COURSE UNIT TITLE

: DATA ASSIMILATION IN OCEAN MODELLING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
PHO 5014 DATA ASSIMILATION IN OCEAN MODELLING ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR MURAT GÜNDÜZ

Offered to

PHYSICAL OCEANOGRAPHY

Course Objective

The class aims to provide fundemental knowledge and techniques to combine ocean observations and models to predict future state of the ocean. An advanced data assimilation methods that are considered useful in oceanography will be will be reviewed. The class will cover from basic and simple methods in data asimilation to more advanced methos like Kalman filters.

Learning Outcomes of the Course Unit

1   Understanding of the fundamental objectives of data assimilation in ocean modelling
2   Gain knowledge on the theory of data assimilation
3   Understanding of the basic ideas of 4D-var and Kalman-filter
4   to learn asiimilation of the data into models
5   to be able to use basic tools in data asimilation for real case studies

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction: The Oceanographic Data Assimilation Problem, overview, motivation, urposes
2 Data Assimilation vs. Fitting Models to Data
3 Methodology of Model Fitting
4 Observational Errors
5 Models and Data
6 Parameter Estimation
7 Global Applications: Combining data and the global Primitive Equation Ocean General Circulation model using the adjoint method, Identical Twin Experiments
8 mid-term
9 Extended Nudging Method in Data assimilation
10 Re-Initialization Method
11 Kalman Filter for Nonlinear Primitive Equation Model
12 Real-time Regional Forcasting Aspect
13 Interdisiplinary Applications
14 Case Study: Assimilation of Satellite Altimeter Data into an Eddy-Resolving Primitive Equation Model of the North Atlantic Ocean.

Recomended or Required Reading

Thacker, W. C., Three lectures on fitting numerical models to observations, 1988, GKSSForschungszentrum
Geesthacht GmbH, Geesthacht, 64p.

Malanotte-Rizzoli, P., Modern Approches to Data Assimilation in Ocean Modelling, 1996, Elsvier Oceanography Series, 61.

Brandt, S., Datenanalyse, 1992, Wissenschaftverlag, Mannheim, 650p.

Thacker, W. C. and R.B. Long, 1987, Fitting Dynamics to Data, Atlantic Oceanographic and Meteorological Laboratory National Oceanic and Atmospheric Admistration.

Ghil, K., Cohn, S., Tavantzis, J., Hube, K. and Isaacson, E., Application of Estimation Theory to Numerical Weather Prediction, Courant Institute of Mathematical Sciences, New York University.

Planned Learning Activities and Teaching Methods

Lectures will be held conventionally

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE PRJ * 0.30 + MTE * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) PRJ * 0.30 + MTE * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Yrd. Doç. Dr. Murat Gündüz
Deniz Bilimleri ve Teknolojisi Enstitüsü
murat.gunduz@deu.edu.tr

Office Hours

will be announce at the first lecture

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 12 6 72
Preparation for midterm exam 1 15 15
Preparation for final exam 1 40 40
Preparing assignments 1 60 60
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 232

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12
LO.1555545544435
LO.2555535544435
LO.3545535554435
LO.4555545555555
LO.5555545545545