July 31, 2025 • 10 mins
This deeo dive discusses how artificial intelligence is revolutionizing weather forecasting, making predictions significantly more accurate and timely. It reviews an article titled "The Digital Meteorologist: Unleashing AI for Accurate Weather Insights" which highlights various AI methods and models developed by major tech companies and research institutions, emphasizing their strengths in different forecasting ranges. The text also addresses the crucial need to balance advanced accuracy with user-friendliness, ensuring that complex weather data can be easily interpreted and acted upon by a wider audience. Ultimately, the source envisions a future where AI democratizes access to sophisticated weather insights, benefiting individuals and organizations alike. Read the full article here
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to the deep dive. You know we all check
the weather, right, I mean every day.
Speaker 2 (00:04):
Pretty much unavoidable.
Speaker 1 (00:05):
Yeah, whether it's like do I need an umbrella or
planning a weekend trip, or just what am I wearing tomorrow?
Weather forecasts are just so baked into our daily lives.
But what if I told you those forecasts are undergoing
this massive transformation. It's not just about getting slightly better
odds on rain next Tuesday, No, not at all. Artificial
(00:27):
intelligence is well, it's fundamentally changing how we predict this.
Speaker 2 (00:30):
Guys.
Speaker 1 (00:31):
Okay, let's unpack this. It's a pretty big shift.
Speaker 2 (00:33):
That's exactly right. And our mission here really is to
explore both sides of AI's impact on weather forecasting.
Speaker 1 (00:41):
Both sides.
Speaker 2 (00:42):
Yeah, So, first, how AI is making predictions faster, more
accurate than we've ever seen. But also, and this is critical,
how it's making all that information actually usable easible for everyone,
for everyone, whether you're just wondering about a jacket like
you said, or you're managing complex logistics. The goal today
is to pull out the really key insights from the
(01:02):
sources we've looked at and show how this new era
of meteorology really affects you.
Speaker 1 (01:07):
Okay, So this AI revolution, when we talk about that,
it's really a core shift, isn't it, And how we
even approach predicting weather fundamentally.
Speaker 2 (01:16):
Guess, it's not just.
Speaker 1 (01:17):
Small improvements here and there. It's changing the whole process
way beyond just a slightly better guess for tomorrow.
Speaker 2 (01:23):
And the way it's transforming things is well, speed and accuracy.
Predictions are coming faster, and they're hitting the mark more
often than traditional methods could. There's actually a bit of
a race going on finding the best AI approaches using
these incredible new algorithms and computing power.
Speaker 1 (01:42):
And you can see that in specific models, can't you.
Like Google's graph cast right graph Cast that's making waves
in medium range forecasts, giving us what predictions out to
ten days, which is amazing.
Speaker 2 (01:53):
For planning, truly revolutionary for that timeframe.
Speaker 1 (01:56):
But maybe it struggles a bit with the really fine
street level d T or very long range stuff.
Speaker 2 (02:02):
That seems to be the case, and that's where you
see other models stepping in, like MetNet three, okay, which
is all about that short term, super high resolution forecast
updates every few minutes, really granular, so it's a.
Speaker 1 (02:16):
Spectrum different tools for different jobs.
Speaker 2 (02:18):
Exactly, it's a whole ecosystem developing. And it's not just
Google either. You've got major players like Nvidia, Microsoft, Huawei.
They're all in this space.
Speaker 1 (02:27):
They all have their own models, their own approaches.
Speaker 2 (02:29):
Each bringing something unique to the table. It shows how
big this push is right across the tech industry.
Speaker 1 (02:35):
And it's not just the tech giants. We have to
talk about the European Center for Medium Range Weather Forecasts ECMWF.
Speaker 2 (02:43):
Oh, absolutely crucial.
Speaker 1 (02:44):
They're kind of the benchmark, aren't they the gold standard.
Lots of companies measure their AI models against what ECMWF produces.
Speaker 2 (02:52):
They really are and their contributions are huge. They partner
with Copernicus, for instance, on the ER five re analysis
model ER five.
Speaker 1 (03:00):
I've explained that a bit.
Speaker 2 (03:01):
Think of it like the ultimate historical weather data set,
decades of observations blended with computer models, creating this super
detailed picture of past weather.
Speaker 1 (03:10):
Okay, so it's the training data exactly.
Speaker 2 (03:12):
It's what these new AI systems learn from. You need
that deep history. And ECMWF also hosts some of these
big AI models graph Casts forecast Net from Nvidia, pangu
Weather from Huawei on their cloud platform.
Speaker 1 (03:26):
Making them accessible right.
Speaker 2 (03:28):
And importantly they've developed their own AI model too, the AISSAH.
It enhances their existing main system, the Integrated Forecasting System
or IFS, so they're innovating internally as well.
Speaker 1 (03:41):
Okay, so some of this first part, then, the predictive
power of AI is just exploding. You've got big tech
academia international groups like ECMWF all pushing forward faster, more
accurate predictions. But having amazing predictions is only half the story,
isn't it. Because here's where it gets really interesting.
Speaker 2 (03:57):
That's such a critical point because even with all this
accuracy users, you know, people trying to use the forecast,
they face real challenges, practical ones like what well, for one,
just data overload. You can get so much information thrown
at you it leads to analysis.
Speaker 1 (04:12):
Proalysis, right, too much noise, you don't know what matter exactly.
Speaker 2 (04:15):
Then you've got incompatible data formats, trying to plug data
from one source into another system often.
Speaker 1 (04:21):
A nightmare, yeah, I can imagine.
Speaker 2 (04:23):
And just a lack of standardization generally makes it really
hard to compare different forecasts or different models meaningfully. So
you can have tons of accurate data but if you
can't access it easily, or understand it or compare it,
it's not actually helping you much.
Speaker 1 (04:39):
So what does this all mean for you listening right now?
It means making weather data accessible isn't just about dumping
numbers online, not at all. It's about designing systems that
are easy to use, that give you the right information
when you need it, making the data work for people,
not you know, the other way around.
Speaker 2 (04:55):
It's like having a Formula one car but no steering
wheel or map. All that power, but kind of useless
if you can't direct it.
Speaker 1 (05:01):
Get analogy.
Speaker 2 (05:02):
And this is another area where AI is stepping up,
actually transforming that raw data into something genuinely useful, actionable
insights how soon Well, instead of just saying fifty percent
chance of rain, AI can generate tailored alerts for specific
things you care about, like high wind warnings for your
specific sailing route or frost alerts for your particular crops.
(05:26):
It can power user friendly dashboards that visualize really complex
data simply, and it can summarize huge data sets, turning
a complex atmospheric forecast into Okay, your picnic tomorrow afternoon
probably going to get rained out.
Speaker 1 (05:40):
That makes sense, turning data into actual.
Speaker 2 (05:42):
Advice, precisely actionable intelligence.
Speaker 1 (05:45):
Okay, so let's look ahead then, what are the future trends?
Where is AI taking weather forecasting? Next?
Speaker 2 (05:50):
One thing you mentioned is ensemble forecasts.
Speaker 1 (05:53):
Right, Ensembles are becoming much more feasible with AI.
Speaker 2 (05:55):
Explain again, it's like running lots of slightly different predictions.
Speaker 1 (05:58):
At once, exactly, doesn't Maybe hundreds? Models like Huawei's Pango
Weather or Nvidia's forecast net are so fast now they
can run these huge ensembles quickly.
Speaker 2 (06:08):
And why is that important? It's all about uncertainty. Instead
of just one prediction, you get a range of possibilities
and crucially how likely each one is. It gives you
much better handle on the confidence of the forecast. Is
it a sure thing or are there wildly different outcomes possible?
Got it?
Speaker 1 (06:26):
More confidence? Better decisions?
Speaker 2 (06:28):
What else well tied to that is the drive for
better data to feed these models in the first place.
Speaker 1 (06:33):
Right, the initial conditions garbage in, garbage out basically pretty much.
Speaker 2 (06:36):
So you have companies like Jua and medium Attics exploring
new ways to get data, not just weather balloons and stations,
but maybe IoT sensors.
Speaker 1 (06:46):
Drones, drones flying into storms.
Speaker 2 (06:48):
Potentially, yeah, or just gathering more data and hard to
reach places, anything to get a more precise snapshot of
the atmosphere right now to start the forecast. And there's
also a push to make these initial condition data sets
more more open and accessible, so more people can actually
run these powerful open source AI models.
Speaker 1 (07:05):
That links into accessibility again. Okay, so better data, better
uncertainty estimates. Yeah, what about the forecast themselves.
Speaker 2 (07:12):
They're getting better across the board, really more accurate, definitely,
but also more detailed, more timely. We're really moving towards
true hyper local.
Speaker 1 (07:21):
Forecasts, hyper local like down to my street or my
garden potentially.
Speaker 2 (07:26):
Yes, that level of granularity. Imagine knowing the precise moment
rain will start on your block, not just in your city.
That's the kind of detail AI is starting to unlock.
It was almost sci fi a few years ago.
Speaker 1 (07:39):
Wow.
Speaker 2 (07:40):
And the bigger impact of all this well, obviously better
short term forecasts, much better prediction of extreme weather events,
which is huge for safety and planning. Absolutely, and it's
often more computationally efficient than the massive traditional models. But
here's something really fascinating. AI isn't just crunching numbers faster.
It seems to be helping us understand the underlying physics
(08:00):
of the atmosphere better. No by spotting patterns complex interactions
that maybe human scientists hadn't noticed or couldn't easily model,
bele for things hidden in the data, it could lead
to breakthroughs in understanding climate dynamics, not just weather.
Speaker 1 (08:14):
That's deep finding connections we missed, which leads to maybe
one of the most exciting ideas here, the democratization of
weather forecasting.
Speaker 2 (08:24):
Yes, this is a huge implication.
Speaker 1 (08:27):
Explain that. It means it's not just for governments or
big companies anymore.
Speaker 2 (08:30):
That's the core idea. High quality, accurate weather information is
becoming accessible to many more people and organizations.
Speaker 1 (08:37):
How does that work in practice, Well.
Speaker 2 (08:40):
Think about smaller groups, maybe farmers, cooperatives, or community emergency
response teams. With open source AI weather models becoming available
and the power of cloud.
Speaker 1 (08:50):
Computing, they can run their own forecasts.
Speaker 2 (08:53):
Potentially, yes, create their own tailored forecasts for their specific needs.
Imagine a farmer getting a super accurate range fall prediction
just for their fields, or a coastal community using an
AI model to predict local flooding risks. With much higher
precision than the regional forecast. That's empowering, it really is.
And AI can also help fill data gaps, you know,
(09:15):
in remote areas where there aren't many traditional weather stations.
AI can sometimes infer conditions, making forecasts pozzile where they
barely existed before. It's about getting vital information to more people.
Speaker 1 (09:25):
Okay, so let's try and wrap this up. It feels
like AI in weather forecasting is genuinely a game changer,
no question. We're seeing forecasts that are faster, significantly more reliable.
You've got companies like Google and Video Microsoft really pushing
things forward. Huge potential hurdles still exist though, right especially
around making it all easy to use. Like we discussed.
Speaker 2 (09:47):
Absolutely that combination of accuracy and usability, that's the key,
that's the future focus for meteorology. I think we're heading
towards a time where weather data, these AI driven insights
are just seamlessly integrated into our daily decisions, right there
at our fingertips.
Speaker 1 (10:05):
So a final thought for everyone listening, what stands out
to you when you think about AI helping us prepare
for whatever the weather throws our way. How might these hyperlocal,
super accessible forecasts change your own daily routines, or maybe
even how your community prepares for things, something to think about.
Welcome to the deep dive. You know we all check
the weather, right, I mean every day.
Speaker 2 (00:04):
Pretty much unavoidable.
Speaker 1 (00:05):
Yeah, whether it's like do I need an umbrella or
planning a weekend trip, or just what am I wearing tomorrow?
Weather forecasts are just so baked into our daily lives.
But what if I told you those forecasts are undergoing
this massive transformation. It's not just about getting slightly better
odds on rain next Tuesday, No, not at all. Artificial
(00:27):
intelligence is well, it's fundamentally changing how we predict this.
Speaker 2 (00:30):
Guys.
Speaker 1 (00:31):
Okay, let's unpack this. It's a pretty big shift.
Speaker 2 (00:33):
That's exactly right. And our mission here really is to
explore both sides of AI's impact on weather forecasting.
Speaker 1 (00:41):
Both sides.
Speaker 2 (00:42):
Yeah, So, first, how AI is making predictions faster, more
accurate than we've ever seen. But also, and this is critical,
how it's making all that information actually usable easible for everyone,
for everyone, whether you're just wondering about a jacket like
you said, or you're managing complex logistics. The goal today
is to pull out the really key insights from the
(01:02):
sources we've looked at and show how this new era
of meteorology really affects you.
Speaker 1 (01:07):
Okay, So this AI revolution, when we talk about that,
it's really a core shift, isn't it, And how we
even approach predicting weather fundamentally.
Speaker 2 (01:16):
Guess, it's not just.
Speaker 1 (01:17):
Small improvements here and there. It's changing the whole process
way beyond just a slightly better guess for tomorrow.
Speaker 2 (01:23):
And the way it's transforming things is well, speed and accuracy.
Predictions are coming faster, and they're hitting the mark more
often than traditional methods could. There's actually a bit of
a race going on finding the best AI approaches using
these incredible new algorithms and computing power.
Speaker 1 (01:42):
And you can see that in specific models, can't you.
Like Google's graph cast right graph Cast that's making waves
in medium range forecasts, giving us what predictions out to
ten days, which is amazing.
Speaker 2 (01:53):
For planning, truly revolutionary for that timeframe.
Speaker 1 (01:56):
But maybe it struggles a bit with the really fine
street level d T or very long range stuff.
Speaker 2 (02:02):
That seems to be the case, and that's where you
see other models stepping in, like MetNet three, okay, which
is all about that short term, super high resolution forecast
updates every few minutes, really granular, so it's a.
Speaker 1 (02:16):
Spectrum different tools for different jobs.
Speaker 2 (02:18):
Exactly, it's a whole ecosystem developing. And it's not just
Google either. You've got major players like Nvidia, Microsoft, Huawei.
They're all in this space.
Speaker 1 (02:27):
They all have their own models, their own approaches.
Speaker 2 (02:29):
Each bringing something unique to the table. It shows how
big this push is right across the tech industry.
Speaker 1 (02:35):
And it's not just the tech giants. We have to
talk about the European Center for Medium Range Weather Forecasts ECMWF.
Speaker 2 (02:43):
Oh, absolutely crucial.
Speaker 1 (02:44):
They're kind of the benchmark, aren't they the gold standard.
Lots of companies measure their AI models against what ECMWF produces.
Speaker 2 (02:52):
They really are and their contributions are huge. They partner
with Copernicus, for instance, on the ER five re analysis
model ER five.
Speaker 1 (03:00):
I've explained that a bit.
Speaker 2 (03:01):
Think of it like the ultimate historical weather data set,
decades of observations blended with computer models, creating this super
detailed picture of past weather.
Speaker 1 (03:10):
Okay, so it's the training data exactly.
Speaker 2 (03:12):
It's what these new AI systems learn from. You need
that deep history. And ECMWF also hosts some of these
big AI models graph Casts forecast Net from Nvidia, pangu
Weather from Huawei on their cloud platform.
Speaker 1 (03:26):
Making them accessible right.
Speaker 2 (03:28):
And importantly they've developed their own AI model too, the AISSAH.
It enhances their existing main system, the Integrated Forecasting System
or IFS, so they're innovating internally as well.
Speaker 1 (03:41):
Okay, so some of this first part, then, the predictive
power of AI is just exploding. You've got big tech
academia international groups like ECMWF all pushing forward faster, more
accurate predictions. But having amazing predictions is only half the story,
isn't it. Because here's where it gets really interesting.
Speaker 2 (03:57):
That's such a critical point because even with all this
accuracy users, you know, people trying to use the forecast,
they face real challenges, practical ones like what well, for one,
just data overload. You can get so much information thrown
at you it leads to analysis.
Speaker 1 (04:12):
Proalysis, right, too much noise, you don't know what matter exactly.
Speaker 2 (04:15):
Then you've got incompatible data formats, trying to plug data
from one source into another system often.
Speaker 1 (04:21):
A nightmare, yeah, I can imagine.
Speaker 2 (04:23):
And just a lack of standardization generally makes it really
hard to compare different forecasts or different models meaningfully. So
you can have tons of accurate data but if you
can't access it easily, or understand it or compare it,
it's not actually helping you much.
Speaker 1 (04:39):
So what does this all mean for you listening right now?
It means making weather data accessible isn't just about dumping
numbers online, not at all. It's about designing systems that
are easy to use, that give you the right information
when you need it, making the data work for people,
not you know, the other way around.
Speaker 2 (04:55):
It's like having a Formula one car but no steering
wheel or map. All that power, but kind of useless
if you can't direct it.
Speaker 1 (05:01):
Get analogy.
Speaker 2 (05:02):
And this is another area where AI is stepping up,
actually transforming that raw data into something genuinely useful, actionable
insights how soon Well, instead of just saying fifty percent
chance of rain, AI can generate tailored alerts for specific
things you care about, like high wind warnings for your
specific sailing route or frost alerts for your particular crops.
(05:26):
It can power user friendly dashboards that visualize really complex
data simply, and it can summarize huge data sets, turning
a complex atmospheric forecast into Okay, your picnic tomorrow afternoon
probably going to get rained out.
Speaker 1 (05:40):
That makes sense, turning data into actual.
Speaker 2 (05:42):
Advice, precisely actionable intelligence.
Speaker 1 (05:45):
Okay, so let's look ahead then, what are the future trends?
Where is AI taking weather forecasting? Next?
Speaker 2 (05:50):
One thing you mentioned is ensemble forecasts.
Speaker 1 (05:53):
Right, Ensembles are becoming much more feasible with AI.
Speaker 2 (05:55):
Explain again, it's like running lots of slightly different predictions.
Speaker 1 (05:58):
At once, exactly, doesn't Maybe hundreds? Models like Huawei's Pango
Weather or Nvidia's forecast net are so fast now they
can run these huge ensembles quickly.
Speaker 2 (06:08):
And why is that important? It's all about uncertainty. Instead
of just one prediction, you get a range of possibilities
and crucially how likely each one is. It gives you
much better handle on the confidence of the forecast. Is
it a sure thing or are there wildly different outcomes possible?
Got it?
Speaker 1 (06:26):
More confidence? Better decisions?
Speaker 2 (06:28):
What else well tied to that is the drive for
better data to feed these models in the first place.
Speaker 1 (06:33):
Right, the initial conditions garbage in, garbage out basically pretty much.
Speaker 2 (06:36):
So you have companies like Jua and medium Attics exploring
new ways to get data, not just weather balloons and stations,
but maybe IoT sensors.
Speaker 1 (06:46):
Drones, drones flying into storms.
Speaker 2 (06:48):
Potentially, yeah, or just gathering more data and hard to
reach places, anything to get a more precise snapshot of
the atmosphere right now to start the forecast. And there's
also a push to make these initial condition data sets
more more open and accessible, so more people can actually
run these powerful open source AI models.
Speaker 1 (07:05):
That links into accessibility again. Okay, so better data, better
uncertainty estimates. Yeah, what about the forecast themselves.
Speaker 2 (07:12):
They're getting better across the board, really more accurate, definitely,
but also more detailed, more timely. We're really moving towards
true hyper local.
Speaker 1 (07:21):
Forecasts, hyper local like down to my street or my
garden potentially.
Speaker 2 (07:26):
Yes, that level of granularity. Imagine knowing the precise moment
rain will start on your block, not just in your city.
That's the kind of detail AI is starting to unlock.
It was almost sci fi a few years ago.
Speaker 1 (07:39):
Wow.
Speaker 2 (07:40):
And the bigger impact of all this well, obviously better
short term forecasts, much better prediction of extreme weather events,
which is huge for safety and planning. Absolutely, and it's
often more computationally efficient than the massive traditional models. But
here's something really fascinating. AI isn't just crunching numbers faster.
It seems to be helping us understand the underlying physics
(08:00):
of the atmosphere better. No by spotting patterns complex interactions
that maybe human scientists hadn't noticed or couldn't easily model,
bele for things hidden in the data, it could lead
to breakthroughs in understanding climate dynamics, not just weather.
Speaker 1 (08:14):
That's deep finding connections we missed, which leads to maybe
one of the most exciting ideas here, the democratization of
weather forecasting.
Speaker 2 (08:24):
Yes, this is a huge implication.
Speaker 1 (08:27):
Explain that. It means it's not just for governments or
big companies anymore.
Speaker 2 (08:30):
That's the core idea. High quality, accurate weather information is
becoming accessible to many more people and organizations.
Speaker 1 (08:37):
How does that work in practice, Well.
Speaker 2 (08:40):
Think about smaller groups, maybe farmers, cooperatives, or community emergency
response teams. With open source AI weather models becoming available
and the power of cloud.
Speaker 1 (08:50):
Computing, they can run their own forecasts.
Speaker 2 (08:53):
Potentially, yes, create their own tailored forecasts for their specific needs.
Imagine a farmer getting a super accurate range fall prediction
just for their fields, or a coastal community using an
AI model to predict local flooding risks. With much higher
precision than the regional forecast. That's empowering, it really is.
And AI can also help fill data gaps, you know,
(09:15):
in remote areas where there aren't many traditional weather stations.
AI can sometimes infer conditions, making forecasts pozzile where they
barely existed before. It's about getting vital information to more people.
Speaker 1 (09:25):
Okay, so let's try and wrap this up. It feels
like AI in weather forecasting is genuinely a game changer,
no question. We're seeing forecasts that are faster, significantly more reliable.
You've got companies like Google and Video Microsoft really pushing
things forward. Huge potential hurdles still exist though, right especially
around making it all easy to use. Like we discussed.
Speaker 2 (09:47):
Absolutely that combination of accuracy and usability, that's the key,
that's the future focus for meteorology. I think we're heading
towards a time where weather data, these AI driven insights
are just seamlessly integrated into our daily decisions, right there
at our fingertips.
Speaker 1 (10:05):
So a final thought for everyone listening, what stands out
to you when you think about AI helping us prepare
for whatever the weather throws our way. How might these hyperlocal,
super accessible forecasts change your own daily routines, or maybe
even how your community prepares for things, something to think about.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
My Favorite Murder with Karen Kilgariff and Georgia Hardstark
My Favorite Murder is a true crime comedy podcast hosted by Karen Kilgariff and Georgia Hardstark. Each week, Karen and Georgia share compelling true crimes and hometown stories from friends and listeners. Since MFM launched in January of 2016, Karen and Georgia have shared their lifelong interest in true crime and have covered stories of infamous serial killers like the Night Stalker, mysterious cold cases, captivating cults, incredible survivor stories and important events from history like the Tulsa race massacre of 1921. My Favorite Murder is part of the Exactly Right podcast network that provides a platform for bold, creative voices to bring to life provocative, entertaining and relatable stories for audiences everywhere. The Exactly Right roster of podcasts covers a variety of topics including historic true crime, comedic interviews and news, science, pop culture and more. Podcasts on the network include Buried Bones with Kate Winkler Dawson and Paul Holes, That's Messed Up: An SVU Podcast, This Podcast Will Kill You, Bananas and more.
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.