PhD and Masters Scholarships in Weather and Climate
Forecasting using Artificial Intelligence
1
PURPOSE
There have been great advances in the application of artificial neural networks (ANNs) for forecasting, but so far
this technology has not been used for operational weather forecasting. Preliminary investigations suggest that
prototype models, based on this powerful statistical modeling tool run on gaming computers, are more skillful at
medium-term rainfall forecasting than the most advanced general circulations models run on supercomputers.
2
SCOPE
If you would like to be part of an expanding team using this form of artificial intelligence to better understand
weather and climate, consider applying for one of three PhD/Masters Scholarships in Weather and Climate
Forecasting using Artificial Intelligence (hereafter ‘the Scholarships’) currently offered through Central Queensland
University.
The Scholarships will be awarded by the University through a competitive selection process.
applications will be ranked on the basis of merit and responses with the selection criteria.
Individual
Successful applicants will be based at the Noosa campus of Central Queensland University. Noosa is 140 km
north of Brisbane on the Sunshine Coast. With its laidback life style and open spaces, Noosa is a pleasant place to
live and a good place for thinking.
Professor John Abbot will be the principal supervisor. Professor Abbot has 10 years experience working with
artificial neural networks, initially for share trading. It was the flooding of Brisbane in January 2011 that motivated
his interest in using artificial neural networks for rainfall forecasting.
Professor Abbot has a BSc from Imperial College, London, a PhD from McGill University, Montreal, and over 110
publications in scientific and law journals. While at the University of Tasmania, Professor Abbot successfully
supervised to completion nine PhDs and nine Honors students resulting in 50 publications.
3
PROCEDURE
Project Summary
Projects include, but are not limited to, the following:
1. Forecasting El Niño-Southern Oscillation
For three decades, there has been a significant global effort to improve El Niño-Southern Oscillation (ENSO)
forecasts with the focus on using fully physical ocean-atmospheric coupled general circulation models. Despite the
increasing sophistication of these models, their predictive skill remains only comparable with relatively simple
statistical models, with some blaming a phenomenon known as the Spring Predictability Barrier (SPB).
Preliminary studies suggest that artificial neural networks can forecast through the SPB. It is possible further
advances could be made through the refining of input variables building on the work of Aiming Wu (see Neural
Networks, Volume 19), and possibly by also potentially considering extra-terrestrial influences including
atmospheric tides (see Ken Ring, The Lunar Code).
The development of an improved method for forecasting ENSO through the elucidation of the most relevant input
variables could be the focus of this project.
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2. Signal processing to understand drivers of rainfall
There is a natural relationship between artificial neutral networks and signal processing. The neural network
software that underpins our current prototype models was developed at the University of Florida by researchers in
their department of electrical engineering with expertise in signal processing. Our prototype models, however, do
not explicitly decompose the rainfall time-series signals into components. If the component signals were elucidated
it would potentially aid understanding of the drivers of rainfall, and potentially improve forecasts.
Exploration of these concepts could form the central theme of a project that would best suite a graduate with a
background in signal processing and/or electrical engineering.
3. Considering cyclical changes at the Antarctic to forecast rainfall in the Murray Darling
Australian farmers have long sought advice from long-range weather forecasters who operate independently of the
Bureau of Meteorology, perhaps beginning with the work of astronomer Inigo Owen Jones. Modern forecasters
using the same cyclical variations claims a strong relationship between higher sea ice averages in the Antarctic
and periods of below average rainfall for eastern Australia and heavier late season frosts (see Kevin Long,
www.thelongview.com.au). The Antarctic Oscillation (also known as the Southern Annular Mode or SAM) is also
thought to be an important driver of rainfall variability in southern Australia (see Australian Bureau of Meteorology,
http://www.bom.gov.au/climate/enso/history/ln-2010-12/SAM-what.shtml).
The focus of this project could be input selection and optimisation for monthly rainfall forecasting in the Murray
Darling, including a consideration of the Antarctic Oscillation and changes in sea ice extent.
4. Modelling past temperatures and forecasting future temperatures – globally and locally
General circulation models, that underpin the current dominant paradigm in climate science and forecast global
warming, simulate climate based on an assumed first principles understanding of the physical process. In contrast,
ANNs rely on historical climate data to acquire knowledge, learn relationships, model and measure relationships
and then use this information to make forecasts.
ANNs could be used to both provide an independent forecast of future temperatures, and as an independent
method of GCM validation under future climate. Limited research is already occurring in this area (e.g. Kisi and
Shiri, International Journal of Climatology Volume 34) and could be the focus of more than one PhD and/or Masters
project. Such projects could also explore local, regional and global variability in temperatures historically and into
the future.
The integrity of historical temperature data is largely irrelevant to the performance of a GCM, but critical to the
operation of an ANN. So projects that focused on the use of ANN for forecasting future climate, would very likely
benefit from first developing a technique for creating continuous series of high quality temperature data for
individual locations as an input variable. While such temperature series theoretically already exist, they are not
stable over time and often represent a modelled version of the temperatures originally recorded (see Zhang et al,
Theoretical and Applied Climatology, Volume 115; Stockwell and Stewart, Energy & Environment, Volume 23; T.
Heller http://stevengoddard.wordpress.com/2014/07/18/nasa-hacking-australia/; B. Dedekind
http://wattsupwiththat.com/2014/06/10/why-automatic-temperature-adjustments-dont-work/; Marohasy et al., The
Sydney Papers Online, Issue 26).
5. Forecasting rainfall to aid mine scheduling – Provisional depending on support from industry
There is a need for more skillful medium-term rainfall forecasts for the Bowen Basin, a key coal-mining region in
Queensland. Official seasonal forecasts are currently based on general circulation models, are not reliable, and do
not provide adequate information in terms of timing and strength of rainfall for mine scheduling and pro-active risk
management. V.S. Sharma and colleagues detail these issues in a report published by the National Climate
Change Adaptation Research Facility in 2012.
The focus of a PhD or masters could include investigation of the possibility of using ANNs to generate forecasts for
shorter time intervals (2 weeks and 1 week) and shorter lead times (2 weeks and 1 week) and using humidity,
atmospheric pressure, cloudiness, wind direction and speed, as well as key climate indices as input variables.
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ABOUT ARTIFICIAL NEURAL NETWORKS
General information about ANNs is taught as part of machine learning courses. Yaser Abu-Mostafa at the
California Institute of Technology offers such an introductory online course, which includes some theory, algorithms
and applications, available for download and viewing at https://work.caltech.edu/telecourse.html.
Our ANNs are based on software developed by Neurosolutions. More information on this software is available at
http://www.neurosolutions.com .
REFERENCE LIST
Abbot J., Marohasy J., 2015. Using artificial intelligence to forecast monthly rainfall under present and future
climates for the Bowen Basin, Queensland, Australia. International Journal of Sustainable Development and
Planning. In press
Abbot J., Marohasy J., 2014. Input selection and optimization for monthly rainfall forecasting in Queensland,
Australia, using artificial neural networks. Atmospheric Research 128 (3), 166-178
Abbot J., Marohasy J., 2013. The potential benefits of using artificial intelligence for monthly rainfall forecasting for
the Bowen Basin, Queensland, Australia, In: Brebbia, C.A. (Ed.), Water Resources Management VII, WIT Press,
Southhampton, (on-line) doi:10.2495/WRM130261
Abbot J., Marohasy J., 2012. Application of Artificial Neural Networks to rainfall forecasting in Queensland,
Australia. Advances in Atmospheric Science 29, 717-730
Australian Bureau of Meteorology, 2014. The Southern Annular Mode (SAM)
http://www.bom.gov.au/climate/enso/history/ln-2010-12/SAM-what.shtml
Dedekind, B. 2014. Why automatic temperature adjustments don’t work
http://wattsupwiththat.com/2014/06/10/why-automatic-temperature-adjustments-dont-work/
Heller A., 2014. NASA Hacking Australia http://stevengoddard.wordpress.com/2014/07/18/nasa-hacking-australia/
Halide H., Ridd P., 2008. Complicated ENSO models do not significantly outperform very simple ENSO models.
International Journal of Climatology 28, 219–233
Kisi O., Shiri J., 2014. Prediction of long-term monthly air temperatures using geographical inputs. International
Journal of Climatology 34, 179-186
Long K., 2014. Current forecasts http://www.thelongview.com.au/forecast.html
Marohasy J., Abbot J., Stewart K., Jensen D., 2014. Modelling Australian and Global Temperatures: What’s Wrong?
Bourke and Amberley as Case Studies. The Sydney Papers Online, Issue 26.
http://www.thesydneyinstitute.com.au/paper/modelling-global-temperatures-whats-wrong-bourke-amberley-ascase-studies/
Ring K., 2006. The Lunar Code. Random House, New Zealand, pp 208
Risbey J. S., 2009. On the remote drivers of rainfall variability in Australia. Monthly Weather Review 137, 32333253
Sharma V.S, et al. 2012. Extractive resource development in a changing climate: Learning the lessons from
extreme weather events in Queensland, Australia, National Climate Change Adaptation Research Facility, Gold
Coast, pp. 110.
Stockwell D., Stewart K, 2012. Biases in the Australian High Quality Temperature Network, Energy & Environment,
Vol. 23, 10.1260/0958-305X.23.8.1273
Wu A., Hsieh W.W., Tang B., 2006. Neural network forecasts of the tropical Pacific sea surface temperatures.
Neural Networks 19, 145–154
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Zhang L. et al. 2014. Effect of data homogenization on estimate of temperature trend: a case of Huairou station in
Beijing Municipality. Theoretical and Applied Climatology 115, 365-373
Eligibility
Applicants don’t already need to know about meteorology, climate change or artificial intelligence.
3.1
To be eligible for the Scholarships, an applicant must:
a) have completed a Bachelor Degree with Honours I or IIA, or be regarded by CQUniversity as having an
equivalent level of attainment in a science or engineering discipline;
b) be an Australian or New Zealand citizens or permanent residents of Australia;
c) be approved for admission and enrolling in, a research higher degree program at CQUniversity;
d) enrol full-time and be an internal candidate at CQUniversity’s Noosa campus;
e) like problem-solving, and playing with numbers;
f) have an ability to work alone, but also be able to follow directions;
g) want to build a portfolio of peer-reviewed publications; and
h) must not be receiving an equivalent award, scholarship or salary providing a benefit greater than 75% of
the Scholarship stipend rate to undertake a research higher degree.
Tenure of award
3.2
The period of tenure for the Scholarships is:
a) in the case of a candidate enrolled for a research masters degree – two (2) years; and
b) in the case of a candidate enrolled for a research doctorate – 3.5 years.
3.3
A Scholarship recipient who commences as a candidate for research masters candidate and who before the
maximum duration of two (2) years, changes to doctoral candidature may hold the scholarship for a total of
up to 3.5 years.
3.4
The duration of the Scholarship will be reduced by:
a) any periods of study undertaken towards the degree prior to the commencement of the Scholarship; or
b) towards the degree during suspension of the Scholarship (unless the study was undertaken overseas as
part of a Commonwealth Government financially supported international postgraduate research
scholarship or award.
Extension of award
3.5
3.6
The scholarship is renewed on an annual basis subject to the candidate maintaining satisfactory progress,
as evidenced in candidature Progress Reports.
There is no provision for an extension to the duration of the Scholarships.
Part-Time study
3.7
The University may approve part-time candidature if the candidate has an acceptable reason related to
caring commitments, a medical condition, a disability or other circumstance which limits the student’s
capacity to undertake full-time study for part or all of the course of study.
3.8
A candidate who has been approved for part-time candidature may revert to full-time study at any time with
the permission of the University.
Termination of award
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3.9
The University will terminate the Scholarship
a) at the end of the period of tenure of the award;
b) on submission of the thesis;
c) if the candidate has failed to maintain satisfactory academic progress;
d) if the candidate ceases to be a full-time student and approval has not been obtained from the University
to hold the Scholarship;
e) if the award holder does not resume study at the end of a period of suspension or approved leave of
absence;
f) if the candidate ceases to meet the eligibility criteria as specified in Section 3.1 of these Conditions of
Award;
g) if the University determines that the Candidate has committed serious misconduct, including, but not
limited to the provision of false or misleading information in relation to the Scholarship.
3.10 If the Scholarship is terminated, it cannot be re-activated unless the termination occurred in error.
Stipend
3.11 The stipend will be paid fortnightly into an account in a bank, building society or credit union, through the
University’s payroll system. The stipend commencement date will be the date of commencement of study, or
in the case of a candidate who is already enrolled, on the date of acceptance of the Scholarship offer.
3.12 The stipend payable is $32,000 per annum (2014 rate) for full-time candidates. The stipend may be indexed.
It is the responsibility of the Candidate to assess the tax liability of their scholarship.
Overseas study
3.13 Where a candidate is required to pursue studies overseas for a limited period in order to advance the
research program, permission may be obtained to continue to hold the scholarship while overseas.
3.14 Overseas research will not normally be approved earlier than six (6) months into the program. The following
requirements must be met:
a) the period overseas must not exceed twelve (12) months;
b) adequate supervision of the candidate's research program overseas must have been arranged by the
School and approved before departure;
c) the candidate must return to Australia to complete the research program immediately following the
completion of study overseas;
d) the candidate must continue to be enrolled as a full-time candidate at the University during the period of
overseas study.
Research at other organisations
3.15 Approval may be given to conduct research at organisations outside the higher education system. Adequate
supervision of the candidate's research program must be arranged by the School.
Transfer
3.16 The Scholarship is not transferable to another university.
Other courses
3.17 As postgraduate research awards are provided for full-time study, the holder of such an award may not
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engage in any academic course of study leading to a qualification which is not an essential part of the award
recipient's current program.
Student charges
3.18 The candidate is required to pay all charges so levied by the University or the Commonwealth on the basis of
being a full-time candidate.
3.19 The Scholarship recipients may be eligible to be allocated a Research Training Scheme (RTS) place which
will be exempt from liability for tuition fees for the duration of their RTS eligibility.
3.20 The Scholarship is a living allowance stipend and does not cover any program tuition fees.
Leave of absence
3.21 Approval may be given for up to twelve months’ suspension of the Scholarship where an approved Leave of
Absence has been granted to the Candidate. Such periods of approved suspension will be added to the
Scholarship duration.
Other employment
3.22 Subject to approval by the Dean of Graduate Studies, a candidate may be permitted to undertake a limited
amount of paid employment, provided that such employment does not interfere with the recipient’s study
program. Employment may not exceed twelve (12) hours in any one (1) week if the award holder is to retain
status as a full-time candidate.
3.23 Where such employment is as a tutor, (and each contact hour involves additional hours of marking,
preparation, interviewing and administration), the candidate must include additional duties in calculation of
total weekly work hours.
3.24 The University cannot require the candidate to undertake employment.
Obligations of Recipient
3.25 The recipient is required to notify the Office of Research Services within seven (7) days in writing if:
a) the candidate leaves Australia for reasons other than for approved overseas study, approved suspension
or approved recreation leave;
b) the candidate discontinues full-time study in their CQUniversity research program and is not approved for
part-time study;
c) the candidate is absent for any reason for a period of fourteen (14) days or longer from the candidate's
place of study, except on approved leave.
3.26 The candidate shall abide by relevant legislation on human and animal experimentation and rulings of the
Safety and Ethics committees of the University.
3.27 An award recipient is required to conform to the regulations (including disciplinary provisions) of the
University.
Obligations of the University
3.28 The “Postgraduate Research Studies and Supervision Procedures" endorsed by the Academic Board
outlines the responsibilities of the University, Schools, supervisors and candidates.
3.29 Unsuccessful applicants who believe they have reasonable grounds for dissatisfaction with any aspect of the
selection procedure may request a re-evaluation of the original application. Such a request should be made
in writing and submitted to the Dean, Graduate Studies through the Office of Research Services not more
than twenty-eight (28) days after the date shown on the formal advice of the outcome of the selection
procedure.
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3.30 A candidate who has reasonable grounds for dissatisfaction with any formal decision made with respect to
the conditions of award, may appeal, in writing, to the Dean, Graduate Studies through the Office of
Research Services not more than twenty-eight (28) days after the date of the formal notification of the
decision.
3.31 CQUniversity recognises the importance of providing prompt and fair complaint resolution procedures for
candidates, without victimisation for initiating or participating in the settlement. The candidate’s enrolment
will be maintained while the complaint and appeals process is ongoing. For further information, please refer
to the CQUniversity Academic Appeals Procedures
4
RESPONSIBILITIES
Compliance, Monitoring and Review
4.1
The Dean of Graduate Studies is responsible for ensuring compliance with these procedures.
Records Management
4.2
All records relevant to this document are to be maintained in a recognised University recordkeeping system.
5
DEFINITIONS
Refer to the University glossary for the definition of terms used in this policy and procedure.
6
RELATED LEGISLATION AND DOCUMENTS
Related Policy Document Suite
Postgraduate Research Studies and Supervision Procedures
Academic Appeals Policy and Procedures
Approval and Review
Details
Approval Authority
Advisory Committee to Approval Authority
Administrator
Next Review Date
Dr John Abbot / Dr Jennifer Marohasy
Office of Research Services
Manager, Office of Research Services
Not applicable
Approval and Amendment History
Details
Original Approval Authority and Date
25 July 2014
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