As climate change intensifies extreme
weather, accurate forecasting is becoming
more imperative than ever.
The US experienced 18 separate weather or
climate disasters in 2022 alone, costing a
total of $165 billion in damages.
The disasters were also deadly, causing at least
474 direct or indirect fatalities.
Tracking weather and disseminating forecasts
in the US has historically been the job
of government agencies like the National Oceanic
and Atmospheric Administration or NOAA.
But as climate change makes weather disasters
deadlier and more expensive, a new
generation of private companies has cropped up
to provide paying customers with
hyperlocal, hyperspecific weather
forecasting.
What Tomorrow is trying to do is to help
customers, either countries or businesses,
manage their weather-related
challenges by giving them weather intelligence
software. Software that tells them exactly what
to do in relation to their job and their
industry.
The weather forecasting services industry in the
US was estimated to be worth $17.4 billion in
2023.
The economic value of the forecast has always kind
of far exceeded how much it costs to produce.
But in an era of climate extremes, that becomes
even more true. Which is also why you see a lot of
the impulse towards private companies to
capitalize on that.
In an effort to get ahead of severe weather, NOAA
is also increasingly turning to private
partnerships. The organization already
works with the private sector to build some of
its infrastructure, like weather satellites and
the supercomputers that it uses to generate
forecasts. Now, NOAA is exploring buying weather
data as a service.
It's an effort that gained momentum in 2017
with a new law that encourages the private
sector to generate weather data that the
government can use to beef up its forecasts.
Society's needs for actionable and
intelligible weather and environmental information
is growing at such a rapid pace with the
number of billion dollar disasters and the
duration between them growing shorter for any
one entity to meet alone.
Private weather companies are likely here to stay,
leading some to wonder how those who can't
afford their services will be affected.
Weather forecasting has come a long way from its
humble beginnings, when volunteers would send
weather reports from all over the country by
telegraph to the Smithsonian Institution
in Washington, D.C., which generated a
national weather map.
Meteorologists kind of improved their
understanding of the atmosphere, you know, in
the sort of 100 years between those initial
telegraph forecasts and World War II.
Beginning in the 1950s and 60s with computers
and satellites, up until today, you have this
incredible improvement where for kind of every
decade, the weather forecast improves by a
day. So today's five-day forecast is as good as
the three-day forecast was 20 years ago.
In the US, the National Weather Service, which is
part of NOAA, releases about 1.5 million
forecasts and 50,000 warnings each year as
part of its mandate to protect life and
property. One part of the NWS is made up of
local offices, which are in charge of day-to-day
weather forecasting and issuing severe weather
warnings to their communities. The other
part is made up of national centers such as
the National Hurricane Center, which focuses on
tracking hurricanes and tropical cyclones, and
the Storm Prediction Center that focuses on
tracking severe thunderstorms, tornadoes
and wildfires.
Weather forecasting is an immense task that begins
with collecting more than 6.3 billion
observations per day of metrics like temperature,
wind speed and moisture.
Over 95% of these observations come from
satellites. But weather balloons, ground-based
weather stations, ground-based radar and
buoys all contribute.
NOAA also shares and pulls observation data
from foreign governments. All this
information is fed into powerful supercomputers
that can each crunch a quadrillion calculations
per second to produce weather models, which are
basically numerical representations of the
atmosphere that meteorologists use to
come up with forecasts.
We can forecast weather skillfully, which means
that when we put out a forecast, it would be
more informative than just regurgitating the
average conditions on that particular day out
to about 7 to 8 days.
Beyond that, it's a little more challenging.
There's always going to be some imprecision and
some error in our current picture of the
atmosphere, and that's because we don't have an
actual observation of what the atmosphere is
doing at every single point on the ground or
every single point in the atmosphere.
So there's some assumptions being made
and those small errors grow with time as you
project them out in time, using things like a
computer model.
In 2022, NOAA began to use two new
supercomputers made by General Dynamics
Information Technology, which the agency says
will allow it to make more detailed forecasts
further in advance.
NOAA also makes its data sources and models
available to researchers through a number of cloud
providers, including Google, Microsoft, Amazon
and IBM. Despite all the high tech sensors and
supercomputers, meteorologists still play
a crucial role. They generally will look at
several different weather models and weigh
the different outcomes against one another to
come up with the most likely forecast.
It's a highly collaborative process,
with organizations routinely exchanging
observation data with their counterparts in
other parts of the world that can help to inform
their own weather models.
It's part of our core mission to cooperate with
other countries to bring that vital data in for
our global models.
Those global models turn into local forecasts.
Two of the most well-known global weather
models are the Global Forecast System, run by
NOAA, and a European model run by the European
Center for Medium-range Weather Forecasts, an
organization made up of over 30 European
countries. But whereas NOAA makes its weather
data available for free, the ECMWF charges for
some of its data. On average, the European
model has produced more accurate weather
forecasts than the US model, though experts
point out this doesn't mean that the European
model will outperform the US one on every
storm. The accuracy of the two models became a
huge point of contention in 2012 after the
European model predicted that Hurricane Sandy
would slam into the East Coast days before the US
model eventually came to the same conclusion after
originally predicting that the hurricane would
drift into the Atlantic.
In some cases, computer models may not
necessarily capture the full intensity of the
event. They may hint that a blizzard is
possible, and sometimes the most intense
conditions are in very localized areas.
So if you think back to the pre-Christmas
blizzard in Buffalo, New York, in 2022, that
blizzard itself was a very, very localized
area, but obviously very extreme.
They had about 36 hours of blizzard conditions,
gusts over 70mph.
And so that poses challenges in really
trying to dial in the exact area that's going
to get the most extreme conditions.
Recent years, NOAA has provided a lot more
high-resolution modeling that's really helping us
dial in these types of situations.
To help foresee localized extreme weather events,
NOAA is increasingly turning to the private
sector.
The private sector is very adept at tailoring
products and reaching niche markets that The
Weather Service is not able to provide support
to.
Private weather forecasting is not an
entirely new concept.
Companies like AccuWeather and The
Weather Company, which was purchased by IBM in
2016, have been around for decades.
These companies have taken the free, raw,
weather data from NOAA and other government
agencies, repackaged it into a digestible format
using their own proprietary algorithms
and distributed it to customers. Some of these
private weather platforms have also
collected their own supplemental weather
data. But newer companies are taking
private weather forecasting a step
further, not only telling customers what to
expect, but how to respond.
Founded in 2016, Boston-based startup
Tomorrow.io is in the business of offering its
clients extremely detailed weather
predictions and pre-storm strategy plans
based on its proprietary algorithms and data.
This can include things like warning a trucking
company of an oncoming thunderstorm so that it
can send out its fleet earlier, or helping tech
companies like Uber with more accurate ETAs.
Most businesses do not have anything in place to
manage weather and climate related
challenges, and if they have something, it's
usually talking to a meteorologist over the
phone, which is not a scalable solution.
The company says its data is more accurate than
information the government can offer.
I can 100% guarantee, looking at our historical
data over the last few years that on average our
models are more accurate in between 10 to 40% than
government models, not only the American models,
also the European model.
Tomorrow.io's customers span a wide number of
industries from airlines for whom precise weather
data can serve to help them make decisions such
as whether they need to de-ice planes, to sports
organizations that can use Tomorrow's
predictions to schedule events.
Technology companies, utility companies and
government organizations are also clients.
Tomorrow.io has also worked with a number of
cities, including ones in New Jersey and
Massachusetts, to help them prepare for incoming
storms.
A state may want a very tailored, specific
forecast for a road.
And so because our information is public
good and publicly available, the private
sector can take our information and our
models and our forecasts, and they can
innovate on them and make pinpoint accurate
forecasts. They can advise on particular road
conditions, closures, what it means for local
officials.
In addition to using government data,
Tomorrow.io initially gathered weather
information from devices like car temperature
sensors, windshield wipers and the signal
strength of cellular networks, which tends to
be lower when it's raining. But the company
says it's moving away from this strategy in
favor of space-based observations.
The idea is to launch a constellation of 30
radar-equipped satellites into space by
2024 to gather weather data from every point on
earth. Last year, NOAA announced that it was
partnering with Tomorrow.io to see how
the company's hyperlocal satellite weather data
might be used to inform NOAA's weather models.
According to the agency, the agreement is
exploratory and unpaid.
Radars are the most important weather
sensors. They tell us where it's raining in
real time, how the clouds look like and
whatnot. And right now, 5 billion people do not
have radar, which means that in those places, you
don't even know where it's raining in real
time. I'm not even talking about the
forecast. You don't have flood alert.
You don't have farmers insurance.
The GDP is correlated to that and it's lower.
Customers pay anywhere between $20,000 per year
to millions of dollars per year for
Tomorrow.io's services, according to Elkabetz.
Our hypothesis for Tomorrow is that climate
security is going to be very similar to cyber
security. If you're not going to put systems in
place to manage your weather and climate
risks, you're going to be held accountable for
not doing everything in your power to protect
your assets, your customers, your top line,
your bottom line.
Founded in Alameda, California about ten
years ago, Saildrone is another private company
in the business of gathering and selling
hard-to-obtain weather data. But unlike
Tomorrow.io, Saildrone is targeting the ocean.
To do this, Saildrone has a fleet of nearly 100
unmanned surface vehicles that it calls
Explorers. The company does not sell the
vehicles themselves, but rather the data they
collect. Each is outfitted with a number
of different sensors that it uses to gather
and transmit real time measurements on metrics
including salinity, wind speed and direction, air
temperature, sea surface temperature, humidity and
barometric pressure. All variables that can be fed
into weather models to improve forecasting.
In terms of the ocean's role in weather, there is
a ton of interaction that happens between the
surface of the ocean in terms of heat transfer
and energy transfer between the oceans and
the atmosphere and that interaction, that is
where weather systems garner a lot of their
energy. One of the most important ocean weather
phenomena that Saildrone is helping forecasters
track are El Niño and La Niña.
Things like El Niño, La Niña, the North Atlantic
Oscillation, or NAO, longer term patterns like
that will tend to affect weather over the course
of an entire season or an entire year.
So, for instance, you might say in El Niño, the
water temperatures in the tropical Pacific
Ocean are warmer than normal. On average, you
can expect wetter conditions in California
and the West Coast and places like that.
It doesn't mean that on a particular day that
you're going to get a storm system or that a
particular storm system is made possible because
of El Niño. It just loads the dice towards
certain outcomes in certain areas of the
world.
Saildrones have also been very useful in tracking
and observing hurricanes, a task that
the company has undertaken in partnership
with NOAA for hurricanes like Sam in 2021 and
Fiona in 2022.
Data gathered from saildrones will help NOAA
with one of the biggest challenges in hurricane
forecasting, predicting rapid intensification,
when hurricane wind speeds increase at least
35mph over a 24 hour period.
In the past, human hurricane hunters and
buoys gathered much of this data for NOAA.
But Saildrone allows the agency to gather data
from parts of the hurricane that are too
dangerous for people to fly into or areas that
are not covered by their buoy system.
NOAA has also used Saildrone to collect fish
population data and map the ocean floor.
Nearshore surface mapping, knowing the
depths of the oceans, knowing where the shelves
are helps with forecasting floods and
coastal inundation. So there's some big efforts
that we are looking to take part in Florida to
better map the coastal areas there to help
inform flood inundation and flood forecasting.
To accomplish these ocean mapping missions,
Saildrone has two larger models of its vehicles
that can be equipped with sonar.
Like Tomorrow.io, Saildrone has customers
beyond NOAA, including the US Navy, Customs and
Border Protection and the US Coast Guard, which
are using the drones for surveillance.
Saildrone declined to comment on the cost of
its services, but customers reportedly pay
around $2,500 for a day of saildrone data.
While public private partnerships can provide
NOAA with valuable data to improve its forecasts,
some worry about a future where NOAA and the
public become overly dependent on private
weather companies.
The advantage of privatizing it is you get
all the force of private innovation.
More accuracy, and better decisions, and
better observations, and better satellites, and
better supercomputers and all those things.
What you're doing, though, is you're
immediately setting up the situation where
people who can pay for forecasts are at less
risk or safer than people who can't pay for
them. And in an era of climate extremes, when
you're talking about evacuations, when you're
talking about hurricanes, when you're
talking about, you know, deadly snowstorms, you
really have a kind of climate dystopian
situation where, you know, rich people are
getting out of the way of the weather, way ahead
of poor people.
But Elkabetz says there's room for both.
If we waited for the government to develop a
Covid-19 vaccine, it would have been a
disaster. The government cannot, in our age, be
responsible for developing innovative
technology. There is a way to build innovative
and great technologies and also benefit the
public.
Draggon agrees that private public
partnerships will likely do more good than harm as
long as NOAA remains steadfast in its mission
to serve the public good.
The private industry for the weather community is
a rapidly growing market and the taxable revenue
from that industry goes right back into the
economy and helps to fund public entities such
as the National Weather Service. There's data
sets that are absolutely essential for numerical
weather prediction and getting that forecast
down to the local level.
That has to remain as a public good.
But there's additional data that can help
increase the accuracy of those models and help
make the local context very granular.
And that's where perhaps there's more space to
explore data buys.