2023-2024 / OCEA0045-1

Statistical methods of analysis of oceanographic data


20h Th, 10h Pr

Number of credits

 Master in space sciences (120 ECTS)3 crédits 



Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Oceanographic data, in-situ or satellite based, cover a wide time-space spectrum of processes, contain errors specific to each measurement system and require specific data analysis methods in function of the use one wants to make of them.

Learning outcomes of the learning unit

The lecture aims at describing the different measurement techniques used in hte marine environment and to introduce the different type of data analysis tools adapted to each type of data.

1.Statistical methods and error estimates : - probability, distributions, confidence intervals, regressions (linear, multivariate, correlation) - degrees of freedom, hypothesis testing - Obvious errors, rounding errors - interpolation - covariance 2. Spatial analysis - Objective analysis - Principal component analysis - Inverse methods 3. Temporal analysis - correlation function - Harmonic analysis - spectral analysis (FFT) - Wavelets - digital filters
Data acquisition and presentation will be also discussed during the lessons.

Prerequisite knowledge and skills

A solid mathematical background and basic knowledge in ocean sciences

Planned learning activities and teaching methods

Exercises will use real data sets from a CD-ROM. Matlab will be used for most exercises.

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face learning

Recommended or required readings

The main reference is
"Data analysis methods in physical oceanography", William J. Emery and Richard E. Thomson.
Other materials are available as well (see "on-line notes" section)

Skills will be assessed during exercise sessions using real inter-disciplinary data sets, taking into account the complexity and quality of methodology, processing, presentation, interpretation and synthesis.

Work placement(s)


Organisational remarks and main changes to the course

Lectures will be given once per week, with sessions of 3-4 hours.
Interested students should contact me to establish the timetable of this lecture.


Aida Alvera Azcárate AGO-GHER Université de Liège Allée du 6 Août, 17, Bât. B5 4000 Liège, Belgium
Tel.: +32 (0)4 366 3664 Fax.: +32 (0)4 366 9729

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