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.