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Data Assimilation and Model Improvement (ATOC90015)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable (login required)(opens in new window)
Contact information
February
Overview
Availability | February |
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Fees | Look up fees |
Data assimilation refers to the process of combining model simulations of a natural system such as the atmosphere or ocean with observations to obtain an estimate of the actual trajectory of that system. It is vitally important to weather and climate prediction. Of all the improvements made to the Bureau of Meteorology’s global forecasting system since 2011, the top 5 were all from improvements to the data assimilation system. It is data assimilation that produces the multi-decadal reanalyses from which details of climate change and climate model error can be deduced. A wide range of industries such as finance, mining and medicine now regularly use data assimilation tools that were originally developed for atmosphere/ocean data assimilation applications. The course will introduce and explain the data assimilation systems now used at the world’s leading weather and climate forecasting centres. These systems include 4DVar and various flavours of the Ensemble Kalman filter. In addition, a brief introduction will be given to more accurate but more computationally expensive methods such as the particle filter and Monte-Carlo-Markov chain approaches.
Intended learning outcomes
On completion of this subject students will be able to:
- Derive Ensemble Kalman Filter (ENKF) data assimilation schemes and implement them in simple models;
- Derive 4DVar data assimilation schemes and implement them in simple models;
- Give examples of situations where assumptions of error distribution symmetry are invalid;
- Describe some of the data assimilation errors that occur when a scheme incorrectly assumes that the functional relationship between the error in one variable and another variable is linear; and
- Define the limitations of EnKF and 4DVar data assimilation systems and show how more advanced techniques such as the Particle Filter and the Monte-Carlo Markov Chain approaches attempt to overcome these limitations.
Generic skills
Upon completion of this subject, students should have gained the following generic skills:
- The ability to exercise critical judgement;
- Rigorous and independent thinking;
- Adopted a problem-solving approach to new and unfamiliar tasks; and
- Developed high-level written report presentation skills; oral communication and presentation skills.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
Linear algebra: Knowledge of matrices, inverse and matrix eigenvector/eigenvalue decompositions
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10007 | Linear Algebra |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Calculus: Knowledge of basic derivatives and limit theory
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10006 | Calculus 2 |
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
AND
Basic computing or programming.
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP10001 | Foundations of Computing |
Semester 2 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
COMP10002 | Foundations of Algorithms |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP20005 | Intro. to Numerical Computation in C |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
COMP90059 | Introduction to Programming |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Statistics: Knowledge of basic statistics (e.g. MAST10010)
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
6 x 15min Quizzes
| Throughout the teaching period | 30% |
Written Assignment
| Due during the second week of the teaching period | 20% |
Written Assignment
| Due during the first week of the assessment period | 20% |
Oral Exam
| Due at the end of the assessment period | 30% |
Last updated: 31 January 2024
Dates & times
- February
Coordinator Craig Bishop Mode of delivery On Campus (Parkville) Contact hours Total time commitment 170 hours Teaching period 12 February 2024 to 23 February 2024 Last self-enrol date 14 February 2024 Census date 23 February 2024 Last date to withdraw without fail 15 March 2024 Assessment period ends 22 March 2024 February contact information
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
Recommended texts and other resources
While there are no required texts for this subject, to prepare for the course consider consulting.
- Probabilistic Forecasting and Bayesian Data Assimilation, Sebastian Reich and Colin Cotter, Cambridge University Press Publication.14 May 2015 – 308 pages, and
- Atmospheric Data Analysis, Roger Daley, Cambridge University Press, 26 Nov 1993 - Science - 457 pages.
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 31 January 2024