MTMG06-Statistics for Weather and Climate Science

Module Provider: Meteorology
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites: Understanding of the basics of matrix algebra and some programming experience (ideally in python) would be an advantage for students taking this course.
Co-requisites: MTMA33 Introduction to Computing or MTMW12 Introduction to Numerical Modelling
Modules excluded:
Current from: 2020/1

Module Convenor: Prof Maarten Ambaum


Type of module:

Summary module description:
This module aims to introduce the basic statistical concepts and reasoning relevant to atmospheric science as well as providing experience in the proper use of statistical methods for the analysis of weather and climate data.

This module aims to introduce basic statistical concepts and reasoning relevant to environmental science, as well as provide experience in the proper use of statistical methods for the analysis of weather and climate data.

Assessable learning outcomes:

By the end of this module the student should be able to:

• Describe the main concepts in statistical science;

• Select and compare appropriate analysis methods;

• Critically analyse data and draw correct inferences;

• Implement statistical methods in the python programming language.

Additional outcomes:

• Discuss the development and importance of statistics;

• Appraise and criticise quoted statistics (transferable skill) especially in the geosciences.

Outline content:

The lecture content covers:

• Exploratory data analysis;

• Probability distributions;

• Statistical inference and testing;

• Linear modelling;

• Multivariate methods;

• Time series analysis.

The practical content involves supervised hands-on experience using statistical software to analyse and interpret data.

Brief description of teaching and learning methods:

Lectures and computer practicals.

Reading lists for meteorology modules are available here

Contact hours:
  Autumn Spring Summer
Lectures 12
Practicals classes and workshops 6
Guided independent study: 82
Total hours by term 100
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Written assignment including essay 100

Summative assessment- Examinations:


Summative assessment- Coursework and in-class tests:

Formative assessment methods:
Practical exercise, which is an introduction (or review) to python and to exploratory data analysis. This does not count to the final mark but allows students to practice before the real assignment.

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

Assessment requirements for a pass:
A mark of 50% overall.

Reassessment arrangements:
For candidates who have failed, an opportunity to take a resit examination will be provided within the lifetime of the course.

Additional Costs (specified where applicable):

1) Required text books: 2) Specialist equipment or materials: 3) Specialist clothing, footwear or headgear: 4) Printing and binding: 5) Computers and devices with a particular specification: 6) Travel, accommodation and subsistence:

Last updated: 4 April 2020


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