Generative Modeling for Atmospheric Convection

• URL: http://arxiv.org/abs/2007.01444v2
• Date: Sat, 24 Oct 2020 22:43:46 GMT
• Title: Generative Modeling for Atmospheric Convection
• Authors: Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael Pritchard, Tom Beucler
• Abstract summary: We explore the potential of generative modeling to cheaply recreate small-scale storms by designing and implementing a Variational Autoencoder (VAE) VAE performs structural replication, dimensionality reduction, and clustering of high-resolution vertical velocity fields on 6*106 samples spanning the globe. It successfully reconstructs the spatial structure of convection, performs unsupervised clustering of convective organization regimes, and identifies anomalous storm activity.
• Score: 13.104272504735052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources. Here, we explore the potential of generative modeling to cheaply recreate small-scale storms by designing and implementing a Variational Autoencoder (VAE) that performs structural replication, dimensionality reduction, and clustering of high-resolution vertical velocity fields. Trained on ~6*10^6 samples spanning the globe, the VAE successfully reconstructs the spatial structure of convection, performs unsupervised clustering of convective organization regimes, and identifies anomalous storm activity, confirming the potential of generative modeling to power stochastic parameterizations of convection in climate models.

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