Department of Marine Sciences

Oceanographic Data Processing

(1) General

School:	Of the Environment
Academic Unit:	Department of Marine Sciences
Level of studies:	Undergraduate
Course Code:	191ΜΥ25Ε	Semester:	G
Course Title:	Oceanographic Data Processing
Independent Teaching Activities		Weekly Teaching Hours	Credits
		Total credits	5
Course Type:
Prerequisite Courses:	Officially, there are not prerequisite courses. However, the student needs to have essential knowledge of Descriptive Physical Oceanography, Statistics and Calculus
Language of Instruction and Examinations:	English - in the absence of Erasmus students, the course can be taught in Greek
Is the course offered to Erasmus students:	Yes. In the case of Erasmus students the course is adapted depending on each student background
Course Website (Url):	https://www.mar.aegean.gr/index.php?lang=en&pg=3.1.1&lesson=1079

(2) Learning Outcomes

Learning Outcomes

The aim of this course is the introduction of the students with the available time-series analysis tools, to that in they will be aware of the data analysis capabilities and limitations in identifying deterministic processes, the statistical verification or rejection of relationships and hypothesis. The course’s aim is not to arm students with a complete theoretical background and the ability to develop new statistical tools, but to educate the students about the available tools and their capabilities and limitations.

In more detail, the student that will have successfully participated in the course are expected to:

(α) know the available time-series analysis tools and their characteristics.

(β) be able to design sampling strategies taking into consideration the capabilities and limitations of the tools that will be later applied.

(γ) be able to select the tools suitable to the problem he is facing.

(δ) be able to apply and develop programming code in a language of the Octave – Matlab – Scilab family.

(ε) be informed about some basic properties of more complicated available tools, like wavelets, empirical orthogonal functions etc., their applicability and capabilities.

General Competences

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Criticism and self-criticism

Production of free, creative and inductive thinking

(3) Syllabus

Introduction
Types of Oceanographic Measurements
Eulerian measurements - synopticity
Lagrangian measurements
Vector and scalar variables – complex representation of time-series
Sampling rate – burst sampling
Kinds of time-series
Deterministic, stochastic and chaotic processes
Simple records, ensembles
Ensemble mean values and autocorrelations
Stationarity and ergodicity of timeseries
Laboratory Introduction
Introduction to the languages octave – matlab – scilab
Familiarization with simple commands and performing the absolutely necessary actions (variable usage, calculations using matrices, I/O functions, making diagrams).
Simple statistics calculations (computing mean, maximum and minimum values, standard deviation), production of scatterplots and histograms.
Construction of simple lowpass and highpass filters
Calculation of running mean
The role of sampling window length
Use of running mean as a low-pass and high-pass filter
Fourier analysis
Periodograms
Role of sampling intervals and window-length
Frequency determination
Spectral estimations
Harmonic Analysis
Deficiencies of the application of spectral analysis on highly deterministic processes
Description of harmonic analysis
Application on a time-series focusing on tidal signals
Covariance and correlation
Discussion about the need to quantitavely identify the statistical relation between two time-series, or the “memory” of one.
Description of the calculation of covariance and correlation.
Time-lagged cross-correlation.
Auto-correlation.
Application to two time-series.
Coherence
Discussion about the need to identify highly-correlated frequency bands.
Description of computation of coherence through analysis Fourier.
Application to two time-series.
Return to spectra and epilogue.
Return to spectral computation
Reference to rotary spectra
Reference – description to sampling window types and characteristics.
Determination of spectral confidence intervals
Reference to other methods (wavelets, Empirical Orthogonal Functions)

(4) Teaching and Learning Methods - Evaluation

Delivery:

Face–to-face delivery.

All the necessary material, plus supplementary material is provided electronically through the e-class platform.

Use of Information and Communication Technology:

The e-class platform is used extensively, both for delivery of the material to the students, as well as for examining their progress and grading their performance.

Teaching Methods:

	Activity	Semester workload
	Lectures	19
	Laboratory exercises	20
	Independent study	16
	Final exam	1
	Assignment work	40
	Essay writing	30
	*Course total*	126

Student Performance Evaluation:

The Language of evaluation for Greek Students is Greek, for foreign student is English.

The student evaluation takes place (a) via the submission of weekly homework, with applications on real data from the field and (b) via the assessment of understanding and capacity to exploit the course material at the end of the semester using the e-class platform. The homework reports are assigned to groups of two to three students.

The evaluation criteria for the reports are mentioned in the first homework assignment, include the correct data analysis procedure, the presentation of their report, the correct descritios of axes and units on the diagrams produced, the commenting of the results of the analysis so that it is revealed whether the students has not only analysed the data correctly but also understood the reason for applying the method and its results. Additionally, the overall student evaluation policy is described in the first lecture in class (and the corresponding notes).

The student reports are commented via the e-class system.

(5) Attached Bibliography

- Suggested bibliography:

Kogias G., 2010. Introduction to digital signal processing. Publisher: "Modern Publishing", ISBN: 978-960-6674-53-2.
Strintzis, M.-G.., 2000. Time-series analysis. Kyriakides Bros, Thessaloniki (in Greek).
Zervakis, V., 2016. Laboratory Exercises on «Oceanographic Time-Series Analysis», University of the Aegean (in Greek).
Emery and Thompson, 2001. Data analysis methods in physical oceanography.Elsevier Science
Storch & Zwiers, 2002. Statistical Analysis in Climate Research. Cambridge University Press.
Trauth, Gebbers, Marwan and Sillmann, 2007. MATLAB Recipes for Earth Sciences. Springer.

- Related academic journals:

Progress in Oceanography - https://www.journals.elsevier.com/progress-in-oceanography
Journal of Physical Oceanography - https://www.ametsoc.org/ams/index.cfm/publications/journals/journal-of-physical-oceanography/
Journal of Geophysical Research – Oceans - http://agupubs.onlinelibrary.wiley.com/hub/jgr/journal/10.1002/(ISSN)2169-9291/
Journal of Marine Systems - https://www.journals.elsevier.com/journal-of-marine-systems

Connection