<div class="csl-bib-body">
<div class="csl-entry">Hobeichi, S., Curran, D. J., Sun, Y., Bittner, M., Isphording, R., & Alexander, L. (2025, June 3). <i>Comparison of State-of-the-Art Machine Learning Models with Dynamical Downscaling for Australian Precipitation Using a Standardised Benchmark Framework</i> [Conference Presentation]. EXCLAIM Symposium, Zürich, Switzerland. http://hdl.handle.net/20.500.12708/216639</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/216639
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dc.description.abstract
The recent surge in machine learning (ML) methods has opened new possibilities for improving the resolution of coarse global climate model (GCM) simulation outputs through downscaling. These advancements aim to capture critical aspects of climate variability and change at regional scales, offering computationally efficient alternatives to traditional regional climate models (RCMs). This study presents a comparative analysis of precipitation outputs from state-of-the-art machine learning models including a Vision Transformer, Diffusion Model, Spherical Fourier Neural Operator and a Long Short-Term Memory model, and RCM simulations from the CORDEX-Australasia ensemble. Using a standardised benchmark framework, we evaluate model skills across diverse precipitation characteristics, leveraging Australia’s highly variable and geographically diverse climate as a test bed. Our findings indicate that ML-based climate downscaling performs within the range of dynamical models across all evaluated metrics and climate regimes, with the added advantage of significantly reduced computational costs. Notably, the optimal ML model varies depending on the metric and climate regime, underscoring the value of an ensemble approach. State-of-the-art ML models have the potential to augment regional precipitation ensembles, complementing dynamical downscaling to better quantify and characterise precipitation changes under climate variability and change.
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dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.subject
Dynamical Downscaling
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dc.subject
Standardised Benchmark Framework
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dc.title
Comparison of State-of-the-Art Machine Learning Models with Dynamical Downscaling for Australian Precipitation Using a Standardised Benchmark Framework
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dc.type
Presentation
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dc.type
Vortrag
de
dc.contributor.affiliation
Australian Research Council Centre of Excellence for the Weather of the 21st Century, Australia
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dc.contributor.affiliation
UNSW Sydney, Australia
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dc.contributor.affiliation
National Computational Infrastructure, Australia
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dc.contributor.affiliation
Australian Research Council Centre of Excellence for the Weather of the 21st Century, Australia
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dc.contributor.affiliation
Australian Research Council Centre of Excellence for the Weather of the 21st Century, Australia
