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ARIA Opportunity Space: Scoping Our Planet
AI for Atmospheric Systems
Developing an interpretable foundation AI model capable of generating and analyzing cloud structures and their behavior in novel aerosol-cloud interactions and climate feedback scenarios. Leveraging recent breakthroughs in conditional generative AI, such as those used for biological structure prediction and video generation, the model will integrate multi-modal, high-resolution satellite data. The resulting model will generate and reconstruct cloud structures under varying environmental conditions, significantly reducing uncertainties in climate prediction and facilitating the detection and quantification of cloud feedbacks across the satellite record.
Tim holds a PhD in ML and Statistics from Oxford. He was previously a Postdoc in the Climate Processes group at the University of Oxford, developing novel compression algorithms for atmospheric data. He has broad interests in probabilistic machine learning, Bayesian statistics, and deep learning.
Philip Stier
Professor of Atmospheric Physics
Philip Stier leads the Climate Processes Research group at Oxford University, specializing in aerosol-cloud interactions, cloud feedbacks, and climate modeling. His work is pivotal in addressing uncertainties related to clouds in climate prediction and has significantly contributed to global atmospheric research initiatives.
