When Tropical Storm Melissa hovered
south of Haiti, Philippe Papin at the U.S. National
Hurricane Centre had a
strong feeling that it was about to strengthen rapidly.
As the lead forecaster
at the time, he issued a bold early alert. Within a day, Melissa would
shoot up
to a Category 4 storm. Of course, it turned toward Jamaica.
It was one of the agency’s
clearest and most decisive rapid-intensification warnings in years. A big part of that confidence came
from a brand-new tool. Google’s
DeepMind hurricane-forecasting AI.
This AI had
only recently gone into real-world use. It was showing a sharp, sudden jump in the
storm’s strength. Papin trusted what he saw. As we know, Melissa ended up proving the
AI right. It moved through Jamaica with a huge force.
Setting a
New Bar for Forecast Accuracy
DeepMind’s model is the first AI
system designed specifically for hurricane prediction. It is already
outperforming the traditional numerical models that meteorologists have used
for decades.
Across all 13 Atlantic storms this year, it consistently delivered
the best results. In many cases, its storm - track predictions even beat those of forecasters.
Melissa eventually hit land as a
Category 5 hurricane. It was one of the strongest storms Jamaica has seen in about 200 years. Papin’s early call gave people on the island precious time to
prepare. This likely lowered both the damage and the number of injuries.
How This AI
Actually Works
Google has spent years building
machine - learning weather tools. This model is based on DeepMind’s
larger forecasting system. The AI does not go through a long list of equations like traditional models.
It simply studies a huge amount of past storm data. It then picks up patterns that these older systems might overlook. One of its biggest advantages is
speed.
Former NHC forecaster Michael Lowry points out that the AI can deliver
forecasts in minutes. It runs on regular PCs and does not need pricey supercomputers. After just one season of use, he says it has already shown it
can match, and sometimes beat, the long-trusted physics-based models.
Still Some
Gaps
Despite this feat, the DeepMind system isn’t perfect.
Retired NHC forecaster James Franklin notes that the AI has struggled with a
few extreme cases. It struggled with the sudden intensity spikes during Hurricane Erin and
Typhoon Kalmaegi.
Even so, he thinks the model’s success is real, not a fluke.
He hopes to work with Google in the off-season. This is to help make the tool even more
practical for forecasters.
One of the big issues is improving transparency. The model still operates like a “black box,” with limited insight into how
it makes decisions.
A Shift in
the Weather-Forecasting World
It’s unusual for a private company
to lead this kind of work. Most top weather models are government-built. They are also mostly open to researchers.
But Google does publish DeepMind’s forecast outputs. It is its private property. Thus, the details behind the system remain mostly under wraps.
Meanwhile, U.S. and European
weather agencies are racing to develop their model. These will be AI-based models just like DeepMind. Early results suggest they are making some progress.