top of page
Student Researcher at Google LLC and Earth System Science Ph.D. Candidate at UC Irvine
CV | Google Scholar | ResearchGate | Github
Generative Modeling of Atmospheric Convection
VAEs for Convection Representation: Text
How reliable is unsupervised learning on a high-resolution climate simulation? Can it provide any novel insights into convection physics or be used to emulate stochastic parameterizations in climate models? We attempt to answer these questions by building a Variational Autoencoder (VAE) and training it on cloud-resolving vertical velocity fields.
VAEs for Convection Representation: Research
VAEs for Convection Representation: Image
Not only does our VAE learn to reconstruct the details of convection, but it sorts the test vertical velocity fields in a physically sensible way on just two components in the latent space:
VAEs for Convection Representation: Research
VAEs for Convection Representation: Research
Our VAE latent space also captures expected physics such as the diurnal cycle, which loops around the space over the course of several days in the Amazon Rainforest
VAEs for Convection Representation: Research
VAEs for Convection Representation: Research
Collaborators
VAEs for Convection Representation: Team Members
VAEs for Convection Representation: Pro Gallery
bottom of page