1.1k citations · 1.9k across the 16 of their papers we have counts for
3 papers · 1 filter
Understanding Extreme Precipitation Changes through Unsupervised Machine Learning
Griffin Mooers, Tom Beucler, Mike Pritchard +1
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 mo…
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
Griffin Mooers, Mike Pritchard, Tom Beucler +5
Global Storm-Resolving Models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult…
Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Gunnar Behrens, Tom Beucler, Pierre Gentine +3
Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…