Supercomputers play an important role in emergency preparation, disaster recovery, and prevention of natural hazards becoming disasters. They enable high-speed simulations, real-time data assimilation, geospatial analysis, visualization, machine learning and assessment of infrastructure resiliency. Specifically, supercomputers and experts at UT Austin were able to accurately forecast Hurricane Harvey and Irma's impacts. They also help improve storm forecasts through data assimilation of weather observations. Additionally, machine learning techniques can be used to automatically search imagery for mosquito breeding grounds to help fight Zika virus spread.

1. 3WAYS
SUPERCOMPUTERS
HELP PREVENT NATURAL HAZARDS FROM BECOMING

2. Whether it’s a natural disaster like
Hurricane Harvey or a man-made
disaster like the Deepwater Horizon oil
spill, in times of crisis, supercomputers —
and supercomputing centers – play an
important role in emergency
preparations, disaster recovery and
prevention.
By enabling high-speed simulations, real-
time data assimilation, geospatial
analysis, visualization, machine learning
and infrastructure resiliency,
supercomputers have an outsized, real-
world impact when natural hazards
occur.
INTRODUCTION

3. WAYS
Forecasting
storms before
they strike
Data drives
better models
Machine learning
and the frontiers
of forecasting

4. F O R E C A S T I N G
S T O R M S
B E F O R E T H E Y
S T R I K E
Access to massive
supercomputers,experts at
The University of Texas at
Austin (UT Austin),
working with collaborators
at LSU and the University
of North Carolina, could
accurately predict where
and when Hurricanes
Harvey and Irma would hit
the Texas and Florida
coasts and how damaging
they would be.

5.
DATA DRIVES
BETTER MODELS
Another way that
TACC’s systems help
improve storm
forecasts is through
data assimilation,
which incorporates
observations from
weather stations,
radar systems and
sensors into the
numerical model of a
storm in real-time.

6. At a TACC-hosted hackathon for
scientists interested in applying
advanced computing to fight the spread
of the Zika virus, researchers used
machine learning techniques to
automatically search aerial imagery for
pools of stagnant water, which serve as
potential breeding ground for mosquitos
that carry Zika.
MACHINE
LEARNING
AND THE
FRONTIERS
OF
FORECASTING

7. THANK YOU!
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