weather predictions in the northeast

of the U.S. sounded like this.

All eyes are on Hurricane Lee.

The storm has strengthened back to a category 3.

We're expecting it to make

a northward turn over the next few days.

By September 16th, after being

downgraded, storm Lee made

landfall in Nova Scotia, Canada,

flooding roads, downing trees and

cutting out power for tens of

thousands of people along the East Coast.

At least five days before Hurricane Lee struck

land, weather forecasts had roughly

predicted its trajectory.

But another forecast beat them

three days before weather stations,

an AI model created by Google

predicted the cyclone's path.

The AI revolution has reached

meteorology, and it's at a

time when we are responding to

extreme weather more than ever.

We're about to find out if it will

help us prepare by bringing the

future into clear view.

Predicting future weather more than

a few hours out starts with creating

a snapshot of Earth's current

atmosphere.

Scientists do that by collecting

data from sources like satellites

and weather stations and buoys

located around the world, taking

pictures of clouds and measuring

temperature and pressure and wind

speed and humidity.

All that disparate data gets

fed into computers, which generate

a 3D grid of boxes that represent

the atmosphere, both vertically and

horizontally.

Computers then do a lot of physics

to determine how these conditions

interact with each other, and they

produce a forecast.

I think any forecast has like

150 trillion calculations.

It's pretty amazing.

All that math requires some of the

world's most powerful

supercomputers.

The two big ones are run by the

European Centre for Medium-Range

Weather Forecasts and the National

Weather Service in the U.S.

To make a local weather forecast

from this global model,

meteorologists zoom in and refine

their own forecasts with their local

expertise.

Like if they live in a hilly area

or a flat area or near a lake,

they'll adjust those models and do

their own professional

interpretation based on their

area.

No matter what, this initial 3D grid

of that sphere is never going to

exactly replicate reality.

There's too many gaps in the data we

can measure.

That means forecasts get blurrier

the further out you go.

Which is why the big weather centers

don't just generate one forecast.

They tweak the initial data and

produce up to 50 forecasts.

It's called ensemble forecasting,

and it helps meteorologists measure

uncertainty.

If all 50 forecast looks similar,

there's a higher certainty in the

prediction. But if there's a lot of

variation there's much less.

We got to kind of keep an eye to the

sky. There's a potential of another

storm in the works.

This went from 0% chance to

40% at two o'clock,

70% at 8:00,

and at 11:00.

We have a tropical storm.

Weather centers only release their

forecasts every six hours.

Because that's all today's computing

power will allow.

But what if that limit didn't exist?

Before we explain that, we'll hear

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Now back to our video.

In 2020, a group of researchers

published a data set called Era5

five, which contained about 40 years

of the Earth's hourly weather data.

The data set was just primed for

using AI because it was huge.

The data is nice and smooth,

there's no missing values, and

it's free just to step in.

And say, "Hey, you have terabytes of data?

I can learn how weather moves."

AI models learned how weather moves,

not through applying trillions of

physics equations to the Earth's

atmosphere, but by being trained

on Era5's enormous historical

data set.

Researchers gave the models a

snapshot of weather conditions,

ask them to make a prediction, and

then scored them on how closely that

prediction matched what really

happened.

After a while, the models eventually

got really good at this.

By 2023, the tech companies

Google, Huawei and Nvidia

had developed models that rivaled

traditional forecasting on variables

like surface temperature, humidity

and wind speed, and on some extreme

weather events like the paths of

tropical cyclones, atmospheric

rivers, and extreme temperatures.

These AI models still rely on the

same observation data from the big

weather centers, the data that

creates that initial 3D grid

snapshot.

But they don't require anything

close to six hours to produce a

prediction. Huawei's PanguWeather

model, for example, can produce a

week long forecast in 1.4 seconds.

Which means that we spent over a century

figuring out the physics, the

atmospheric science and the

computational skill to bring us our

modern day weather forecast.

And now suddenly we have these AI

models that have come out of, you

know, the past 2 or 3 years,

and they're getting the same skill,

and now they run on a

modest laptop.

Despite the impressive results from

these first AI models, there's still

lots of work to do.

Google's GraphCast predicted

Hurricane Lee's path faster than

traditional models, but it didn't

prove it could predict a hurricane's

intensity, which is a trickier

calculation to make.

These AI models are incentivized to

get as many correct answers as they

can through the scoring system.

If you swing for it, swing for the

fences, right?

If it misses, the model is

penalized very large. It says "No, you

should never do that.

Don't swing for the fences because

the error is going to be huge."

But by prioritizing safer,

correct answers to boost the model's

score, it could miss rare,

outlier weather events.

Plus, they are learning from 40

years of history, and historical

weather has fewer extreme events

than we do today or will have in

the future due to climate change.

But a big reason for optimism with

these AI models comes from their

ensemble forecasting.

Instead of the traditional 50

ensemble forecasts, they can predict

a thousand or more because they're

freed from computing and time

constraints.

There's always going to be

uncertainty in a weather prediction,

but larger ensembles will help us

measure that uncertainty better.

That's an extremely useful context

to say you are a emergency manager

down in Florida who's dealing with

the very difficult decision:

Are you going to, you know, order an

evacuation or not?

You want as much information about

the uncertainty as possible.

Large ensembles might also catch

a rare weather event that a 50-member ensemble would miss.

Or, measure the probability of

weather events even further into the

future than our 10 day forecast.

Are we magically going to get a

crystal ball that lets us foresee

perfectly into the future?

Probably not.

I think especially on like sub

seasonal timescales, like multiple

months out, we're going to be able

to frame the statistical question

with a lot more specificity

and probably a much better

quantification of the uncertainty.

We do have a new winter storm

warning.

One thing we shouldn't

expect to change any time soon is

the role of the meteorologist...

at least the ones you see on TV.

If only because we fundamentally

have to communicate uncertainty, and

we have to walk through all the

various what-ifs.

And a human is the best tool that we

have today to effectively

communicate that and help somebody

else make a decision.

AI forecasting models are still in

an experimental phase, but the

European Centre for Medium-Range

Weather Forecasts has started

publishing AI forecast alongside

their traditional ones for the

public to compare.

When we check the weather in the

very near future, it might be

powered by AI instead of physics

based models or a combination

of the two.

And if we get things right, we'll

have a sharper view of the weather

events that we need to prepare for

the most.