MT38CNU-Numerical Weather Prediction
Module Provider: Meteorology
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Autumn term module
Pre-requisites: MT24CNU Numerical Methods for Environmental Science
Non-modular pre-requisites: MT12CNU £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:
Current from: 2021/2
Module Convenor: Prof Peter Clark
Email: p.clark@reading.ac.uk
Type of module:
Summary module description:
In this module we will examine the components that make up a numerical weather forecast.
The module lead at NUIST is Prof. Xiefei Zhi (zhi@nuist.edu.cn).
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, and uncertainty.
Additional outcomes:
The student will also develop an understanding and appreciation of some basic dynamical systems theory as applied to weather prediction. During 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.
Outline content:
0. History of weather forecasting
Lecture 0.1. Introduction to NWP.
1. Equations of motion
Lecture 1.1. The Material Derivative and Advection.
Lecture 1.2. Fundamental equations of fluid mechanics.
2. Finite difference discretisation of partial differential equations.
Lecture 2.1. Approximate versions of fluid mechanics equations for meteorology.
Lecture 2.2. Vertical coordinates.
3. The barotropic and equivalent barotropic vorticity equations
Lecture 3.1. The Shallow Water Equations.
Lecture 3.2. Circulation, vorticity and potential vorticity in the SWEs.
Lecture 3.3. The Barotropic Vorticity Equation.
Lecture 3.4. Solution strategy for the EBVE.4. Other numerical techniques for pde’s.
4. Spectral methods
Lecture 4.1. Spectral methods – introduction.
Lecture 4.2
. Discrete Fourier series.
Lecture 4.3. Non-linear terms in spectral methods.
Lecture 4.4. Two dimensions and the sphere.
Lecture 4.5. Semi-Lagrangian advection.
Lecture 4.6. TVD advection.
5. Parametrization in NWP models
Lecture 5.1. Energy Cascades in the Atmosphere.
Lecture 5.2. Physical Parametrizations in an NWP/Climate Model.
Lecture 5.3. Parametrizing Moist Convection.
Lect
ure 5.4. Parametrization and Modelling Microphysical Processes.
Lecture 5.5. Parametrization and Modelling Radiative Transfer Processes.
Lecture 5.6. Simpler Parametrizations of Microphysics and Radiation.
Lecture 5.7. Diagnostic and 1D Parametrizations of Microphysics and Radiation.
Lecture 5.8. Parametrizing the Boundary Layer.
6. Data assimilation and initialization
Lecture 6.1. Introduction to data assimilation
Lecture 6.2. Bayes’ Theorem.
Lecture 6.3. Variational Data Assimilation.
Lecture 6.4. Initialization and Ensemble Methods.
7. Chaos and uncertainty: dynamical systems, predictability and ensembles
Lecture 7.1 Sources of Uncertainty.
Lecture 7.2. The Birth of Chaos: the Lorenz Attractor (1)
Lecture 7.3. The Birth of Chaos: the Lorenz Attractor (2)
Lecture 7.4. Limits of Predictabilit
y.
Lecture 7.5. Ensemble Forecasts.
Brief description of teaching and learning methods:
Theory is presented in three 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. They will thus gradually build up the components of a simple but realistic atmospheric model. This will be completed and tested within the class practical.
| Autumn | Spring | Summer | |
| Lectures | 48 | ||
| Practicals classes and workshops | 16 | ||
| Guided independent study: | |||
| Wider reading (independent) | 16 | ||
| Wider reading (directed) | 16 | ||
| Group study tasks | 4 | ||
| Total hours by term | 100 | 0 | 0 |
| Total hours for module | 100 |
| Method | Percentage |
| Written assignment including essay | 50 |
| Class test administered by School | 50 |
Summative assessment- Examinations:
One final examination will be administered by School/Dept (2 hours).
The total assessment will take into account 50% of this final examination marks.
Summative assessment- Coursework and in-class tests:
One multiple choice test (1 hour).
One report based on experiments (e.g. looking at predictability) performed with the final student written Python model.
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.
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 40% overall.
Reassessment arrangements:
Re-sit of class test in August/September only.
Re-submission of modelling report.
Additional Costs (specified where applicable):
Last updated: 4 May 2021
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.