MTMD01-Environmental Data Exploration & Visualisation

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: (or other introductory programming course)
Co-requisites: MTMA33 Introduction to Computing or MTMW12 Introduction to Numerical Modelling
Modules excluded:
Module version for: 2017/8

Module Convenor: Dr Tom Frame

Email: t.h.a.frame@reading.ac.uk

Summary module description:
This is a practical module that introduces students to the manipulation and visualisation of environmental data. Tasks are performed in the Python programming language, which students should already be familiar with. Real data are used throughout the module, which the students will learn to read, plot and compare in several different ways.

Aims:

• To educate students in the use of modern tools for supporting scientific data manipulation;



• To instil good discipline in the management of environmental data and its importance to science;



• To compare and contrast different tools and techniques, and their appropriateness to the different stages of scientific investigation.


Assessable learning outcomes:

Students will be able to:



1. Explain the importance of using consistent data formats and appropriate metadata conventions;



2. Format and manipulate data in a number of commonly-used formats;



3. Use a number of tools for exploring, visualizing and intercomparing various kinds of environmental datasets, understanding their strengths and weaknesses.


Additional outcomes:
Students will learn:
1. the importance of “reproducible science”, i.e. data and data-manipulation code should be reusable and verifiable by colleagues

Outline content:

This will include:



• Brief review of the Python programming language;



• Good programming practices, e.g. modularity of code;



• The NetCDF file format and the Climate and Forecast conventions;



• Reading data from numerical models, in situ instruments and remote-sensing platforms;



• Manipulating and visualizing data in Python, ncBrowse, ncview and other tools;



• The use of different map projections.


Brief description of teaching and learning methods:

This module will use a mixture of lecturing and hands-on computer laboratory practical work, supplemented by background reading and assignments.



Reading lists for meteorology modules are available here https://reading.rl.talis.com/departments/mps_met.html



 


Contact hours:
  Autumn Spring Summer
Lectures 10
Practicals classes and workshops 10
Guided independent study 80
       
Total hours by term 100.00
       
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Written assignment including essay 50
Dissertation 50

Other information on summative assessment:

Formative assessment methods:
In-class and take-home exercises.

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

Length of examination:
N/A

Requirements for a pass:
50% overall.

Reassessment arrangements:
Reassessment by resubmission of largest piece of coursework (project).

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: 11 October 2017

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