SOES2025 Methods in Oceanography
Module Overview
Aims and Objectives
Module Aims
1. To present the fundamental principles of ocean data, acquisition, processing, interpretation and application. 2. To provide basic knowledge of how ocean hydrographic and shallow seismic data are acquired. 3. To introduce the principles and limitations of in situ and satellite remote sensing of the ocean. 4. To provide practical experience using the data analysis tool Matlab, for oceanographic applications.
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- team working,
- time management,
- information retrieval from Internet.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Knowledge of physical oceanographic instrumentation and its deployment,
- Knowledge of hydrographic mapping and shallow seismic techniques,
- Capacity to analyse and interpret raw data and present conclusions from them,
- Capacity to critically assess datasets available via the Internet,
- Capacity to use the programming language Matlab in simple models and data analysis.
Learning Outcomes
Having successfully completed this module you will be able to:
- Describe the main methods for shallow seismic and acoustic mapping of the sea bed.
- Interpret the data acquired by geophysical instruments for sea floor mapping.
- Describe the main techniques for making physical measurements in the ocean.
- Analyse raw data acquired by in situ instruments measuring physical properties of the sea, and critically assess the errors and limitations for specific applications.
- Distinguish between the different methods used for satellite remote sensing of the ocean and identify the main ocean data products that each generates.
- Evaluate the quality and reliability of physical ocean data acquired from data sources through the Internet.
- Write a clear account of the analysis and interpretation of physical, geophysical and environmental ocean data.
- Access and download freely available oceanographic datasets in CSV, ASCII, netCDF and HDF format, and load them into Matlab.
- Apply physical understanding and mathematical analysis to real, observational data sets to a. determine fundamental characteristics of the data: mean/median. b. characterize the seasonality, secular trends, and covariability.
Syllabus
• Importance and fundamentals of ocean data • Acoustic methods for subsurface oceanography • Hydrographic mapping • Shallow seismic sea-floor mapping • Acoustic Doppler techniques for water column sounding • Temperature and salinity measurements, CTD etc. • Underway sampling and towed platforms, fluorometry, etc. • Principles of satellite remote sensing techniques to measure sea surface temperature, ocean colour, sea surface height and velocity, and surface winds. • Synergies between different types of data, including between in situ and satellite data sources. • Practical introduction to the use of scientific programming to analyse oceanographic data. • Timescales of variability in the ocean. • Case studies in the ocean, including Sverdrup gyres, eddies, sea level and phytoplankton.
Special Features
Note: this course assumes no prior knowledge in scientific programming, but does expect a basic familiarity with maths, including vectors, matrices, sines and cosines, and the equation of a line..
Learning and Teaching
Teaching and learning methods
Formal Lectures: 24 x 45 minute lectures will provide an introduction to the underlying theoretical principles of ocean data collection and processing, and will present factual information about the topics and methods in the syllabus and give explanations of how to put this knowledge into practice. Where relevant, lecturers’ own research experience in the appropriate fields is brought into the lecturing sessions. References to the applicable chapter of course text and/or other relevant journal articles are provided as useful reading for each lecture. Boatwork: One 6 hour session per group provides an opportunity to experience the deployment of physical and geophysical instruments at sea, plan a sampling strategy and acquire raw data for subsequent processing. Practical sessions: 8 x 2-4 hour sessions provide hands-on experience of processing and analysing different types of data using appropriate analytical computer tools. These sessions exemplify the theoretical concepts for data analysis covered during formal lectures allowing you to develop particular skills of relevance to both local and global-scale oceanographic analysis. Support: is provided by staff and/or postgraduate demonstrators. A wide range of support can be provided for those students who have further or specific learning and teaching needs.
| Type | Hours |
|---|---|
| Tutorial | 3 |
| Practical classes and workshops | 28 |
| Independent Study | 108 |
| Lecture | 24 |
| Total study time | 163 |
Assessment
Formative
Practical exercise
Summative
| Method | Percentage contribution |
|---|---|
| Practical (2 hours) | 25% |
| Practical exercise | 20% |
| Practical exercise | 20% |
| Practical exercise | 25% |
| Tutor group exercise | 10% |