## MTMG06-Statistics for Weather and Climate Science

Module Provider: Meteorology
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
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: 2019/0

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.

Aims:
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.

• 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.

Contact hours:
 Autumn Spring Summer Lectures 12 Practicals classes and workshops 6 Guided independent study: 82 Total hours by term 18 Total hours for module 100

Summative Assessment Methods:
 Method Percentage Written assignment including essay 100

Summative assessment- Examinations:

N/A.

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: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

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.