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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.

·¡³æ±è±ð°ù¾±±ð²Ô³¦±ð:ÌýTo complete this project experience with python is essential and experience with analysing large datasets is a plus.

Supervisors: Dr. Tim RaupachÌý²¹²Ô»å

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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.

·¡³æ±è±ð°ù¾±±ð²Ô³¦±ð:ÌýThe project requires Python programming skills in analysing data.

Supervisors:ÌýDr. Abhnil PrasadÌý²¹²Ô»åÌýProf. 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.

·¡³æ±è±ð°ù¾±±ð²Ô³¦±ð:ÌýÌýFamiliarity with Python and a basic understanding of thermal/convective dynamics is required for this project.

Supervisor: , and Prof Steven Sherwood and

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:ÌýÌý²¹²Ô»å 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.

Supervisor(s):ÌýJason Evans,ÌýUlrike Bende-Michl, Christian Stassen

Description:
Global climate models (GCMs) are essential for projecting long-term climate trends, but their coarse spatial resolution limits their ability to capture regional climate variability and extremes. To address this, high-resolution regional downscaling has been carried out over Australia,Ìýin collaboration with the Bureau of Meteorology, CSIRO, the NSW Department of Climate Change, Energy, the Environment and Water (DCCEEW), the University of New South Wales, and the University of Queensland.

This project will investigate the added value ofÌýthe fourÌýregionalÌýdownscaled simulationscompared to global CMIP6 models, with a focus on identifying improvements in the representation of climate extremes and regional patterns. The student will perform inter-model comparisons using output fromÌýtheÌýdownscaled simulations and GCMs, helping to evaluate the reliability and relevance of these projections for climate adaptation and planning.There will be an additional focus onÌýwhetherÌýbias correctionÌýwill influence the outcomes of theÌýadded valueÌýdatasets.

Through this work, the student will develop skills in analysing high-resolution climate datasets, interpreting model outputs, and working with scientific programming tools commonly used in climate science.

Experience required:
Basic proficiency in Python or another scientific programming language is required. Familiarity with climate data formats (e.g.,ÌýNetCDF) or experience working with large datasets is beneficial but not essential.

This project aims to develop an agentic AI tool for climate applications using NVIDIA AI platforms and tools, OpenAI’s agent builder, or similar frameworks. The focus will be on creating an intelligent chatbot capable of engaging, informative climate-related conversations. The student will experiment with approaches such as fine-tuning large language models or implementing retrieval-augmented generation (RAG) pipelines, exploring how different platforms perform in this context. We are looking for a self-driven student with a strong interest in artificial intelligence and climate science.

Experience:ÌýWe are looking for a self-driven student with a strong interest in artificial intelligence and climate science. A background in computer science, machine learning, or related fields is preferred, though enthusiasm and initiative are equally valued.

Supervisors:ÌýSanaa Hobeichi and Alex Sen Gupta