MT4NM: Numerical Modelling for Weather and Climate Science

Module code: MT4NM

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

Credits: 20

Level: 7

When you’ll be taught: Semester 1

Module convenor: Dr Tom Frame , email: t.h.a.frame@reading.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE MT2NSM OR TAKE MT24C (Compulsory)

Co-requisite module(s):

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

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 3 April 2025

Overview

Module aims and purpose

How do computer models for weather forecasting and climate prediction work?  

The aim of this module is to develop a theoretical and practical understanding of the methods used in numerical models for operational weather prediction, climate simulation and climate change prediction including coupled atmosphere/ocean models and earth-system models. 

Students taking this module will develop an understanding of the structure of computer models, how they are used and many of the limitations arising from design constraints. They will learn the similarities and differences between models designed for weather and climate prediction. They will also develop their computer programming skills as they build their own model of the atmosphere or ocean and use it to perform experiments. 

Module learning outcomes

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

1. Understand and discuss in some detail all the components of numerical weather-forecast, climate and earth system models. 
2. Construct a simplified atmosphere or ocean model using the python programming language. 
3. Use a computer model to undertake experiments studying theoretical concepts in atmospheric and ocean dynamics and real-world prediction.  

Module content

• History of weather forecasting, general circulation models and climate science.
• Equations of motion. 
• Consistent simplifications of the equations of motion, including hydrostatic, anelastic, boussinesq, shallow water, barotropic and equivalent barotropic vorticity equations. 
• Finite difference discretisation of partial differential equations. 
• Other numerical techniques for pde’s. 
• Parametrisation of atmospheric processes. 
• Atmosphere/ocean coupled models. 
• Simulating the earth system. 
• Data assimilation and initialization in Numerical Weather Prediction. 
• How models are designed for super-computers. 
• Chaos and uncertainty: dynamical systems, predictability and ensembles. 

Structure

Teaching and learning methods

Theory is presented in three interactive 50 minute lectures per week and one self-study video. 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 two-hour interactive computer practical class per week. They will thus gradually build up components of a simple but realistic atmospheric model and develop the skills to use this to test theoretical and practical concepts. 

Study hours

At least 50 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.