Hi,
This Friday we will resume the Machine-Learning Working Group monthly meetings. Meeting will be at the usual time, 2pm (AEDT), and we will have a talk by Skye Williams-Kelly (UNSW), Interpolating daily in situ precipitation observations with Bayesian Neural Fields.
Zoom details: Zoom link
Meeting ID: 831 8730 7759
Password: 123456
Agenda
Facilitator: Yue Sun (NCI liaison)
Co-chairs: Tennessee Leeuwenburg, Sanaa Hobeichi, Vassili Kitsios, Ryan Holmes, and Micael Oliveira (ACCESS-NRI liaison)
- Acknowledgement of country
- Updates from ACCESS-NRI
- Self-nominations for the ACCESS-NRI Scientific Advisory Committee (SAC) - Self-nominations for the ACCESS-NRI Scientific Advisory Committee (SAC)
- Updates from NCI
- FCN3 inference notebook available
- HPC for large scale weather prediction and climate modeling hosted by CCRS, RIKEN R-CCS, and NIES Japan. updates from SCA26
- Updates from the Chairs
- AMOS 2026 - Session: 10. Advancing weather, climate and ocean research with AI and machine learning - 17th February 2026, after the afternoon tea.
- Updates from the Community & new community member introductions
- Presentation by Skye Williams-Kelly (UNSW) (Details provided below).
- Discussion (ongoing, dependent on time available in meeting):
- Plans for using project nm47 (100TB gdata storage, 875kSU/quarter) on NCI.
- Proposed projects:
- Sanaa Hobeichi to briefly discuss his proposal: Experiment Proposal: Multisource multiscale AI fusion framework for observationally constrained global climate datasets
Details of this month’s presentation:
Presenter: Skye Williams-Kelly | PhD Candidate | UNSW Sydney - ARC CoE for the Weather of the 21st Century
Title: Interpolating daily in situ precipitation observations with Bayesian Neural Fields
Abstract: Accurate precipitation predictions are vital for water resource management and risk mitigation. Interpolated precipitation estimates derived from in situ observations are frequently used to evaluate climate models and analyse trends. However, these inadequately represent its spatio-temporal characteristics and significantly smooth out extremes, inhibiting effective evaluation of dynamical models and analysis of trends. Machine learning methods may be suited to addressing these limitations due to their ability to identify patterns in large datasets and use of GPU acceleration. Here I discuss results for one such method, the Bayesian neural field, which was inspired by the frequently used Gaussian Process and kriging methods. Its performance is evaluated using traditional and distributional metrics, including on out-of-sample prediction. Results are further compared against existing precipitation products to identify the relative strengths and limitations of each method.
Biography: Skye is a 2nd year PhD candidate at the Climate Change Research Centre, UNSW Sydney. Following a bachelor’s degree in data science, including an Honours project on multivariate Bayesian fitting methods, she briefly worked in data engineering before returning to complete her PhD. Her current research is looking at machine learning and Bayesian methods for interpolating precipitation, with a focus on better characterising extremes.