Confronting Climate Change with Generative and Self-supervised Machine Learning

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Confronting Climate Change with Generative and Self-supervised Machine Learning

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    The stunning recent advances in AI chatbots rely on cutting-edge generative deep learning algorithms and architectures trained on massive amounts of text data. Generative deep learning has also shown remarkable results when trained on video data and on combinations of different data types (i.e., multi-modal). The recent advances in generative deep learning can also benefit a variety of applications for addressing climate change. For example, generative deep learning trained on climate and weather data can be a powerful tool in generating an ensemble of weather predictions and in quantifying the uncertainty of long-term projections of climate change.

    As opposed to text and video, the relevant training data for this domain includes weather and climate data from observations, reanalyses, and even physical simulations. As in many massive data applications, creating “labeled data” for supervised machine learning is often costly, time-consuming, or even impossible. Fortuitously, in very large-scale data domains, “self-supervised” machine learning methods are now actually outperforming supervised learning methods. In this lecture, I will survey our lab’s work developing generative and self-supervised machine learning approaches for applications addressing climate change, including detection and prediction of extreme weather events, and downscaling and temporal interpolation of spatiotemporal data. Our methods address problems such as forecasting the path and intensity of tropical cyclones, renewable energy planning, and projecting future sea-level rise.

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