Study With Us: Undergraduate opportunities at 21st Century Weather

The ARC Centre of Excellence for the Weather of the 21st Century explores how Australia’s weather is being reshaped by climate change. We are currently offering highly competitive scholarships intended to provide undergraduate students from Australian universities an introduction to cutting-edge climate and weather change science research at one of our five universities, or our national partners.

The Centre includes five Australian universities (The University of New South Wales, Monash University, The University of Melbourne, The University of Tasmania and The Australian National University) and a suite of major national and international Partner Organizations.

Students should be in their second, third, or post-honours year and interested in pursuing honours or a postgraduate degree in climate or weather change science. Scholarship projects may either run on a full-time basis over the summer or other mid-semester/trimester breaks, or part-time for the equivalent of six weeks fulltime work throughout the academic year.

The scholarships are valued at $3,800. Scholarships are open to students currently enrolled in an Australian university.

If you have any questions about our undergraduate research scholarships, please contact the Centre’s Associate Director Leadership and Training Melissa Hart.

21st Century Weather Undergraduate Projects

Projects available at UNSW

Machine learning for climate

Supervisor(s): Professor Steven Sherwood, Dr Abhnil Prasad, Dr David Fuchs

Description: Most of our understanding of changes in atmospheric temperature and wind come from reanalysis products, but these are problematic for looking at small changes over long time periods. The student will examine a new homogenised global radiosonde dataset for climate-change signals including trends in upper tropospheric temperatures and winds.

Experience required: Familiarity with Python will be required for this project

Analysis of radiosonde data

Supervisor(s): Professor Steven Sherwood

Description: Australia’s most hail-prone regions are on the east coast from north of Brisbane to south of Sydney. However, the largest hailstone ever recorded in Australia fell in the sub-tropics, just north of Mackay, and the possibility of hail occurrence extends well into the tropics. In particular, a region around Burketown in Queensland shows as a hotspot of hail probability in radar, satellite, and hail-proxy records. In this project, we will investigate hail occurrence in convection-resolving simulations of the atmosphere around Burketown. The student will gain experience in analysing the output from high-resolution weather models, in atmospheric science, and in scientific programming. The project will increase our understanding of the atmospheric conditions leading to hail formation in the (sub-)tropics, a region in which hail occurrence is not well understood.

Experience required: None

Processing urban morphology for computer vision applications in city’s weather and climate

Supervisor(s): Dr Negin Nazarian, Dr Jiachen Lu, Dr Sanaa Hobeichi

Description: Complex flow patterns within urban environments are significantly influenced by the diversity of urban layouts but have only been studied from generalizations based on conventional urban geometrical parameters in climate models. However, the inner- and ultra-variability of cities’ layouts including the street orientations, building shapes, and building height distributions challenge the generalization validity. Considering the scope of the study is for global cities, the validation work is better assisted by computer vision techniques that require a strong database of urban morphology. Based on the recent progress in satellite data processing (e.g., OpenStreetMap (OSM) and Microsoft Building Footprints) and building height estimation (World Settlement Footprint (WSF)), the high-resolution urban morphology is ready for this purpose. In this project, the selected student will learn and apply image pre-processing techniques for computer vision applications in weather and climate. The data produced will contribute to enhancing the understanding of urban heterogeneities’ impact on climate models. In this project, the student will be coding and adapting existing scripts.

Experience required: The applicant needs to have programming experience in Python to be successful.

Validating and preparing an Australian drought inventory for machine learning training

Supervisor(s): Dr Sanaa Hobeichi, Dr Elisabeth Vogel

Description: This project aims to validate a comprehensive drought inventory and prepare it for Machine Learning training. The inventory is compiled from over a hundred drought reports and climate statements and includes detailed information on the locations, times, and impacts of past droughts on Australian communities and ecosystems. Examples of documented impacts include statements such as “crops are cut for hay and silage”, “water supplies in major population centres have been affected”, and “inadequate water availability in the main storage dam”. The selected student will use various climate observations, such as streamflow and precipitation data from monitoring stations, crop yield datasets, and satellite-derived vegetation indices to validate the reported drought impacts. This validation process is essential to identify any erroneous information and ensure the accuracy of the drought database. The validated database will be a valuable resource for advancing drought research and developing accurate drought models using Machine Learning.

Experience required: Students need to have experience in Python or R programming to be considered for this project.