Undergraduate Scholarships

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: To complete this project experience with python is essential and experience with analysing large datasets is a plus.

Supervisors: Dr. Tim Raupach

The characteristics of numerically simulated clouds and convection depend on the resolution of weather and climate models. Subgrid clouds are parameterized in coarse-resolution models but are often resolved at higher resolutions. Such clouds are essential in understanding shallow convection and can significantly affect the radiation budget if unaccounted for in our current models. This project aims to quantify the characteristics of subgrid clouds by comparing several associated cloud and radiation fields simulated at different resolutions from a numerical weather prediction model to ground-based and available satellite observations. The main objective of the project is to understand how well sub-cloud variability is captured to varying resolutions in model simulations.

Experience: The project requires Python programming skills in analysing data.

Supervisors: Dr. Abhnil Prasad and Prof. Steven Sherwood

Apply here 

Humid heat environments feature high temperatures and humidity at the surface, posing significant risks to public health by reducing the body’s ability to effectively cool itself to a safe core temperature. A synoptic-scale substance is commonly recognized to be responsible for a high dry temperature, but the associated divergence at the low level suppresses the moisture transport, which usually leads to aridity. Conversely, an updraft facilitates the low-level convergence of moisture but undermines heat maintenance capacity within the boundary layer. This dilemma hinders a clear understanding of the physics governing the buildup of humid heat environments. The student will investigate how large-scale synoptic patterns interact with boundary layer thermal dynamics to uncover the physical mechanisms behind humid heat extremes in Australia.

Experience:  Familiarity with Python and a basic understanding of thermal/convective dynamics is required for this project.

Supervisor: Dr Qin Jiang, and Prof Steven Sherwood and Dr Marty Singh

Apply Here 

Townsville experienced record-breaking rainfall in February and March 2025, with 1,033 mm falling in just the first eight days of February. On March 18–19, the city recorded 301.4 mm in 24 hours which is the heaviest rainfall in 27 years, caused widespread flooding across north Queensland. In the present study, the student will explore the atmospheric and oceanic conditions that contributed to these extreme rainfall events. Using reanalysis data, the student will compare the behaviour of key atmospheric and oceanic variables such as sea surface temperature, outgoing longwave radiation, and atmospheric temperature against its climatology (1995 to 2024). The student will also examine wind patterns and vertical motion to understand how they influenced moisture transport and instability in the atmosphere. By analyzing these events, the study can identify the large-scale weather patterns responsible for the heavy rainfall. The study will help student to understand the mechanism and processes behind the occurrence of such extreme rainfall events.

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

Supervisors: Dr. Leena Khadke and Prof. Jason Evans

Apply Here

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.

Supervisors: Dr. Sanaa Hobeichi and Dr. Elisabeth Vogel 

Apply Here

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.

Supervisors: Associate Professor Negin Nazarian, Dr. Jiachen Lu and Dr. Sanaa Hobeichi

Apply Here 

Marine extreme events have a significant impact on marine ecosystems and are predicted to intensify as the ocean warms. However, our understanding of the impacts and nature of marine extremes is mostly limited to studying temperature at the surface, due to a lack of observations. Insights into the response of the ocean to marine extremes can only be gained by examining and contrasting the specific sites which have sufficient long-term observations

The student will participate in taking a global inventory of long-term observational sites, identifying marine extremes, and exploring the differences between the ocean’s response to extremes in different locations. This project will contribute to our understanding of global subsurface ocean extremes and their impacts on marine ecosystems.

Experience: None

Supervisor: Dr Neil Malan, opens in a new window

Large-scale climate modes such as El Niño-Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole significantly influence weather and climate variability across Australia. These modes are typically quantified using Sea Surface Temperature (SST) data from specific regions of the ocean. This project aims to compute these climate indices using simulations from Australia's seasonal forecasting system, ACCESS-S2. ACCESS-S2 is a fully coupled dynamical model operated that provides seasonal climate forecasts. Specifically, the project will derive large-scale climate mode indices from historical SST outputs of ACCESS-S2 and compare these results with satellite-derived SSTs.

The project is expected to commence in July.

Requirements: The successful applicant should have strong programming skills in Python.

Supervisors: Dr.Mandy Freund and Dr.Sanaa Hobeichi 

Apply Here