Dr Sanaa Hobeichi
Senior Research Associate
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PhD Climate Science |听91色情片 Sydney听
MSc Environmental Sciences -听 Major Remote Sensing |听Qatar University
BSc Applied Mathematics - Major Computer Science |听Lebanese University
Science
Weather of the 21st Century
Sanaa Hobeichi is a Senior Research Associate at the Climate Change Research Centre and the ARC Centre of Excellence for the weather of the 21st Century. She is interested in applying machine learning (ML) to advance climate and weather research. Her research focuses on using ML in climate downscaling and improving the prediction of weather and climate extremes.听
Sanaa is the co-chair of the .听
Sanaa has a background in Climate Science, Environmental Science, Applied Mathematics, and Computer Science and she is a former International Baccalaureate teacher.
E-mail
- Publications
- Media
- Grants
- Awards
- Research Activities
- Engagement
- Teaching and Supervision
- 2026-2028 Office of National Intelligence Grant: Accelerating climate intelligence provision for risk assessment using machine learning and artificial intelligence.听Chief Investigators: Andy Pitman, Anna Ukkola, Sanaa Hobeichi, Elisabeth Vogel, Scott Sisson
- 2026 Faculty of Science Research Grant: Is Equation Discovery an effective Machine Learning approach for Climate Science? A test case in drought modelling
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Machine learning for climate downscaling
This body of work applies machine learning to improve the spatial representation of climate variables, benchmark ML approaches against dynamical downscaling, and reduce the computational cost of regional climate simulations.
Selected publications:
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Hobeichi, S. et al. (2026). Applying a standardised benchmarking framework to evaluate AI methods for precipitation downscaling over Australia. Artificial Intelligence for the Earth Systems.听
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Rampal, N., Hobeichi, S. et al. (2025). A Reliable Generative Adversarial Network Approach for Climate Downscaling and Weather Generation. Journal of Advances in Modeling Earth Systems.
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Rampal, N., Hobeichi, S. et al. (2024). Enhancing Regional Climate Downscaling through Advances in Machine Learning.听Artificial Intelligence for the Earth Systems.听听
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Nishant, N., Hobeichi, S. et al. (2023). Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation. Environmental Research Letters.听
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Hobeichi, S. et al. (2023). Using Machine Learning to Cut the Cost of Dynamical Downscaling. Earth's Future.
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Machine learning in drought research
Machine learning is used here to improve drought characterisation, prediction, and interpretation
Selected publications:
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Hobeichi, S. et al. (2022). Toward a Robust, Impact-Based, Predictive Drought Metric. Water Resources Research.
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Devanand, A., Hobeichi, S. et al. (2024). Australia's Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change. Science Advances.
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Grant, M. O., Hobeichi, S. et al. (2025). Historical trends of seasonal droughts in Australia. Hydrology and Earth System Sciences.听
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Holgate, C. M., Hobeichi, S. et al. (2025). Physical mechanisms of meteorological drought development, intensification and termination: an Australian review. Communications Earth & Environment.听
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Machine learning in renewable energy research
- Richardson, D., Hobeichi, S. et al. (2025). Predicting Australian energy demand variability using weather data and machine learning. Environmental Research Letters
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Machine learning in paleoclimate research
- Falster, G., Hobeichi, S. et al. (2026). High resolution monthly precipitation isotope estimaes across Australia from machine learning. EGUsphere.
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Large-scale climate modes and rainfall predictability using machine learning
- Hobeichi, S. et al. (2024). How well do climate modes explain precipitation variability?听npj Climate and Atmospheric Science.
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Historical changes in hydrological and energy budgets
This body of work investigates historical changes in the terrestrial hydrological cycle and land鈥揳tmosphere energy exchanges, using observational synthesis products, energy and water budget closure, and model evaluation frameworks.
Selected publications:
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Hobeichi, S. et al. (2022). Reconciling historical changes in the hydrological cycle over land. npj Climate and Atmospheric Science.
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Hobeichi, S. et al. (2021). Robust historical evapotranspiration trends across climate regimes. Hydrology and Earth System Sciences.听
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Hobeichi, S. et al. (2020). Evaluating precipitation datasets using surface water and energy budget closure. Journal of Hydrometeorology.听
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Hobeichi, S. et al. (2020). Conserving land-atmosphere synthesis suite (CLASS). Journal of Climate.听
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Hobeichi, S. et al. (2019). Linear Optimal Runoff Aggregate (LORA). Hydrology and Earth System Sciences.听
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Hobeichi, S. et al. (2018). Derived Optimal Linear Combination Evapotranspiration (DOLCE). Hydrology and Earth System Sciences.听听
Scientific datasets (DOLCE, LORA, and CLASS)
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Hobeichi, S. et al. (2021). Derived Optimal Linear Combination Evapotranspiration - DOLCE v3.0. NCI National Research Data Collection.
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Hobeichi, S. et al. ( 2019). Conserving Land-Atmosphere Synthesis Suite (CLASS) v1.1. NCI National Research Data Collection.
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Hobeichi, S.听et al. (2018).听Linear Optimal Runoff Aggregate (LORA) v1.0 . NCI National听Research Data Collection.
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Hobeichi, S. et al. (2017). Derived Optimal Linear Combination Evapotranspiration v1.0 . NCI National听Research Data Collection.
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My Research Supervision
Husnain Asif - PhD candidate at Australian National University
Thesis: Advancing climate model downscaling for southeast Australia by integrating latent diffusion models with NARCliM 2.0 ensemble
Supervising with: Prof. Sarah Perkins-Kirkpatrick,听 Prof. John Taylor
Yuan Zhuang - PhD candidate at 91色情片 Sydney
Supervising with A/Prof. Fei Huang and Prof. Pen Shi
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Yajat Goswami - MPhil candidate at 91色情片 Sydney
Thesis: Can AI learn Hydrology? Evaluating Physics-based and AI runoff & streamflow simulations across Australia's river basins
Supervising with: Prof. Lisa Alexander
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