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

Supervisors: Dr. Tim RaupachÌýand

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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:ÌýThe project requires Python programming skills in analysing data.

Supervisors:ÌýDr. Abhnil PrasadÌýandÌýProf. Steven Sherwood

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

Supervisor: , and Prof Steven Sherwood and

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: and Prof. Jason Evans

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:ÌýThe applicant needs to have programming experience in Python to be successful.

Supervisors: , Dr. Jiachen Lu and Dr. Sanaa Hobeichi

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:ÌýThe successful applicant should have strong programming skills in Python.

Supervisors:ÌýÌýand Dr.Sanaa HobeichiÌý

This project aims to develop a high-performance, user-friendly toolkit for analyzing NetCDF-based climate data by combining the computational efficiency of Rust with the accessibility of Python. Design and implement a Rust library capable of reading and processing common climate data formats (e.g., ERA5 or CMIP6), and expose these capabilities to Python via bindings using PyO3. Includes building a minimal command-line interface (CLI) and a Python module that allows scientists to perform common analysis tasks such as subsetting, computing temporal means, and extracting regional time series. Performance will be benchmarked against existing Python tools like xarray to assess improvements. The final deliverables will include a working software prototype, example Jupyter notebooks, and documentation outlining the design and usage of the tool.

Requirements:ÌýThe applicant needs strong experience with Python programming, including working with scientific data libraries. Familiarity with basic command-line tools and a willingness to learn Rust are essential; prior exposure to Rust or C++ is a plus

Supervisors:Ìý and Dr Sanaa HobeichiÌý

Uncertainties in rainfall estimates from a range of satellite retrievals challenge our understanding of small and large-scale processes associated with Earth’s rainfall as well as our efforts to validate precipitation simulated by Global Climate Models. The student will compare and analyse similarities and differences in satellite-based precipitation uncertainties between Southern and Northern hemispheres, focusing on mid-to-high latitudes. Simultaneously, this project will incorporate precipitation from reanalysis and CMIP6 models. Common biases in simulated precipitation with respect to the set of observational datasets will be identified for both hemispheres. The candidate will analyse variables such as annual mean, frequency and intensity of precipitation, normalized distributions, variance, extremes, seasonality and snow.

Supervisors:ÌýDr. Joaquin E Blanco and Prof. Lisa V. Alexander

Requirements:ÌýProgramming skills in Python are essential.

The El Niño-Southern Oscillation (ENSO) is the most prominent interannual variation in Earth’s climate system and thus a key driver of Australian climate variability. Different ENSO types—namely Eastern Pacific (EP) El Niño, Central Pacific (CP) El Niño, and La Niña—have distinct impacts on Australian rainfall, particularly in spring. This project introduces undergraduate students to climate science by analysing atmospheric moisture transport. We aim to understand rainfall and evaporation changes, as well as vertically integrated moisture flux under different ENSO types. This project is ideal for students eager to gain advanced knowledge of Australia’s climate, practical coding capability, and essential skills in quantitative climate research, contributing to uncovering the physical mechanisms behind the spring rainfall response to diverse ENSO events.

Supervisors:ÌýDr. Linyuan Sun and A/Prof. Andréa S. Taschetto

Requirements:ÌýNone