertainty: dynamical systems, predictability and ensemblesÌý

Brief description of teaching and learning methods:

Theory is presented in two interactive 50 minute lectures per week. As various equations and solution techniques are introduced, students will implement their own versions, in their independent study time and with in-class feedback during one interactive computer practical class per week. They will thus gradually build up components of a simple but realistic atmospheric model.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	20
Practicals classes and workshops	10
Guided independent study:	70
Ìý	Ìý	Ìý	Ìý
Total hours by term	100	0	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written assignment including essay	50
Class test administered by School	50

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One multiple choice test (1 hour).

One report based on development of and experiments with advection code.

One report based on development of and experiments with inversion or solver code.

One report based on experiments (e.g. looking at predictability) performed with the final student-written python model.

Students will be required to demonstrate a greater degree of understanding and physical insight through performing and analysing a wider range of model experiments than MT38C.

Formative assessment methods:
Immediate feedback on class exercises.

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: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/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:

A mark of 50% overall.Ìý

Reassessment arrangements:

Re-sit of class test in August/September only.Ìý

Re-submission of modelling report.

Ìý

Additional Costs (specified where applicable):

1) Required text books:Ìý

2) Specialist equipment or materials:Ìý

3) Specialist clothing, footwear or headgear:Ìý

4) Printing and binding:Ìý

5) Computers and devices with a particular specification:Ìý

6) Travel, accommodation and subsistence:Ìý

Last updated: 28 June 2023