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Data Assimilation and Model Improvement (ATOC90015)
Graduate courseworkPoints: 12.5Not available in 2023
To learn more, visit 2023 Course and subject delivery.
Overview
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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: 10 November 2023
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 |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
Summer Term (Dual-Delivery - Parkville)
|
12.5 |
Calculus: Knowledge of basic derivatives and limit theory
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10006 | Calculus 2 |
Summer Term (Dual-Delivery - 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)
Semester 1 (On Campus - Parkville)
Summer Term (Dual-Delivery - 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 (Dual-Delivery - Parkville)
Summer Term (Dual-Delivery - 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: 10 November 2023
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: 10 November 2023
Dates & times
Not available in 2023
Last updated: 10 November 2023
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: 10 November 2023