Module manager: Prof Jurgen Neuberg
Email: J.Neuberg@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
SOEE2190
This module is not approved as a discovery module
The module will provide the mathematical foundation for spectral analysis and filter theory as applied to the analysis of a large variety of data sets in applied and pure geophysical and atmospheric science. Students will apply the mathematical concepts to a wide variety of data sets from geophysical and atmospheric examples in computer-based practicals. Students will learn the theoretical basis behind different approaches and also put them into practice using Python on a range of geophysical and atmospheric problems.
On completion of this module, students should be able to:
• Provide an understanding of the consequences of data digitisation compared to dealing with analogue signals.
• Understand the decomposition of any digital signal in time or space into a series of harmonic waves in order to characterise them according to their frequency contents.
• Gain awareness of the different properties of the Discrete Fourier Transform (DFT) and its applications in data analysis.
• This will be achieved by providing a solid mathematical background of the DFT rather than treating it as a computational black box. Extensive computer-based practicals will enable the student to explore and practise a wide variety of applications such as filtering, convolution, correlation, and other methods of data analysis.
On successful completion of the module students will have demonstrated the following learning outcomes:
SSLO1: develop an understanding of discrete frequency (harmonic) analysis by appreciating the consequences of the digitisation process.
SSLO2: apply convolution and deconvolution to time series and the use digital filters in time and frequency domain.
SSLO3: carry out correlations between data sets and assess uncertainties in time and space.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SKLO1: use Jupyter notebooks to visualise data sets and explore programming examples in Python (Digital Skills: Digital proficiency & productivity)
SKLO2: summarise findings in concise scientific reports including the effective use of graphs and figures (Academic Skills: Academic writing)
SKLO3: the ability to work to deadlines (Work Ready Skills: Time management, planning & organisation)
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 14 | 1 | 14 |
| Practicals | 5 | 2 | 10 |
| Independent online learning hours | 14 | ||
| Private study hours | 62 | ||
| Total Contact hours | 24 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Assessment of first report on computer practical is formative. Further feedback is given after submission of consecutive reports.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 40 |
| Coursework | Coursework | 60 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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