Hi,
This month’s meeting of the Machine-Learning Working Group will take place this Friday. Meeting will be at the usual time, 2pm (AEDT), and we will have a talk by Belinda Trotta (BoM), ML for post-processing at BoM.
Zoom details: Zoom link
Meeting ID: 831 8730 7759
Password: 123456
Agenda
Facilitator: Ryan Holmes
Co-chairs: Tennessee Leeuwenburg, Sanaa Hobeichi, Vassili Kitsios, Ryan Holmes (outgoing), Micael Oliveira (ACCESS-NRI liaison), Taimoor Sohail (incoming), and Yue Sun (NCI liaison)
- Acknowledgement of country
- Updates from ACCESS-NRI
- Release of the ACCESS-NRI’s work plan for FY26-27, first draft. Seeking feedback from the community. Please see the announcement for details.
- Updates from NCI
- New models available
- New dk92 environment anemoi/26.04
- Updates from the Chairs
- Taimoor Sohail is going to replace Ryan as a group co-chair.
- ACCESS-NRI Connecting Machine Learning to Earth System Science workshop happening in August. Abstracts due 15 May. See ACCESS Workshop: Connecting Machine Learning to Earth System Science, 19-21 August 2026 | ACCESS-NRI
- Feedback on potential training and discussion sessions due on the 3rd of May Microsoft Forms
- Gauging interest in community knowledge exchange on generative AI assistants
- Updates from the Community & new community member introductions
- Presentation by Belinda Trotta (BoM) (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:
- Coarsening of the fine-scale atmosphere for cross-scale learning. Outputs: Coarse-grained fields and tendencies including fine-scale transports of prognostic variables. nm47 resources: 50TB - two months
Details of this month’s presentation:
Presenter: Belinda Trotta | Research Scientist at the Bureau of Meteorology
Title: ML for post-processing at BoM
Abstract: This talk will describe how the Forecast Improvement team uses machine learning for post-processing weather forecasts. I will focus particularly on Rainforests, our ML model for post-processing rainfall, and describe the current production version, as well as some enhancements we are developing (neural network version, extension to AIFS input). I’ll also discuss our work on applying statistical calibration methods to ML weather models, and some very recent work training a simple version of Nvidia’s ModAFNO model for temporal interpolation.
Biography: Dr Belinda Trotta is a research scientist in the Forecast Improvement team at BoM. Her research focuses on forecast calibration, with an emphasis on machine learning techniques. She led development of Rainforests, the Bureau’s new ML rainfall calibration methodology, which won the award for Best Scientific Publication of 2024. Belinda holds a PhD in pure mathematics from La Trobe University.