A Machine-Learning Assist to Predicting Hurricane Intensity

research could help to improve forecasts of whether a hurricane will suddenly
intensify, which could give people in its path more time to prepare.

In October 2015,
Hurricane Patricia in the Northeast Pacific Ocean blew up from a Category 1 storm
into a Category 5 monster within 24 hours, its winds leaping from 86 mph (138
kph) to 207 mph (333 kph). Patricia wasn’t the first or the last hurricane to
suddenly strengthen in such a short time – but it was a spectacular
demonstration of a phenomenon that has plagued meteorological forecasts for

Accurately predicting
whether a hurricane will undergo rapid intensification – where wind speeds
increase by 35 mph (56 kph) or more within 24 hours – is incredibly difficult. But
researchers led by scientists at NASA’s Jet Propulsion Laboratory in Southern
California have used machine learning to develop an experimental computer model
that promises to greatly improve the accuracy of detecting rapid-intensification

“It’s an
important forecast to get right because of the potential for harm to people and
property,” said Hui Su, an atmospheric scientist at JPL. She and her
colleagues, including a researcher at the National Oceanic and Atmospheric
Administration’s National Hurricane Center, described their forecast model in a
paper published on Aug. 25 in the journal Geophysical Research Letters.

Eyeing the
Inner Workings

There are two
parts to a hurricane forecast: its track and its intensity. Scientists and
forecasters have gotten very good at predicting where a hurricane will make
landfall. But forecasting its strength still gives them trouble because it
depends on the surrounding environment as well as what’s happening inside these
storms. Properties such as how hard it’s raining or how quickly the air is
moving vertically are challenging to measure inside a hurricane.

It’s also difficult
to determine which internal characteristics result in rapid intensification of these
storms. But after sifting through years of satellite data, Su and her
colleagues found that a good indicator of how a hurricane’s strength will
change over the next 24 hours is the rainfall rate inside the storm’s inner
core – the area within a 62-mile (100-kilometer) radius of the eyewall, or the
dense wall of thunderstorms surrounding the eye. The harder it’s raining inside
a hurricane, the more likely the storm is to intensify. The team gathered this rainfall
data from the Tropical Rainfall Measuring Mission, a joint satellite project between NASA
and the Japanese Aerospace Exploration Agency that operated from 1997 to 2015.

In addition, the
researchers found that changes in storm intensity depended on the ice water content
of clouds within a hurricane – measurements they gathered from NASA’s CloudSat
observations. The temperature of the air flowing away from the eye at the top
of hurricanes, known as outflow temperature, also factored into intensity
changes. Su and her colleagues obtained outflow temperature measurements from NASA’s
Microwave Limb Sounder (MLS) on the Aura satellite as well as
from other datasets.

More Power
to Learn

The team added
the rainfall rate, ice water content, and outflow-temperature predictors to the
ones the National Hurricane Center already uses in its operational model to
come up with their own predictions via machine learning. There are so many
variables inside a hurricane, and they interact in such complex ways, that many
current computer models have trouble accurately depicting the inner workings of
these storms. Machine learning, however, is better able to analyze these
complex internal dynamics and identify which properties could drive a sudden
jump in hurricane intensity. The researchers used the computational algorithm capabilities
of the IBM Watson Studio to develop their machine learning model.

Then they
trained their model on storms from 1998 to 2008 and tested it using a different
set of storms, from 2009 to 2014. Su and her colleagues also compared the
performance of their model with the National Hurricane Center’s operational
forecast model for the same storms from 2009 to 2014.

For hurricanes
whose winds increased by at least 35 mph (56 kph) within 24 hours, the researchers’
model had a 60% higher probability of detecting the rapid-intensification event
compared to the current operational forecast model. But for those hurricanes
with winds that jumped by at least 40 mph (64 kph) within 24 hours, the new
model outperformed the operational one at detecting these events by 200%.

Su and her
colleagues, including collaborators at the National Hurricane Center, are
testing their model on storms during the current hurricane season to gauge its
performance. In the future, they plan to sift through satellite data to find additional
hurricane characteristics that could improve their machine learning model. Predictors
such as whether it’s raining harder in one part of a hurricane versus another
could give scientists a better look at how the storm’s intensity might change
over time.

News Media Contact

Jane J. Lee / Ian J. O’Neill
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 818-354-2649
jane.j.lee@jpl.nasa.gov / ian.j.oneill@jpl.nasa.gov


Source: Jet Propulsion Laboratory

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