Speaker 1: When I moved to southern California, I felt this immediate,
Speaker 1: immense relief, not just because I was free of the
Speaker 1: tyranny of outside clothing, but because I was released from
Speaker 1: the anxiety of not knowing if the weather was going
Speaker 1: to ruin my plans. Were you planning an outdoor birthday
Speaker 1: party for your toddler, No need to make backup plans
Speaker 1: just in case it rains. Do you need to drive
Speaker 1: a few hours away, No problem. You don't have to
Speaker 1: worry that a snowstorm might make the roads impassable because
Speaker 1: I could predict the weather myself since it was the
Speaker 1: same every single day. But not all of us are
Speaker 1: lucky enough to live in such calm climbs, so it's
Speaker 1: still very important that we try to anticipate storms so
Speaker 1: that they're less fortunate among us can be prepared. It's
Speaker 1: not often described as important physics, but predicting the weather
Speaker 1: is one of physics' great success stories. John Martin, professor
Speaker 1: of atmospheric oceanic sciences, told me that weather predictions are
Speaker 1: quote the most unheralded scientific advance of the second half
Speaker 1: of the twentieth century. If you keep score every day,
Speaker 1: I can't believe how well we predict the weather three
Speaker 1: to five days in advance. In thirty years, we've gone
Speaker 1: from predictions from one to two days to now five
Speaker 1: to seven days. We have made unbelievable progress. So how
Speaker 1: does that all work? What is the physics underlying the weather,
Speaker 1: why has it gotten better? And what can we expect
Speaker 1: into the future. I talked to Professor Martin and my
Speaker 1: good friend Professor Jane Baldwin here at UC Irvine about
Speaker 1: how the weather all works. So we'll dig into all
Speaker 1: of that in today's episode, dedicated to all of y'all
Speaker 1: who still experience regular weather. Welcome to Daniel and Kelly's
Speaker 1: extraordinarily sunny universe.
Speaker 2: Hello Kelly Leadersmith. I studied fites and space and I
Speaker 2: love rainy days.
Speaker 3: Hi.
Speaker 1: I'm Daniel. I'm a particle physicist, and I can predict
Speaker 1: the weather in California for the next one hundred years
Speaker 1: with my eyes closed.
Speaker 2: How boring, how massively dull.
Speaker 1: How wonderfully, delightfully, predictably, reliably boring.
Speaker 2: Oh, you know, one of my favorite weather moments, I
Speaker 2: have to admit, was a southern California morning. So I
Speaker 2: was a visiting scholar at the University of California, Santa
Speaker 2: Barbara for a little while, and I had an office
Speaker 2: that was like right out on the ocean. Was amazing.
Speaker 2: And when I was driving in one day, there was
Speaker 2: just a little bit of water on the ground and
Speaker 2: the car tires were kicking up a little bit of
Speaker 2: a spray, and there were literally rainbows following all of
Speaker 2: the cars into school. And then I got out of
Speaker 2: the car and the rain had stopped, and there was
Speaker 2: a rainbow over the ocean and there were hummingbirds and
Speaker 2: it was like a Disney movie scene. I expected like
Speaker 2: a bunny to hop out and be like, can I
Speaker 2: help you with anything? Anyway, it was. It was kind
Speaker 2: of magical. I'll give you that.
Speaker 1: California is heaven. Yes, what happens when you die in
Speaker 1: Virginia is you end up in California.
Speaker 2: Do you know that not all California as southern California.
Speaker 1: I mean all of real California.
Speaker 2: Oh, I see, because northern California's got some weather.
Speaker 1: You're absolutely right. In fact, I heard Katrina say something
Speaker 1: really insightful the other day. You know, she's from northern California.
Speaker 1: But now we've lived in southern California for quite a while,
Speaker 1: and she said to somebody that she's now a complete
Speaker 1: Californian because she's lived in both northern and southern California.
Speaker 1: And I was like, Oh, that's cool. She's like accepted
Speaker 1: southern California, which is hard for Northern California's I'm aware, yes,
Speaker 1: not everything is Southern California unfortunately.
Speaker 2: Oh I really like the variability Virginia weather is amazing
Speaker 2: for me. But so my question for you is, what
Speaker 2: is the worst weather situation that you've experienced?
Speaker 1: Great question. I was on the East coast, of course,
Speaker 1: you're doing a college tour with my son, and we
Speaker 1: were in Massachusetts. I think we were visiting Amherst or
Speaker 1: maybe it was Williams, I don't remember. And there was
Speaker 1: some freak tornado which tore up a bunch of trees
Speaker 1: and knocked down a bunch of power lines and there
Speaker 1: was no power in the whole town for like almost
Speaker 1: half a day. It was crazy and the winds were
Speaker 1: insane and it felt a little scary, like we saw
Speaker 1: like huge branches flying by the window.
Speaker 2: Yeah, yep.
Speaker 1: And he didn't end up going to school there.
Speaker 2: Yeah, I get that. I get that. So we lived
Speaker 2: in Alabama, Tuscaloosa, and we moved there pretty soon after
Speaker 2: that giant tornado that like made the news, and you
Speaker 2: could see the path of the tornado because like, you know,
Speaker 2: you'd be driving through an area with lots of like
Speaker 2: you know, Starbucks, Panera, lots of stores or whatever, and
Speaker 2: then suddenly there would be a like air an opening
Speaker 2: in between all of the stores with nothing, and like
Speaker 2: the tornado had just gone through there and just absolutely
Speaker 2: picked up and thrown everything that was in there, and
Speaker 2: even after they cleaned it out, there were still, you know,
Speaker 2: you could tell where the tornado had gone. And we
Speaker 2: were also in Houston during some pretty bad storms and
Speaker 2: we had the kids and our dog and our cats
Speaker 2: in a little hallway in the interior of the house
Speaker 2: and my in laws were visiting, and my mother in
Speaker 2: law was so sweet. She like looked around and she
Speaker 2: she was trying to see, you know, who could get
Speaker 2: hurt and how, And she gave her glasses to Zach
Speaker 2: in case there was any like flying glass and she
Speaker 2: just insisted that he have her glasses. And I was
Speaker 2: like in that moment, I was like, gosh, you are
Speaker 2: the sweetest person in the whole world, Like you are
Speaker 2: thinking about the tiny little things you could do to
Speaker 2: help the people around you, and anyway, she's she's the best.
Speaker 1: Yeah, but we've all been caught in surprise weather, right.
Speaker 1: I remember going backpacking in Arkansas one time and being
Speaker 1: caught in a snowstorm and the temperatures dropped into the
Speaker 1: teens and we weren't one hundred percent sure we were
Speaker 1: going to make it. Oh, and everybody's been, like, you know,
Speaker 1: caught in a snowstorm or a rainstorm or in a
Speaker 1: heat wave. Right, And these things are exciting, they can
Speaker 1: be dramatic, but they can also be very dangerous, right.
Speaker 2: Yeah.
Speaker 1: People die in these crazy weather storms, and so it's
Speaker 1: valuable to be able to know in advance what's going
Speaker 1: to happen, not just so you can plain your picnics,
Speaker 1: but also you can survive the increasingly dramatic weather that
Speaker 1: we're all facing as the planet warms.
Speaker 2: Yeah, that's right, more severe weather is becoming more common.
Speaker 2: And so today we're going to talk about how good
Speaker 2: we are at making predictions and how we go about
Speaker 2: making those predictions exactly.
Speaker 1: And I wanted to pull back the curtain on like
Speaker 1: the science of this, how does this actually happen, What
Speaker 1: are we doing, why is it hard? What are the challenges?
Speaker 1: What improvements might we be seeing in the next five
Speaker 1: or ten years. What problems are just fundamentally impossible and
Speaker 1: might never be solved. And so today we're going to
Speaker 1: dig into science of all that. But before we explain
Speaker 1: to you how the experts do it, I was wondering
Speaker 1: what everybody knew about how weather predictions happen. How do
Speaker 1: those numbers end up on your phone? So I went
Speaker 1: out there to ask our listeners what they knew about
Speaker 1: how we predict the weather. If you would like to
Speaker 1: answer these kind of questions for a future episode, don't
Speaker 1: be shy, right to us two questions at Danielankelly dot org.
Speaker 1: We will send you fun questions every week in your inbox.
Speaker 1: In the meantime, think about it for a minute. What
Speaker 1: do you know about how we predict the weather? Here's
Speaker 1: what our listeners had to say. Sophisticated computer models, which
Speaker 1: with an understanding if case theory, allows us to understand
Speaker 1: the limitations.
Speaker 4: Predicting the weather is like quantum particles.
Speaker 1: There are many probabilities, but it is not known until
Speaker 1: it is observed. Meteorologists they look at the current weather,
Speaker 1: and they try to predict it by looking at the
Speaker 1: moving clouds and all of.
Speaker 3: That, by measuring with velocity and atmospheric pressure and maybe
Speaker 3: modeling these that in supercomputers.
Speaker 1: When a cow lies down in the field, it's going
Speaker 1: to and when my knee aches, it's gonna snow.
Speaker 4: Running multiple models.
Speaker 3: Big computers, really really big computers.
Speaker 4: Feed dad data, two complicated models that run on very
Speaker 4: parfa spoken.
Speaker 1: Pere i'd say, with surface measurements, satellite information and sophisticated
Speaker 1: models and perhaps even artificial.
Speaker 4: Intelligence observations taken by ships, planes, ground stations, satellites combined
Speaker 4: with models built by really really smart people that run
Speaker 4: on some of the fastest computers that humans have ever built.
Speaker 1: There are sophisticated bottles that use a wide range of
Speaker 1: observational and predictive inputs.
Speaker 4: By observing weather patterns and the types of whether those
Speaker 4: patterns tend to bring.
Speaker 2: So I don't know if there's actually like scientific evidence
Speaker 2: that sometimes knees will ache if like a stormfront is
Speaker 2: coming through. But I have to admit that there's a
Speaker 2: part of me that really hopes that if I get
Speaker 2: arthright is when I'm older, I do have the ability
Speaker 2: to tell when the weather's come in because I'll feel
Speaker 2: like I'm really intimately connected to my environment. Oh, the
Speaker 2: knees acted up again. Storms come and get the goats
Speaker 2: in the barn.
Speaker 1: I think that really shows your fundamental optimistic nature, Kelly,
Speaker 1: because you're like, oh, I forget arthritis. There'll be a
Speaker 1: silver lining. I can predict the weather.
Speaker 2: You know, life is easier when you try to see
Speaker 2: the silver lining.
Speaker 1: That's wonderful.
Speaker 2: But our audience had great answers, and they were, you know,
Speaker 2: a lot of them said, you know exactly the right thing,
Speaker 2: which is you've got to have data. Those are the
Speaker 2: observations and you feed them into computers.
Speaker 1: Yeah, essentially, and that's the big picture, not just of
Speaker 1: weather prediction but any kind of prediction. There are two
Speaker 1: fundamental ingredients to how you make a prediction. There's the
Speaker 1: models and then there's the data. So let's take those
Speaker 1: each in turn. When we say the models, we mean
Speaker 1: like we're running a computer simulation or you're calculating things
Speaker 1: on paper. Fundamentally, this is encoding the rules of the
Speaker 1: system what the future can be given what the past was.
Speaker 1: And this doesn't have to be some really complicated thing
Speaker 1: like the weather over ist endbull. Think about a much
Speaker 1: simpler situation, like you're tossing a ball in your backyard.
Speaker 1: You want to know where does it go? Well, the
Speaker 1: laws of physics predict the future, right, this is the model.
Speaker 1: These are the rules that tell you how the past
Speaker 1: becomes the future. Right. And in this case it's simple.
Speaker 1: It's a parabola. It flies through the air. Things to
Speaker 1: keep in mind here, though, is that a model like
Speaker 1: this is always approximate. If I use f eicals MA
Speaker 1: and I just account for gravity, ignore air resistance. When
Speaker 1: I'm describing the ball, I'm going to get a quick answer,
Speaker 1: and it's gonna be pretty good. It's not going to
Speaker 1: be exactly bang on correct. It can't account for everything,
Speaker 1: all the little wind gusts and the air resistance and
Speaker 1: the slight change in humidity and maybe the spin on
Speaker 1: the ball. My model ignores some details, and that's crucial. Right.
Speaker 1: If I included every single particle in the backyard, I
Speaker 1: would never get a calculation. So in order to make
Speaker 1: this tractable, I got to simplify the problem. I got
Speaker 1: to pull out the things that are important and ignore
Speaker 1: the things I think are unimportant. Because I don't think
Speaker 1: they're going to make a big enough difference in the answer.
Speaker 1: And this is where the juice is. This is what
Speaker 1: physics is, right. Physics is taking the universe and simplifying
Speaker 1: it into a model that represents the bits you're excited about,
Speaker 1: the bits you think are interesting and irrelevant, and then
Speaker 1: you use those rules and manipulate it. That's your model
Speaker 1: of the universe, and the model gives you an answer,
Speaker 1: and hopefully, if the model is close enough to your
Speaker 1: description of the universe, the answer you get from the
Speaker 1: model is similar to the answer in the actual universe.
Speaker 2: So one thing I think that's amazing is that something
Speaker 2: as simple as throwing a ball up in the air
Speaker 2: and then seeing where it lands is something we can't
Speaker 2: completely model because there's so many complicating things. And now
Speaker 2: you're talking about weather, which is so much more complicated
Speaker 2: and requires so many more inputs. And of course you
Speaker 2: can update your model. So you know, if you threw
Speaker 2: the ball in the air and you were like, you
Speaker 2: know what, it's a windy day, I absolutely need to
Speaker 2: add wind. Now you've learned something, you add wind, and
Speaker 2: so you know, it's an iterative process where you keep
Speaker 2: trying to say what is in important and do I
Speaker 2: need to include it? And does it make my predictions better?
Speaker 2: But I also will note that you put predicting weather
Speaker 2: under the physics umbrella. You think you guys get to
Speaker 2: claim weather predictions.
Speaker 1: I mean, we're not using economics to predict the weather.
Speaker 1: What else is in the running for taking credit for
Speaker 1: predicting the weather? Is it chemistry?
Speaker 2: I feel like that also is some ecology, you know,
Speaker 2: like because you're tracking.
Speaker 1: Like cowfarts or something.
Speaker 2: No, no, you like, you know, a.
Speaker 1: Fart player role. Actually, so do you think.
Speaker 2: That Noah has cow farts in their weather prediction models?
Speaker 1: I think the climate models do include bovine methane emissions. Yes,
Speaker 1: so not the daily predictions, but the bigger trends. Yes,
Speaker 1: cow farts do help determine the future of our planet.
Speaker 2: Amazing.
Speaker 1: I want to go back to the point you made earlier.
Speaker 1: You're exactly right that we're always approximating, and not just
Speaker 1: when we're doing the weather, not just when we're tossing balls, always,
Speaker 1: every single time, every model is approximation. There's this famous phrase.
Speaker 1: I a memember who said it, like all models are wrong,
Speaker 1: some of them are useful, even our description of like
Speaker 1: the fundamental particles in the universe. As far as we know,
Speaker 1: these are approximations. Every bit of science we have has
Speaker 1: boundaries of where it's relevant because there are approximations made
Speaker 1: when we construct those models everything, literally everything. We have
Speaker 1: no piece of science that isn't an approximation of the universe.
Speaker 1: Maybe one day we have a theory of everything, and
Speaker 1: it's beautiful and we can do exact calculations on very
Speaker 1: very simple situations. But we're not there. We may never
Speaker 1: be there, and even if we are there, it will
Speaker 1: be totally impractical for anything useful. Like you couldn't use
Speaker 1: string theory to predict the path of a hurricane because
Speaker 1: the complexity would be insane, Right, how many strings are
Speaker 1: you modeling? The amount of computation required to do it
Speaker 1: exactly would be impossible. So it's always an approximation. It's
Speaker 1: just a question of which approximations. That's where the science
Speaker 1: comes in, like which ones are important. Having a nose
Speaker 1: for what to approximate and what not to approximate, that's
Speaker 1: what helps some scientists make more progress than others.
Speaker 2: Yeah, And I think another thing to just sort of
Speaker 2: note is that because this is a human endeavor. Sometimes
Speaker 2: you're limited by what you can afford to get data
Speaker 2: on you know, like maybe you do want to know
Speaker 2: how much cows are farting, but in order to get
Speaker 2: that data, you would need seventy billion dollars so that
Speaker 2: farmers could attach sensors to the rear end of every cow.
Speaker 2: And so, like, you know, sometimes you know there's data
Speaker 2: you want, but you can't get it because there's not
Speaker 2: enough money or it's not possible. Maybe one day you
Speaker 2: can get it, Maybe those sensors will become cheap.
Speaker 1: Is seventy billion dollars your like a fantastical number for
Speaker 1: some like absurd amount of money for a science experiment?
Speaker 2: Yeah, I guess that's wow. Yeah what is yours? I
Speaker 2: guess you're a physicist, so it's going to be like.
Speaker 1: Well, that's embarrassing because our next project is one hundred
Speaker 1: billion dollars, So we're like already above the Kelly threshold
Speaker 1: for like absurd amounts of money.
Speaker 2: But wait, like, okay, but that's not like your personal project.
Speaker 2: That's like LHC or like a new particle collider or something.
Speaker 1: Right, Yeah, the new next particle collider budget is about
Speaker 1: one hundred billion, Yes exactly, so more than a planet
Speaker 1: wide cow farts sensor network.
Speaker 2: Well, you guys better make some really important discoveries with
Speaker 2: that money. Otherwise I'm disappointed because I want to know
Speaker 2: what's happening with the cow farts.
Speaker 1: But you bring up another point, which is the data.
Speaker 1: So models are useful. There are a system that tell
Speaker 1: us how the past becomes the future, but you also
Speaker 1: need some data so you know which past you had. Right,
Speaker 1: models describe essentially any possible universe. The rules determine which
Speaker 1: set of universes we might live in, but the data
Speaker 1: constrain it. It tells us which past we had. So
Speaker 1: the rules tell you how the past becomes the future,
Speaker 1: but you need to know which past we were in
Speaker 1: so we know which future will have. So in our
Speaker 1: ball tossing analogy, there's lots of different ways that could
Speaker 1: toss a ball. I Coatalla said high or low, or
Speaker 1: fast or slow or east or west. The rules connect
Speaker 1: the initial conditions that data the past to the future.
Speaker 1: But you need to know where did I throw the ball.
Speaker 1: So if I'm writing a simulation of that ball toss,
Speaker 1: I got to encode in the laws of physics. But
Speaker 1: then I need a data point. I need to say
Speaker 1: the ball was here and it was moving in this
Speaker 1: direction at this velocity. Then I can predict the future.
Speaker 1: Without that, it's useless. Right, So you need these two components.
Speaker 1: You need the models, plus you need the data, and
Speaker 1: then you need more data. Say I'm predicting the ball toss,
Speaker 1: I want to check in halfway and say, hey, it's
Speaker 1: my model correct, doesn't need an adjustment. A way to
Speaker 1: improve your modeling is to shorten the prediction time, to
Speaker 1: say I'm not going to predict the whole path. I'm
Speaker 1: going to predict the second and then I'm going to
Speaker 1: take a measurement and if it's off, I'm going to
Speaker 1: correct it so that if my model has veered off
Speaker 1: from reality, it doesn't get further off. And so the
Speaker 1: more data you have, the better your model is going
Speaker 1: to be. So you need these two elements dancing together,
Speaker 1: the models and the data.
Speaker 2: Yeah, and I checked my weather app today and the
Speaker 2: prediction for tomorrow was changed, And so I'm guessing we
Speaker 2: do this same thing with weather Way update. So I
Speaker 2: think we should talk in a second about what kinds
Speaker 2: of data we collect to help us inform models. But
Speaker 2: I guess my first question is we've talked about models
Speaker 2: in general. How long have we been trying to model weather?
Speaker 2: Aristotle problems, So.
Speaker 1: People have had some crazy ideas about the weather for
Speaker 1: thousands of years. The first real weather models were conceived
Speaker 1: of in the nineteen twenties. And remember we didn't have
Speaker 1: computers really until the fifties or so, so this was
Speaker 1: like a conception and somebody did a proof of principal prediction.
Speaker 1: They tried to predict the weather six hours later. They
Speaker 1: took a bunch of measurements and said, let's try to
Speaker 1: do some calculations. We have an early model. That calculation
Speaker 1: took six weeks.
Speaker 2: So not helpful.
Speaker 1: Not helpful exactly, but they did it and it wasn't terrible,
Speaker 1: and they sort of proved like, hey, you know, if
Speaker 1: you could do this calculation more quickly, then maybe you
Speaker 1: could even know the weather in advance. Oh my gosh,
Speaker 1: what an idea. Right, Yeah, it was until the nineteen
Speaker 1: fifties that we had the first computing models to do
Speaker 1: these calculations. So we can make predictions in time shorter
Speaker 1: than the prediction period. You could have enough data and
Speaker 1: run your model and get an answer before the universe
Speaker 1: revealed it, right, that's that's a prediction instead of a
Speaker 1: post addiction.
Speaker 2: That's better.
Speaker 1: So we've been doing this for decades and the last
Speaker 1: you know, seventy years or so have been improving the
Speaker 1: models and improving the data.
Speaker 2: Man, it's exciting to think that we, you know, we're
Speaker 2: going from slide rules to make these predictions to massive supercomputers.
Speaker 2: I'm appreciating my weather apps a bit more.
Speaker 1: And also, like, six weeks sounds ridiculous. I don't know
Speaker 1: that I could do that in six weeks. Oh, it's
Speaker 1: an amazing calculation. And think about like not just the ideas,
Speaker 1: but all the grunt work doing those calculations and the
Speaker 1: human error that's possible. Like, it's amazing they did it
Speaker 1: in six weeks, you know, So don't laugh at that.
Speaker 2: Absolutely, So we've been doing this since the nineteen fifties.
Speaker 2: Let's talk about what kind of data we're collect to
Speaker 2: inform these models when we get back from the break.
Speaker 2: All right, and we are back, So now we're going
Speaker 2: to talk about the kinds of data that we use
Speaker 2: to make weather predictions. And I'm gonna bet it involves satellites.
Speaker 1: Always going with space first, right, yep, yep. It does
Speaker 1: involve satellites, but there's an amazing, incredible variety of data
Speaker 1: sources we have to understand the weather. And yet still
Speaker 1: it's not nearly enough. Right as you'll hear, our weather
Speaker 1: prediction would be so much better if we had more data.
Speaker 1: We're really limited by the data. But we have lots
Speaker 1: of different kinds. We have weather stations on the surface
Speaker 1: and so a lot of these are called like automatic
Speaker 1: weather stations that are scattered across the country. They're just
Speaker 1: basically a bunch of sensors with a battery and like
Speaker 1: either a wind turbine or a solar panel to get power,
Speaker 1: and they measure things like temperature and pressure and wind
Speaker 1: speed and precipitation, just the raw measurements you need to know,
Speaker 1: like what's going on out there, what is the state
Speaker 1: of the weather right now, because again, if you want
Speaker 1: to predict the future weather, you've got to know what's
Speaker 1: going on right now.
Speaker 2: So is this like a citizen science thing where like
Speaker 2: I could purchase one of these weather stations and hook
Speaker 2: it into what's happening at like the national level.
Speaker 1: Yes and no. So there are a few sort of
Speaker 1: official stations. There's a bunch of different networks. The highest
Speaker 1: quality ones. There's like ten thousand of these scattered around
Speaker 1: the earth, and they're operated by weather services and government agencies.
Speaker 1: But there's a bigger network of like quarter million of
Speaker 1: these things. Some of these are personal weather stations that yeah,
Speaker 1: people just build and publish the data. And there's an
Speaker 1: amazing network it's called COCO ras COOCOHS Community Collaborative Rain,
Speaker 1: Hail and snow Wow. If you can just like build
Speaker 1: your own device and add it to the network and contribute,
Speaker 1: and I think that's super awesome because it's definitely limited
Speaker 1: by the data we have. One problem is that these
Speaker 1: things tend to be where the people are, Like we
Speaker 1: have a few, you know, top of Mount Washington or whatever,
Speaker 1: but mostly these things are put up by people where
Speaker 1: people are near, and so like there's lots in India,
Speaker 1: for example, but very few across Siberia, And often the
Speaker 1: best ones are at places like airports. Airports really need
Speaker 1: to know whether so they have excellent weather stations. But
Speaker 1: like the weather at LaGuardia is not the same as
Speaker 1: the weather in Manhattan, and so often the airport weather
Speaker 1: stations are very very precise and used heavily in the models,
Speaker 1: but they're not giving you the measurements exactly where you
Speaker 1: want them to be.
Speaker 2: Okay, So is that a problem for just the people
Speaker 2: who are in areas where there's not enough weather detectors
Speaker 2: or is that a problem for all of us, because
Speaker 2: what's happened in Siberia is important to what's happening in India.
Speaker 1: Yeah, what happens in Siberia doesn't stay in Siberia. Unfortunately.
Speaker 1: It contributes to uncertainty and error across the model. And
Speaker 1: the Earth is one big system, which is why you
Speaker 1: can't just be like, I'm only going to predict the
Speaker 1: weather Manhattan. I only need to think about Manhattan. You
Speaker 1: need to model the whole planet in order to get
Speaker 1: the weather in Manhattan. So yeah, absolutely, And that's why
Speaker 1: we have lots of different kinds of sensors, not just
Speaker 1: these automatic weather stations. We also have things like weather radar,
Speaker 1: and you might have seen these on your local weather channel.
Speaker 1: Like let's look at the Doppler, this measure of precipitation.
Speaker 1: It also measures the velocity of those rain drops. And
Speaker 1: this is a really cool story because it comes out
Speaker 1: of World War Two. It's another example of like reusing
Speaker 1: military technology and infrastructure after World War two to do
Speaker 1: some science.
Speaker 2: Thank you war Oh boy, hot take pull it back.
Speaker 1: Well, there you are again finding the silver lining. Tens
Speaker 1: of millions of people died, but we have better weather predictions.
Speaker 1: So the way radar works is that it sends these
Speaker 1: pulses of microwave radiation. The wavelengths are like one to
Speaker 1: ten centimeters, that's the microwave region. And it sends a
Speaker 1: pulse for like a microsecond, and then it listens for
Speaker 1: return signals. So like it sends this pulse and rain
Speaker 1: drops will reflect, so it gets the signal back and
Speaker 1: it listens for like a few milliseconds, and then it
Speaker 1: sends another pulse, and so it can tell where the
Speaker 1: clouds are, and it can tell the velocity of those
Speaker 1: clouds by the change in frequency. This is the Doppler effect, right,
Speaker 1: And this is exactly the same effect as like stars
Speaker 1: are moving away from you, so their light is red
Speaker 1: shifted when the radar pulse comes back. If the frequency
Speaker 1: is shifted, you can tell which direction that rain drop
Speaker 1: is moving.
Speaker 2: So that sounds complicated because like there's not just one
Speaker 2: rain drop out there, there's a bunch and so I
Speaker 2: can imagine like your pulse getting lost as it bounces
Speaker 2: off of multiple rain drops and doesn't make it back
Speaker 2: to you. What am I miss saying? This sounds hard?
Speaker 1: No, it is hard, But you're not detecting individual rain drops.
Speaker 1: You're detecting clouds mostly like which direction is this cloud going?
Speaker 1: And you know, initially this was a problem because in
Speaker 1: World War Two, radar operators were trying to use radar
Speaker 1: to discover like enemy planes, and they noticed, like man,
Speaker 1: clouds are getting in the way. And then other folks
Speaker 1: were like, oh wait, you can use radar to see clouds. Awesome,
Speaker 1: and so and so. Then after World War Two they
Speaker 1: started using this to measure the velocity of clouds and
Speaker 1: to see them. And there's this moment in like nineteen
Speaker 1: sixty one when Hurricane Carlo was approaching the coast of
Speaker 1: Texas and Dan Rather went down there to a weather
Speaker 1: station and they were using radar to see the clouds
Speaker 1: and to see their direction, and he had them drawn
Speaker 1: like the coast of Texas over this image of the
Speaker 1: hurricane that showed everybody like, wow, this is a massive
Speaker 1: hurricane moving fast towards the shore and probably save thousands
Speaker 1: of lives because he publicized this like incoming storm much
Speaker 1: more rapidly than we could otherwise without this kind of technology.
Speaker 3: Wo.
Speaker 1: Yeah, this weather radar is really helpful.
Speaker 2: Do you think it still has the same effector or
Speaker 2: do you think people are just kind of like, oh,
Speaker 2: there's hurricanes, I've seen them before. They get big, and
Speaker 2: they don't always leave.
Speaker 1: People don't always leave. There's always somebody who's going to
Speaker 1: ride out the storm, right, Yeah. And I don't know
Speaker 1: with the psychology there, but at least now we can
Speaker 1: inform people further in advance and let them know where
Speaker 1: these things are likely to go. But there's still always uncertainty,
Speaker 1: and we'll talk about that in a minute. You don't
Speaker 1: just have one weather prediction. You have an ensemble. You
Speaker 1: have an envelope of predictions because you don't have perfect
Speaker 1: data and you don't have a perfect model, and so
Speaker 1: often what you do is you vary your data a
Speaker 1: little bit within the uncertainties and run the model again,
Speaker 1: and then you get a different prediction. And I'll give
Speaker 1: you a sense of the spread of the possible outcomes.
Speaker 1: So you might see when there's like a hurricane approaching
Speaker 1: the coast of Florida. They have a bunch of possible trajectories.
Speaker 1: Those are all like different runs of the weather model,
Speaker 1: assuming different initial conditions. Because we have uncertainty, we don't
Speaker 1: have perfect data.
Speaker 2: I personally really enjoy learning about the uncertainty in life
Speaker 2: in general. And whenever I look at those I have
Speaker 2: this weird feeling of security, like, yeah, like they figured
Speaker 2: it out and they know what the errors are. We're good,
Speaker 2: we know what to avoid. Maybe that's maybe that's a
Speaker 2: little bit giving it a little too much credit, but
Speaker 2: it's still amazing.
Speaker 1: And another really important source of uncertainty in our models
Speaker 1: is what's happening in the ocean, like how hot is it,
Speaker 1: how cold is it, how things circulating, all this kind
Speaker 1: of stuff, and so we need data about the ocean.
Speaker 1: But not a lot of people live in the ocean,
Speaker 1: so we don't have like these automatic weather stations, but
Speaker 1: we do have buoy's. These are like floating weather stations,
Speaker 1: and around the world there's a couple of thousand of these,
Speaker 1: depending on the type, that have these like temperature sensors
Speaker 1: on the surface. But we also have this hilarious data
Speaker 1: from what's going on deeper in the ocean that historically
Speaker 1: has come from people on ships taking a bucket, dropping
Speaker 1: it into the ocean, pulling it up, and then measuring
Speaker 1: the temperature of the water. And it's like really that
Speaker 1: lo fi. But for many years that's all we had.
Speaker 1: We had like no other way reliably to know how
Speaker 1: cold is it in the ocean. And this is an
Speaker 1: example of like it's not just data. You need to
Speaker 1: take data and interpret it and clean it and correct it.
Speaker 1: And I spoke to an expert here, you see, I
Speaker 1: Jane Baldwin, who told me that like you had to
Speaker 1: correct for like how long the bucket was out of
Speaker 1: the water before they dunked the thermometer in it, and
Speaker 1: how Japanese ships and US ships used a different bucket
Speaker 1: and it had different effects, and like you got to
Speaker 1: really know, you got to be an expert and how
Speaker 1: this data was taken and what it really means.
Speaker 2: Yeah, So for a while I was doing some water
Speaker 2: quality work and we had this like tube and you
Speaker 2: would put the tube underwater and then you'd sort of
Speaker 2: press a button and like caps would pop into place
Speaker 2: on both sides of the tube, and then you could
Speaker 2: lift it up and so you could get a water
Speaker 2: sample from specifically different depths, and it was it was
Speaker 2: always kind of fun to use that device.
Speaker 1: Yeah, and you might think, like that's ridiculous, what a
Speaker 1: silly system, And it's a little bit silly, but if
Speaker 1: it's the only data you have, it's better than no data. Yeah, right,
Speaker 1: as long as you understand the uncertainties in it. And
Speaker 1: my friend Jane was telling me that misunderstanding this data
Speaker 1: might be a cause for some weird pauses and global
Speaker 1: warming trends, that it could just be like a misinterpretation
Speaker 1: of this ship bucket dunk data.
Speaker 2: I know, we're so moch.
Speaker 1: These days. We have these cool robotic floats that like
Speaker 1: float on the surface of the ocean and then dive
Speaker 1: down up to two thousand meters measure things down in
Speaker 1: the ocean, and then come back up and beam it
Speaker 1: to satellites or whatever. So we're getting better obviously, yes,
Speaker 1: But you know what's really valuable is longitudinal data. Like
Speaker 1: you want data as far back as you can so
Speaker 1: you can understand bigger trends. So you can't just like say, oh,
Speaker 1: that ship bucket dunk data is ridiculous, let's ignore it.
Speaker 1: It's the only data you have for like thirty years
Speaker 1: and so trends in that data do tell you something
Speaker 1: very cool.
Speaker 2: Okay, so now we've gone down deep, how do we
Speaker 2: get data from up high? Yeah?
Speaker 1: Because the weather's not just at the surface, right, And
Speaker 1: the weather folks call the surface the two meter level
Speaker 1: because they want to measure the temperature not on the
Speaker 1: ground literally, but like two meters up like where your
Speaker 1: head is, essentially. But they also need to know what's
Speaker 1: going on even further, so we take measurements in the
Speaker 1: upper atmosphere. We use weather balloons, and these are literally
Speaker 1: what you imagine. You put like a bunch of helium
Speaker 1: in a balloon and you put a weather station on
Speaker 1: it that commissure altitude, pressure, temperature, humidity, wind speed, et cetera.
Speaker 1: And you just let it go and it rises because
Speaker 1: helium rises, and as it goes up, the balloon expands
Speaker 1: because the pressure in the upper atmosphere is less, and
Speaker 1: eventually it pops and then the thing comes back down.
Speaker 1: So these are like one time uses, right, and they
Speaker 1: can go up like twenty kilometers.
Speaker 2: When I visited Saint Catherine's University in Minnesota to give
Speaker 2: a talk, they had a special day where they launched
Speaker 2: a weather balloon, like you know for my visit and
Speaker 2: you know, did a demonstration for all the students and
Speaker 2: it was the coolest thing ever.
Speaker 1: Super cool. Right, These are amazing experiments. And I know
Speaker 1: people who do physics experiments on balloons where they like
Speaker 1: go the Antarctic and they let up a balloon and
Speaker 1: it floats in the atmosphere for like up to a
Speaker 1: month or something, and like, wow, that's really brave work
Speaker 1: because you spent like four years building this instrument and
Speaker 1: then you're putting it on a balloon and to the
Speaker 1: atmosphere and sometimes it's just gone, like it just disappears
Speaker 1: and you lose your whole thesis. And this seems like
Speaker 1: kind of bespoke, right, and it is. There's like a
Speaker 1: couple hundred launches per day in the United States, but
Speaker 1: it's not reliable. It's not like the place you've visited.
Speaker 1: They do exactly the same balloon launch every single day
Speaker 1: at the same time, right, which is the most useful
Speaker 1: thing for a weather model. It's like reliable data and
Speaker 1: we don't have a lot of them. But again, this
Speaker 1: helps you probe the upper atmosphere. We don't have many
Speaker 1: ways to measure the temperature in the upper atmosphere. This
Speaker 1: is a really powerful one.
Speaker 2: Do we also use planes.
Speaker 1: We do use planes because every airplane you've been on
Speaker 1: has really valuable information about weather because it samples from
Speaker 1: the two meter level up to like thirty thousand feet.
Speaker 1: An aircraft, of course have sensors to measure wind speed
Speaker 1: and temperature and all this kind of stuff. So every
Speaker 1: commercial airplane has these sensors, collects this data and then
Speaker 1: sells it to the government. Noah buys this data because
Speaker 1: there's so many flights, Like look at a map of
Speaker 1: airplane flights for a single day in the United States.
Speaker 1: There are so many flights they crisscross the country, and
Speaker 1: it's incredibly valuable data. And this is usually very high
Speaker 1: quality data because it's very important for these planes to
Speaker 1: understand the weather.
Speaker 2: I had no idea Noah was getting access to all
Speaker 2: of that data. That's super cool.
Speaker 1: It's super cool. Basically, any way you can imagine to
Speaker 1: learn the state of the weather somewhere on Earth, somebody's
Speaker 1: doing it, because the more data we have, the better
Speaker 1: these models get. But then of course we can go
Speaker 1: all the way up to space, right because there are
Speaker 1: places where there are no automatic weather stations and there
Speaker 1: are no buoys and there are no airplane flights, yet
Speaker 1: they still contribute to the weather prediction in Kansas or
Speaker 1: in Mexico City or whatever. So we have satellites, and
Speaker 1: since about nineteen seventy nine we've had weather satellites. We
Speaker 1: of course had satellites earlier than that, but none devoted
Speaker 1: to like gathering weather data, and the primarily cover things
Speaker 1: like storm systems and cloud patterns. They can tell you
Speaker 1: where the snow is. They can also tell you like
Speaker 1: where wildfires are, which is an important part of the weather.
Speaker 2: Yeah, and so they.
Speaker 1: Can't directly measure like what is the temperature in Houston
Speaker 1: right now, but they can make indirect measurements like, for example,
Speaker 1: they can measure the amount of infrared radiation from the surface,
Speaker 1: and that is connected to the temperature, but it's actually
Speaker 1: connected to the temperature of the surface, not the two
Speaker 1: meter level. Right, So, like how hot is the blacktop
Speaker 1: in Houston right now? That's what your satellite is telling you,
Speaker 1: and you have to use that to infer how hot
Speaker 1: is it two meters above the blacktop in Houston, which
Speaker 1: is what you actually want to know.
Speaker 2: That sounds hard, it's hard.
Speaker 1: Yeah, exactly. And so we also don't have a lot
Speaker 1: of satellites because they're expensive. There's something like twenty satellites
Speaker 1: are between geostationary and polar orbits. Eight of them are
Speaker 1: operated by Noah. But there's a bunch out there. But
Speaker 1: my friend the climate scientist says that we might be
Speaker 1: on the verge of having a lot more data because
Speaker 1: launching stuff in a space is cheaper, and now we
Speaker 1: can do like small satellites, CubeSats. These might give us
Speaker 1: more data, not as high quality as like the dedicated
Speaker 1: you know, super nerd designed billion dollar satellites. On the
Speaker 1: other hand, we don't know what the future holds for
Speaker 1: like supporting and operating these satellites. This requires money to
Speaker 1: fund these things and have people interpreting these things. We
Speaker 1: don't know how long the government is going to continue
Speaker 1: to support it. They could just like unfund this stuff
Speaker 1: or turn off weather stations. And you know it's more
Speaker 1: than just like, oh, we turned it off for a year.
Speaker 1: Having continuous records is super important for these models for
Speaker 1: predicting the immediate weather, but also for the long term
Speaker 1: climate models, which are essentially an average of the weather,
Speaker 1: and so even turning it off briefly, could be very
Speaker 1: damaging for our abilities to do long term predictions.
Speaker 2: And I'm kind of blown away by the fact that
Speaker 2: we only have twenty satellites. I guess I had assumed,
Speaker 2: since you know, there's like five thousand satellites up there
Speaker 2: or something. I guess most of them are dedicated to
Speaker 2: like beaming cat videos to us from anywhere in the world.
Speaker 2: But like weather seems so important, you know, for farmers,
Speaker 2: for like people who are traveling, just like for everything.
Speaker 1: Yeah, that's true, but the satellites don't give you a
Speaker 1: direct measurement of what you're most interested in. They're essentially
Speaker 1: like really good for filling in the gaps or places
Speaker 1: where you have no other measurements. So yeah, it would
Speaker 1: be great, but they're also super expensive, so you'll hear
Speaker 1: at the end, I asked one of the climate scientists
Speaker 1: I spoke to, like, if you had a billion dollars,
Speaker 1: what would you spend it on? And satellites is not
Speaker 1: their top priority.
Speaker 2: Huh okay, all right, so maybe twenty is the right number.
Speaker 1: So you have all these different kinds of data. You
Speaker 1: have automatic weather stations, you have radar, you have buois,
Speaker 1: you have ship bucket data, you have weather, balloons, aircraft,
Speaker 1: you have satellites. What you need for your model are
Speaker 1: the initial conditions. What you need for your model as
Speaker 1: a set of what is the temperature and the pressure
Speaker 1: and the humidity everywhere on the planet right now, so
Speaker 1: that I can run it and predict it in the future.
Speaker 1: And there's not a trivial step from like here I
Speaker 1: have all this data to what are the initial conditions?
Speaker 1: Because the data can disagree, right you have multiple measurements,
Speaker 1: sometimes nearby, using different kinds of sensors. How do you
Speaker 1: incorporate that, How do you clean this data, how do
Speaker 1: you decide what to use? How do you merge all
Speaker 1: of this into your best prediction? And so there's a
Speaker 1: lot of work in this area. It's called data assimilation
Speaker 1: of running sort of mini models fluid dynamics to do
Speaker 1: like physics informed interpolations between the places where you don't
Speaker 1: have measurements, and to factor in the various uncertainties from
Speaker 1: the various different kinds of measurements. So sometimes you like
Speaker 1: back up the model a little bit and feed in
Speaker 1: some data and then use it to predict the current
Speaker 1: initial conditions before you go to your full model, and
Speaker 1: then you do what we talked about earlier, which is ensembling.
Speaker 1: You say, well, here's my best guess for the weather,
Speaker 1: like right now, before we even run the model. But
Speaker 1: I'm going to make like one hundred versions of it,
Speaker 1: and each one I'm going to tweak my assumptions a
Speaker 1: little bit. So I get an envelope where I hope
Speaker 1: reality somehow is described by one of these models, or
Speaker 1: is near one of these models, or the spread in
Speaker 1: these models describes my uncertainty in the state of the
Speaker 1: initial conditions. We haven't even done any predictions yet. This
Speaker 1: is just like measuring what's happening.
Speaker 2: Now right well, and what's so stressful for me to
Speaker 2: thinking about this is like your data are coming in constantly,
Speaker 2: and so it's not like you do this once and
Speaker 2: then you're like, okay, good, now we will project. It's
Speaker 2: like every second more data are coming in. So this
Speaker 2: has to be like a constant process that's happening over
Speaker 2: and over again. And integrating the information into bigger models
Speaker 2: in exactly, amazing, exactly.
Speaker 1: And yeah, and we haven't even talked about how the
Speaker 1: models work.
Speaker 2: And so let's take a break and when we get back,
Speaker 2: we'll talk about how those models work. All right, So
Speaker 2: now we have all of this data and you've got
Speaker 2: it into an ensemble and you sort of maybe know
Speaker 2: what's happening right now plus some uncertainty. How do you
Speaker 2: now predict what's going to happen next?
Speaker 1: Yeah, so simple. You just break out your pencil and
Speaker 1: paper and you do a bunch of strength theory calculations
Speaker 1: and that's it. Right, it's just like physics. It into
Speaker 1: the future.
Speaker 2: Finally, strength theory is useful.
Speaker 1: Yeah, unfortunately not, as we said earlier, like you can't
Speaker 1: describe everything. You have to make assumptions about what you're
Speaker 1: going to calculate and what you're going to simplify. Otherwise
Speaker 1: you're never going to be able to make a prediction, right,
Speaker 1: or your predictions are going to be done in a
Speaker 1: thousand years for the weather that's happening in an hour,
Speaker 1: and that's not useful. And so it's always a question
Speaker 1: of how to judiciously make those assumptions. So the current
Speaker 1: state of the art for weather modeling has basically two
Speaker 1: big pieces. One is directly model the atmosphere itself as
Speaker 1: if it's a big fluid. So you use like navea
Speaker 1: Stokes equations and think about how it flows and how
Speaker 1: temperature moves through it. That's the dynamical core of the model.
Speaker 1: But there's a bunch of stuff that influences the atmosphere
Speaker 1: that you don't explicitly include in the model. The clouds,
Speaker 1: the convection, the ocean, the radiation, the surface temperature, all
Speaker 1: this kind of stuff. Your model doesn't explicitly include that stuff.
Speaker 1: We don't have like a complete model of the ocean
Speaker 1: or the clouds, etc. And so we have like various
Speaker 1: inputs to this core piece that they call parameterizations that
Speaker 1: like capture the big picture effects of all these pieces
Speaker 1: that are not included directly in our model but are
Speaker 1: influencing us.
Speaker 2: So I feel like this is a question where you
Speaker 2: think to yourself, am I about to ask a really
Speaker 2: stupid question? But I'm going to move forward because that's
Speaker 2: my job in this podcast. The atmosphere is not a
Speaker 2: fluid though, right, So like guy, am, so, what why
Speaker 2: are we doing? Are we modeling it as a fluid
Speaker 2: because we just can't model it as something else because
Speaker 2: it's too complicated and fluids are a simplification or Daniel,
Speaker 2: I don't think the atmosphere is a fluid.
Speaker 1: Well, it depends on what you mean by a fluid,
Speaker 1: and you know, when it comes to like how things
Speaker 1: flow and pressure, et cetera, the fluid dynamic equations do
Speaker 1: describe the atmosphere. And so you know, fluid doesn't mean liquid, right,
Speaker 1: Fluid is about how things flow and move. Right. So,
Speaker 1: for example, like the mantle of the Earth is a fluid.
Speaker 1: It flows. It's not a liquid, right, It's this weird
Speaker 1: solidy kind of state and it moves, but it also flows,
Speaker 1: and so you can describe it and it has convection.
Speaker 1: You can describe it with fluid equations. And so the
Speaker 1: Navi or Stokes equations are these famous equations that describe
Speaker 1: fluid dynamics and they're pretty good at modeling the atmosphere.
Speaker 1: They're not perfect, right, They're not perfect, but they're pretty
Speaker 1: good at it. So, yeah, I think fluid is not
Speaker 1: a liquid. It's just things that flow.
Speaker 2: Okay, in my head, fluid is synonymous with liquid. And
Speaker 2: so I have learned something today that will probably help
Speaker 2: me not look silly in the future. That's great.
Speaker 1: No, it was a great question. And so this is
Speaker 1: the big picture. You have the dynamical core, and then
Speaker 1: you have these parameterizations and we'll dig into that and
Speaker 1: we'll describe sort of the US approach to it. But
Speaker 1: there are three sort of major weather communities. There's the US,
Speaker 1: the UK, and the Japanese, and they have slightly different approaches,
Speaker 1: which is good because you know, different predictions can crosscheck
Speaker 1: each other. But then some people think it's bad because hey,
Speaker 1: let's pool all of our resources and make one big
Speaker 1: global model, and that's awesome, but then you only have
Speaker 1: the one and you're not sure. Maybe it's all wrong.
Speaker 1: There's a lot of debate about, you know, how to
Speaker 1: deal with global questions and global resources.
Speaker 2: But who's the best.
Speaker 1: Oh, I'll give you a ranking at the end. Okay,
Speaker 1: all right, So the dynamical core, Right, think of the atmosphere.
Speaker 1: We're going to treat the atmosphere basically like a spherical cow. Right,
Speaker 1: It is a spherical fluid, right, the atmosphere. Yeah, it's
Speaker 1: a thin shell around the Earth, and you know the
Speaker 1: temperature and the pressure, and then you can describe how
Speaker 1: it's going to flow, how the temperature and pressure are
Speaker 1: going to change using the Navier Stokes equations. So Navia
Speaker 1: Stokes is a set of really gnarly equations. They're nonlinear
Speaker 1: partial differential equations. A differential equation is one where like
Speaker 1: the value depends on how quickly it's changing. For example,
Speaker 1: like antecology, you have differential equations that describe like predator
Speaker 1: and prey. Right, these two things are coupled, and so
Speaker 1: these are nonlinear partial differential equations, which means like that
Speaker 1: depends on things squared or cubed. All that to say,
Speaker 1: they're very, very difficult to solve. In fact, differential equations
Speaker 1: in general are hard to solve. If you've taken a
Speaker 1: differential equations class, it's basically like differential equations are not
Speaker 1: solvable except for these four examples that we have answers
Speaker 1: to and we know how to solve them, and so
Speaker 1: you just got to memorize those. It's a little bit
Speaker 1: like chemistry.
Speaker 2: I gotta say, oh no.
Speaker 1: It's mostly unsolved, right. And the Navias Stokes equations we've
Speaker 1: known about them for like two hundred years that were
Speaker 1: initially developed to try to answer these questions about like
Speaker 1: how do things flow and how does momentum and mass
Speaker 1: flow through pipes, et cetera. Essentially, people took Newton's second
Speaker 1: law ethicals MA and applied it to fluids and then
Speaker 1: added terms for like stress and pressure and viscosity. And
Speaker 1: it's like a real triumph that we can describe this
Speaker 1: at all. But calculationally it's a real bear. You can't
Speaker 1: like sit down and derive a solution and say, here's
Speaker 1: my pressure and temperature. Let me crunch it through the
Speaker 1: Naviastokes equation. It's going to give me a formula. It's
Speaker 1: all numerical approximations, which means it takes a lot of
Speaker 1: computing to go from now to one second from now
Speaker 1: or two seconds from now, and that computing means approximating things.
Speaker 1: You're like doing numerical derivatives instead of exact analytical derivatives.
Speaker 2: Okay, so some of that got pretty complicated, But what
Speaker 2: I guess what I want to know is when this
Speaker 2: is all done, I feel like, if we are trying
Speaker 2: to model fluids, does this just tell us that, like,
Speaker 2: the wind is now over here going this fast but
Speaker 2: before it was over there? And how many are we
Speaker 2: going to get to like how you get from that
Speaker 2: to like and it's raining, Because that seems like a
Speaker 2: different problem sort of than how fluid is moving around.
Speaker 1: Yeah, so there's a couple of things to know there.
Speaker 1: You're exactly right. It takes the current conditions and tries
Speaker 1: to predict the future conditions. And those conditions are pressure
Speaker 1: and temperature, wind speed, humidity, right, these kinds of things.
Speaker 1: But because we're solving this numerically, we can't solve it everywhere.
Speaker 1: If you have a formula and analytics description you can
Speaker 1: write down, for like, where is my ball as I've
Speaker 1: thrown it? I can write that down on a piece
Speaker 1: of paper a formula. I can tell you where the
Speaker 1: ball is at any point in time. You ask me
Speaker 1: any point literally any value of T, I could plug
Speaker 1: it into my formula give you an answer. But if
Speaker 1: I don't have a formula, that's called an analytics description.
Speaker 1: If all I have is a numerical estimate, then I've
Speaker 1: made a grid. I've said I'm going to sample it
Speaker 1: at time one time, two time, three time four, I'm
Speaker 1: going to make an estimate of those times, and it
Speaker 1: don't have an answer everywhere. And that's the situation we
Speaker 1: have with weathers that they put a grid on the
Speaker 1: planet and they estimate what's going to be the weather, temperature,
Speaker 1: et cetera, in a grid of points, not everywhere over
Speaker 1: the planet. And you might think, oh, I bet that
Speaker 1: grid's pretty small, right, maybe they measure down to the
Speaker 1: centimeter or something. No, the grid sizes are like ten
Speaker 1: kilometer cubes.
Speaker 2: What, Yes, that's too big.
Speaker 1: It's too big, right. They are averaging the temperature and
Speaker 1: the humidity over cubes of atmosphere ten kilometers on a side,
Speaker 1: it's crazy and I think that's way too big. On
Speaker 1: the other hand, that's still a lot of cubes, right,
Speaker 1: Like the atmosphere is a lot of ten kilometer sized cubes.
Speaker 1: And then the time steps are tens of minutes, right,
Speaker 1: And this is awesome that we can even do this.
Speaker 1: It requires massive supercomputers. We'll talk about it in a minute.
Speaker 1: But the problem is that it ignores a lot of
Speaker 1: little details like how big is a cloud? Usually they're
Speaker 1: like a kilometer or less. And so you're missing out
Speaker 1: on a lot of stuff by making your grid. Anything
Speaker 1: that happens that's subgrid. That's crucial and important, but it's
Speaker 1: small than the size of your grid is not being
Speaker 1: described by your model. But your question was like, is
Speaker 1: this directly outputting? Like, hey, it's going to rain on
Speaker 1: Kelly's picnic. In a sense, yes, the direct outputs are
Speaker 1: things like temperature, pressure, humidity, and those are enough to
Speaker 1: tell you like, okay, it's going to rain because the
Speaker 1: pressure and humidity are above some threshold or whatever. So
Speaker 1: it's not directly outputting like three centimeters of snow. There's
Speaker 1: another step you have to take after that, but it
Speaker 1: feeds into that. So those are the inputs you need
Speaker 1: to the next step, which says how much snow is
Speaker 1: going to fall?
Speaker 2: Okay, So let me see if I can do a
Speaker 2: super simplified version of this. You get all of the
Speaker 2: data that you have about a square in the grid,
Speaker 2: and you do the best job you can to sort
Speaker 2: of summarize it and ensemble it, and then you put
Speaker 2: it in the model the runs through the equations. Than
Speaker 2: does the information from the surrounding grids feed into your
Speaker 2: grid as well, because you would, okay, because you would
Speaker 2: expect there to be similarities between closely related squares in
Speaker 2: the grid.
Speaker 1: Yeah, you can't solve one grid at a time. You
Speaker 1: have to solve all the grids. To grids touch each
Speaker 1: other and influence each other and wind flows right right,
Speaker 1: And that's why Siberia affects Manhattan over time because you've
Speaker 1: propagated these things from grid cell to grid cell.
Speaker 2: Absolutely, So does Siberia have bigger grid cells or just
Speaker 2: the same number of small grid cells each with poorer
Speaker 2: data in them.
Speaker 1: Yeah, great questions. So some of these models are adaptive, right,
Speaker 1: they have bigger grid cells where we have more uncertainty,
Speaker 1: and smaller we have more data. The most precise ones
Speaker 1: are the UK supercomputers. They go down to two kilometers
Speaker 1: in some cases. Some of them are like fixed grids,
Speaker 1: and some of them are adaptive exactly. It depends a
Speaker 1: little bit on the model. But you know, there's lots
Speaker 1: of details that are not described here, and these are
Speaker 1: called the parameterizations, like especially subgrid stuff and exchanges with
Speaker 1: other parts of the system. They're not just the fluid.
Speaker 1: And one important thing are the clouds. Like you cannot
Speaker 1: model every individual cloud because clouds are smaller than your
Speaker 1: grid size. We do not have the compute to do that.
Speaker 1: People have tried, and you can like do dedicated runs
Speaker 1: on subsets to try to resolve clouds, but then you
Speaker 1: don't have enough computing to do like ensembles. So you
Speaker 1: can be like one prediction you're like, well, here's a prediction,
Speaker 1: but I don't know what the uncertainties are on it
Speaker 1: at all. And so instead what you tend to do
Speaker 1: is parameterize the bulk outcomes, you know, the vapor, the clouds,
Speaker 1: the liquid, the ice, the rain, the snow, etc. The condensation,
Speaker 1: all this kind of stuff. You try to like grab
Speaker 1: all that average over what's happening in that grid cell
Speaker 1: and use that to inform your naveor Stokes equation. So
Speaker 1: things you're not explicitly modeling, you're sort of like averaging
Speaker 1: over You're losing all the details and saying like, well,
Speaker 1: on average, this is going to be the effect of
Speaker 1: clouds on my grid cell.
Speaker 2: Do you think that as Oh, well, I was going
Speaker 2: to say, do you think that as we continue to
Speaker 2: have more and more computing power and more and more supercomputers,
Speaker 2: at some point we'll be doing better here? But we
Speaker 2: were just talking about how More's law. We've maybe hit
Speaker 2: the end of that. So are we like, is this
Speaker 2: as good as wherever we're going to get at it?
Speaker 2: This is probably an end of the podcast question, but
Speaker 2: I'm thinking it right now.
Speaker 1: No, I think that there's lots of possibilities for making
Speaker 1: this faster and more efficient, and not just wait till
Speaker 1: computers get faster. Okay, there's definitely clever ideas and we'll
Speaker 1: get there. Yeah, But there are lots of parts of
Speaker 1: the weather that are not directly described in the dynamical core,
Speaker 1: and not just the clouds, but also things like convection,
Speaker 1: like vertical transport of heat. You know, especially there is
Speaker 1: complex boundary mixing near the surface, like the lowest kilometer
Speaker 1: or so of the atmosphere, where you have like heat
Speaker 1: from the surface and turbulent momentum exchanges as wind is
Speaker 1: like hitting mountains and stuff. These things. You can't model
Speaker 1: all of those details, and so you have like parameterization
Speaker 1: schemes that model the turbulence and the boundary level mixings.
Speaker 1: There's radiation from the surface also, right that changes from
Speaker 1: day to night. You have models of vegetation and snow
Speaker 1: how those things couple. But then the biggest one is
Speaker 1: the ocean. Right, Like we would love for our models
Speaker 1: to include also a Navio Stoke simulation of the whole ocean, right,
Speaker 1: might as well do that because the ocean it plays
Speaker 1: a big role. But we don't have the compute for
Speaker 1: that at all, So we just like use a slab
Speaker 1: ocean model. We just say, let's just assume the ocean
Speaker 1: is like simple, and we have a certain temperature, and
Speaker 1: we assume like how the energy transfers from the boundaries,
Speaker 1: and it's really quite simplified. But that's we're just limited, right.
Speaker 1: We don't have great data in the ocean, and we
Speaker 1: don't have the compute to also model the ocean as
Speaker 1: well as the atmosphere. So places where we don't do
Speaker 1: our best approximation, which is like Navia Stokes equations of
Speaker 1: the atmosphere, we have simplified versions, which are called parmetization. Says,
Speaker 1: feed in to the core. But in the end you
Speaker 1: got to take it to the computers. And this is
Speaker 1: why we have like massive supercomputers to make weather predictions.
Speaker 1: So Noah in the US has a couple of really
Speaker 1: big facilities. They're called Dogwood and Cactus. One of them
Speaker 1: is in.
Speaker 2: Virginia, You're welcome, everyone.
Speaker 1: And one of them is in Arizona, and they're huge,
Speaker 1: amazing computers. Those two have like twelve point one petaflops.
Speaker 2: You made that word up.
Speaker 1: It sounds like a made up word. Peta means quadrillion
Speaker 1: and flops are floating point operations. So you know it
Speaker 1: takes the computer time to add like three point nine
Speaker 1: to one to fourteen point four two and floating point
Speaker 1: numbers those numbers with a dot in them, right, not
Speaker 1: integers are more computationally expensive to add or subtract. And
Speaker 1: that's what most of these models do. They're like, add
Speaker 1: this number, multiply by this, and so this is like
Speaker 1: the way you measure the speed of a computer. And
Speaker 1: so these computers can each do twelve point one quadrillion
Speaker 1: floating point operations per second.
Speaker 2: Wow.
Speaker 1: Right, imagine the guys back in the nineteen twenties, they're
Speaker 1: like adding two numbers. It probably takes them a minute, right,
Speaker 1: or they're super good, takes them twenty seconds. The computer
Speaker 1: does twelve quadrillion a piece per second. Right. So together
Speaker 1: with all of their computers, Noah has about fifty petaflops
Speaker 1: and that's what it uses to run its model. And
Speaker 1: so that's the state of the art. In the United States.
Speaker 1: The Europeans have a couple of computers. Is one really
Speaker 1: big one in Bologna called Bull Sequanya and has about
Speaker 1: thirty petaflops. But the biggest, most powerful weather computer in
Speaker 1: the world is in the UK. It's at the Met
Speaker 1: Office and it's built by Microsoft and has sixty petaflops.
Speaker 1: And this is why the UK has some of the
Speaker 1: best weather prediction in the world because they have the
Speaker 1: biggest computer. They beat us. Yeah exactly, they just spent
Speaker 1: more money. They bought more computer. This is literally like
Speaker 1: money equals computing.
Speaker 2: Tea drinking bastards.
Speaker 1: Good day. Yeah, well, you know, they got tricky weather
Speaker 1: over there, and so they need it. It's an island
Speaker 1: after yet guys and the Japanese. The Japanese have a
Speaker 1: big investment in weather prediction computers. Also, this one called
Speaker 1: Prime HBC. It has thirty one petaflops. So these are
Speaker 1: really powerful devices and they run these huge models. And
Speaker 1: you know, think about what the model does. It predicts
Speaker 1: the state of the atmosphere on these pretty chunky grids.
Speaker 1: But it's still it's a huge amount of data, like
Speaker 1: every few minutes, every ten kilometers. My friend Jane was
Speaker 1: telling me that sometimes the data is so big that
Speaker 1: you just throw it away. You run it, you get
Speaker 1: like a summary number, but you can't keep all of
Speaker 1: the data because it would just like fill up all
Speaker 1: of the hard drives. Everywhere. And this is familiar for
Speaker 1: me because like at the LHC, we also we run
Speaker 1: these experiments every twenty five danoseconds. We throw away most
Speaker 1: of the data from that because it would just fill
Speaker 1: up all of our storage. And they're in a similar situation.
Speaker 1: They produce more data than they can store.
Speaker 2: So are these facilities where the Navier Stokes equations are
Speaker 2: being run or are these facilities where you have the
Speaker 2: output from each grid and now you are translating that
Speaker 2: into information about where the rain is falling?
Speaker 1: Both? Yeah, okay, So these programs do the data similation,
Speaker 1: come up with the current initial conditions, and then also
Speaker 1: run the model forward to make those predictions, and from
Speaker 1: that glean things like weather details, snowfall, et cetera. And
Speaker 1: so what you're getting on your phone, what you're hearing
Speaker 1: on TV is not just like what Jane, your local forecaster,
Speaker 1: came up with. She's relying heavily on these central predictions
Speaker 1: from major resources. Right, So, for example, if worldwide governments
Speaker 1: decide we don't need these computers anymore, we don't need
Speaker 1: these satellites, it's not like you could be like, that's cool,
Speaker 1: I got my local weather forecaster I don't need you.
Speaker 1: No your local weather forecaster is getting that information from
Speaker 1: these big models that are being run by the government.
Speaker 2: Oh wow, yeah, and did Noah get cuts recently? I'm
Speaker 2: going to bet they did.
Speaker 1: There were some talk about cuts. I don't know how
Speaker 1: much of that is going through. It's all kind of scary.
Speaker 1: It's hard to know.
Speaker 2: Yeah, okay, all right, we won't get into that. Moving on.
Speaker 1: But amazingly, currently we can pretty accurately predict the weather
Speaker 1: five to six days in the future, you know, and
Speaker 1: you mostly remember when the weather prediction is wrong. You
Speaker 1: mostly don't realize that most of the time it's right. Yeah,
Speaker 1: you know, it tells you it's going to rain, it
Speaker 1: tells you it's going to be study. It's mostly correct.
Speaker 1: It's amazing, but you know, there's still challenges. Things are
Speaker 1: not perfect. One of the biggest challenge is just incomplete information.
Speaker 1: You know, we don't have sensors in enough places, and
Speaker 1: we don't have enough sensors, and sometimes this data availability changes,
Speaker 1: you know, things go offline or come online. Now your
Speaker 1: model has to compensate for that. I don't have the data.
Speaker 1: Do I assume it's similar to the past. Do I
Speaker 1: try to ignore that kind of data. It's not easy
Speaker 1: to be running a model if the puts are constantly changing.
Speaker 2: The bucket had a hole in it, So now you
Speaker 2: don't have good bucket data exactly exactly.
Speaker 1: They got a new kind of bucket. You don't know
Speaker 1: how to calibrate it. I spoke to John Martin, he's
Speaker 1: a professor of meteorology, and he said that this might
Speaker 1: be the biggest challenge is how to combine the data
Speaker 1: to make a high quality initial state. That's one challenge.
Speaker 1: The other are these subgrid parameterizations. Can we develop better
Speaker 1: models for turbulent flow at the boundaries or for latent
Speaker 1: he's released back into the environment. And another limiting factor
Speaker 1: is just the computing cost. More computes, more GPUs from
Speaker 1: Nvidia means smaller grids, which means the effect of these approximations,
Speaker 1: these parameterizations is less. Another continuing challenge are rare and
Speaker 1: extreme events, like we're pretty good at predicting the bigger picture,
Speaker 1: like is it going to be sunny here, is it
Speaker 1: going to be rainy here? But like small, rare extreme events,
Speaker 1: like there's a tornado right here, that's more challenging because
Speaker 1: they depends in detail well on things that happen within
Speaker 1: the grid that we're averaging over. And so there's a
Speaker 1: lot of work being done right now. One thing we're
Speaker 1: hoping to do is like, let's reduce the grid size,
Speaker 1: get more computing, more accurate. Right. But another really promising
Speaker 1: error of research is using machine learning. Oh, there's this
Speaker 1: movement in many fields of science to use machine learning
Speaker 1: to make predictions by essentially skipping the physics. Like, the
Speaker 1: physics is hard, it takes a lot of time to
Speaker 1: push the initial conditions through these equations. In the end
Speaker 1: you have an input and an output. And the idea is, well,
Speaker 1: can we train machine learning, not a chatbot, not LMS,
Speaker 1: it's AI, but it's not LMS to map the initial
Speaker 1: conditions to the output because in the end it's just
Speaker 1: a mapping and one could learn it. And so we
Speaker 1: have these machine learning models that are simple functions that
Speaker 1: take the input and give you the output, and they
Speaker 1: don't have the physics encoded in them, but they learn
Speaker 1: from the simulations, they learn the patterns, they learn what
Speaker 1: the rules are implicitly, and so you don't have to
Speaker 1: go through all the detailed calculations. So this can dramatically
Speaker 1: speed up your predictions. We use these the large handroom
Speaker 1: collider all the time so that we don't have to,
Speaker 1: for example, model every single particle that might hit the
Speaker 1: detector and create another particle and another particle. We can
Speaker 1: learn to predict the final thing we're interested in and
Speaker 1: to sort of leapfrog over all the tiny details.
Speaker 2: And his machine learning being used right now for weather predictions,
Speaker 2: or they're just starting to work on how you would
Speaker 2: do that.
Speaker 1: They're using that now. There's sort of experimental. But there's
Speaker 1: a guy here at you see Irvine, Mike Pritchard, who
Speaker 1: is an expert in this kind of stuff, and it's
Speaker 1: very powerful, absolutely cool. Yeah. So I asked John Martin,
Speaker 1: if I give you a billion dollars to improve weather predictions,
Speaker 1: what would you do, And he said he would spend
Speaker 1: a billion dollars on ocean probes, like he wanted a
Speaker 1: more substantial understanding of how water is circulating in the
Speaker 1: ocean and temperature in the ocean and how that's all working.
Speaker 1: Because his suspicion was like, we're right next to this
Speaker 1: other big fluid that's affecting our temperaatereure, and we don't
Speaker 1: have much enough data about it. If we just knew
Speaker 1: more about the ocean, and this just highlights like how
Speaker 1: little information we have. It's not just a question of
Speaker 1: like puzzling out the rules of the universe, but just
Speaker 1: like knowing what's happening. If we had more data everywhere
Speaker 1: about temperature, pressure, about cosmic rays, we would just learn
Speaker 1: so much about the universe. And we have so few
Speaker 1: ways to probe. But we're really just like taking the
Speaker 1: tiniest teaspoon out of this massive river of data and
Speaker 1: trying to use that to understand the whole river. It's crazy.
Speaker 2: How good do you think weather prediction would have to
Speaker 2: be before people stopped complaining about weather prediction?
Speaker 1: I asked John that question, and his prediction was, quote,
Speaker 1: the complaining will never stop amazing. I think that, you know,
Speaker 1: weather prediction has improved a lot over the last few decades.
Speaker 1: It used to be you couldn't get any reliable prediction
Speaker 1: more than a day in advance. Now five six days,
Speaker 1: it's pretty reliable. But people expect that and they get
Speaker 1: used to it, and they're like, what, you didn't predict
Speaker 1: the weather or my ski trip in two weeks? I'm you,
Speaker 1: And so yeah, the complaining will never stop because we
Speaker 1: always just get used to the level of technological prowess
Speaker 1: that we've had, and so people want more because it's
Speaker 1: so important and it's a hard problem. There's so much
Speaker 1: physics here, there's instrumental science, there's so many different kinds
Speaker 1: of science at interface with each other. It's really an
Speaker 1: exciting field. And let me throw a special thanks to
Speaker 1: Professor Jane Baldwin here you see I who told me
Speaker 1: a lot about weather predictions, and Professor John Martin at
Speaker 1: Wisconsin who answered a lot of naive questions of mine.
Speaker 1: Thanks to both of you.
Speaker 2: Thank you community. All right, see you all next time.
Speaker 2: I hope the weather is nice where you are.
Speaker 5: It always will be nice where I am.
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