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  • title: An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes
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            An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes
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            An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes

            Dec 2, 2022

            Speakers

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            Griffin Mooers

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            Tom Beucler

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            Stephan Mandt

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            About

            Despite the importance of quantifying how the spatial patterns of extreme precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an unsupervised machine learning framework to quantify how storm dynamics affect precipitation extremes and their changes without sacrificing spatial information. Over a wide range of precipitation quantiles, we find that the spatial patterns of extreme precipitation…

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            NeurIPS 2022

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