1 research outputs found
Generative Modeling for Atmospheric Convection
To improve climate modeling, we need a better understanding of multi-scale
atmospheric dynamics--the relationship between large scale environment and
small-scale storm formation, morphology and propagation--as well as superior
stochastic parameterization of convective organization. We analyze raw output
from ~6 million instances of explicitly simulated convection spanning all
global geographic regimes of convection in the tropics, focusing on the
vertical velocities extracted every 15 minutes from ~4 hundred thousands
separate instances of a storm-permitting moist turbulence model embedded within
a multi-scale global model of the atmosphere.
Generative modeling techniques applied on high-resolution climate data for
representation learning hold the potential to drive next-generation
parameterization and breakthroughs in understanding of convection and storm
development. To that end, we design and implement a specialized Variational
Autoencoder (VAE) to perform structural replication, dimensionality reduction
and clustering on these cloud-resolving vertical velocity outputs. Our VAE
reproduces the structure of disparate classes of convection, successfully
capturing both their magnitude and variances. This VAE thus provides a novel
way to perform unsupervised grouping of convective organization in multi-scale
simulations of the atmosphere in a physically sensible manner. The success of
our VAE in structural emulation, learning physical meaning in convective
transitions and anomalous vertical velocity field detection may help set the
stage for developing generative models for stochastic parameterization that
might one day replace explicit convection calculations.Comment: 8 Pages, 6 Figures. Submitted to Climate Informatics 202