Undergraduate Research: An Archive - 2022 Program

CLIMATE AND
ENVIRONMENTAL SCIENCE
THESIS TITLE
Neural Network-based
Model to Describe
2-dimensional Tropical
Cyclone Wind Fields
ADVISER
Gabriel Vecchi,
Professor of
Geosciences and the
High Meadows
Environmental Institute
Ryan Eusebi ’22
COMPUTER SCIENCE
Certificate in Environmental Studies
Many existing studies have demonstrated the
power of machine learning-based models for
geoscience and environmental models, especially
those that attempt to predict or describe
geophysical fluid events. However, very little
research has used machine learning models to
describe or predict 2-dimensional hurricane
wind fields, which would benefit from fast and
accurate modeling. My research demonstrated
how a simple deep neural network using a small
subset of easy to forecast parameters could be
capable of describing 2-dimensional tropical
cyclone wind fields for storms simulated in a
high-resolution atmospheric model. The average
absolute value error of the network across the
grid of windspeeds for a given storm is about 2.1
meters per second (m/s) for tropical storms and
2.6 m/s for hurricanes. These figures outperform
common parametric models such as the Holland
vortex model by a factor of 2-3 depending on how
intense the storm is. The network also works well
when applied to a variety of climate scenarios,
and it tends to recover most of the important
structural and climatological features of tropical
cyclones. I presented analyses of the neural
network model to explain the patterns the neural
network is learning and how accurate those
learned relationships were.
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