Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says, it's like
Speaker 1: rain on your wedding day. I'm Jonathan Strickland, I'm La,
Speaker 1: and I'm Joe McCormick. And today you don't need a
Speaker 1: weatherman to know which way the wind sucks, because we
Speaker 1: are going to be talking about predictive modeling of weather,
Speaker 1: weather forecasting. Yeah, we've talked in the past a lot
Speaker 1: about weather and sometimes when I wasn't here. Yes, we
Speaker 1: had a two parter about the potential future of weather
Speaker 1: control with special guest Julie Douglas back in February. Yeah,
Speaker 1: And one of the interesting things about that episode is
Speaker 1: I think in the end we decided, after all of
Speaker 1: our research that really the best avenue for humans to
Speaker 1: sort of get a grip on the weather is not
Speaker 1: to try to control it, because in many ways that
Speaker 1: is a fool's are end, it's physically impossible, Yeah, but
Speaker 1: to instead try to understand it, just to have a
Speaker 1: better better idea of what's coming your way and win right.
Speaker 1: The further out and the more accurately you can forecast
Speaker 1: the weather, the better prepared you are for the various
Speaker 1: eventualities that will unfold, things like flooding. Like if you
Speaker 1: know ahead of time that flooding is is almost certainly
Speaker 1: going to affect a certain region, you can start to
Speaker 1: take steps to protect people and property in that area.
Speaker 1: Sandbags are an amazingly effective low tech solution to things
Speaker 1: or maybe out there. They don't do much good if
Speaker 1: they're not there, Yes that's true. If they're they're somewhere else.
Speaker 1: If they're in a warehouse, that warehouse maybe nice and dry,
Speaker 1: but the area that you were hoping to say, will
Speaker 1: be rather squishy. Uh same same sort of thing that
Speaker 1: if you're talking about, like you're looking ahead at a
Speaker 1: very long term forecast and you were to say, oh,
Speaker 1: it looks like there's not going to be any rainfall
Speaker 1: for quite some time, you can start to make plans
Speaker 1: for that so that you're not stuck in a situation
Speaker 1: where it happened but you weren't aware that that was
Speaker 1: going to you know that was going to be the case.
Speaker 1: Uh So, in other words, we don't necessarily try and
Speaker 1: control it. We just get a better idea of what
Speaker 1: is going to happen, so we're more prepared for that. Yeah,
Speaker 1: And we glanced across that topic in those Future of
Speaker 1: Weather Control episodes. But uh yeah, so we wanted to
Speaker 1: talk about that today. And we were also inspired by
Speaker 1: a video episode that you did, Jonathan about Bubble Yeah,
Speaker 1: the Bay of Bengal Boundary Layer Experiment or Bubble Yes.
Speaker 1: I I said in the video that I consider myself
Speaker 1: a bubblehead because I'm a huge fan of this project.
Speaker 1: The video and that just came out this week. You
Speaker 1: can check it out on YouTube this very day if
Speaker 1: you would like to, or on fw thinking dot com.
Speaker 1: But specifically, Bobble is a very particular regional weather predicting project. Yeah.
Speaker 1: It's a study of how a number of complex factors
Speaker 1: in the Bay of Bengal come together to create a
Speaker 1: monsoon season of heavy rains in northern India every year.
Speaker 1: And it's a particular interest to researchers because that monsoon
Speaker 1: season drives the agriculture and the water supply and the
Speaker 1: energy supply for about a billion people, so seven of
Speaker 1: the world's population, no big uh, And and clearly variations
Speaker 1: in the seasonal norm of rainfall either too wet or
Speaker 1: too dry reek havoc on this region. So what if
Speaker 1: we could predict those variations before they happen, disaster could
Speaker 1: hypothetically be if not prevented, then then perhaps mitigated. Uh Okay.
Speaker 1: And so besides being a project that could hypothetically change
Speaker 1: the lives of a billion people, Bobble is really cool
Speaker 1: because it's kind of a microcosm of weather prediction research
Speaker 1: in general, because it's it's so multi disciplinary. You've got
Speaker 1: ships and satellites making classic observations in the bay. You've
Speaker 1: got robotic submarines that are checking out the situation under
Speaker 1: the surface. You've got researchers designing did little simulations to
Speaker 1: crunch the data, and they'll compare their models to the
Speaker 1: actual season's results to see where they went right and
Speaker 1: where they need to make improvements. Right. So we're going
Speaker 1: to talk more about Bubble in detail a little bit later,
Speaker 1: But first, as per our usual m O, we like
Speaker 1: to go back and look at how we got to
Speaker 1: where we are now. Like, like, obviously, when you look
Speaker 1: back to the ways humans tried to forecast the weather
Speaker 1: centuries ago, they're supercomputers were sorely underperforming. Yeah, so those
Speaker 1: advocacies didn't process it quite the same thing exactly. You
Speaker 1: have all of your little scribes working in parallel, attempting
Speaker 1: in vain to simulate weather well, and the thing they
Speaker 1: were trying to calculate was how angry the god was.
Speaker 1: So there were several steps along they were going, going
Speaker 1: off the path in a few different ways. So let's
Speaker 1: let's talk about let's talk about you know, kind of
Speaker 1: the a shoudn't approach to forecasting weather and work our
Speaker 1: way up to what we tend to do today. Okay, well,
Speaker 1: joking aside, there were of course lots of just straight
Speaker 1: up magical thoughts about how to control the weather or
Speaker 1: predict the weather originally, and so that's you know, that
Speaker 1: goes that's a tradition that goes way way back into
Speaker 1: the ancient world. Has to do a lot with that
Speaker 1: with astrology, um and as kind of an offshoot of astronomy,
Speaker 1: but mostly it was astrological yeah, um, But those those
Speaker 1: sort of magical predictive interpretations. Aside, there were actually throughout
Speaker 1: history plenty of weather superstitions and sort of rules of
Speaker 1: thumb that actually do have grains of truth to them. Yeah,
Speaker 1: there's a really great article on how stuff works Dot
Speaker 1: com about this, and I did a what the Stuff
Speaker 1: video about it once and and it's it's interesting, how
Speaker 1: many of them really do hold water? Yeah? Yeah, Well
Speaker 1: it makes sense because you figure people are paying attention
Speaker 1: to what has happened, and they realize that there's a
Speaker 1: pattern where when once a circumstances happened, then typically you
Speaker 1: might get a lot of rain. And so you start
Speaker 1: to make a rule about that, and you know it's
Speaker 1: in some cases it can be you can be completely
Speaker 1: off base. It's just coincidence, or you do what I
Speaker 1: like to call you. It's it's called, you know, a
Speaker 1: confirmation bias, but I would call it. I would say
Speaker 1: the van is always at the corner, which is where
Speaker 1: whenever there's a van parked at the corner, you notice it.
Speaker 1: Whenever there's not a van parked at the corner, you
Speaker 1: don't register it. So to you, the van is always
Speaker 1: at the corner. Uh. In those cases, obviously it may
Speaker 1: be that you've made an observation, but it's a faulty one. However,
Speaker 1: there's some that are at least somewhat you know, reliable. Yeah,
Speaker 1: here's one. You've probably heard some version of this weather
Speaker 1: prediction before and often in couplet form about red sky
Speaker 1: at morning, sailor take warning, red sky at night, sailor's delight.
Speaker 1: I feel this. I feel badly for that one sailor
Speaker 1: right like it's just like like, oh man is going
Speaker 1: to be awful because it says sailor was singular, so
Speaker 1: it's one guy. Well, that's the way I always heard that.
Speaker 1: There are other versions. But this is old, old, old,
Speaker 1: It goes way back. People have been using this forecasting
Speaker 1: rule for at least a couple of thousand years. We
Speaker 1: know because it shows up in the Bible, shows up
Speaker 1: in the Gospel of Matthew chapter sixteen, where uh it
Speaker 1: says quote the in RSV, the Pharisees and sad Juicees
Speaker 1: came and to test Jesus. They asked him to show
Speaker 1: them a sign from heaven, and he answered them, when
Speaker 1: it is evening, you say it will be fair weather,
Speaker 1: for the sky is red, and in the morning it
Speaker 1: will be stormy today for the sky is red and threatening.
Speaker 1: You know how to interpret the appearance of the sky,
Speaker 1: but you cannot interpret the signs of the times. So
Speaker 1: obviously that they're trying to make a spiritual or religious
Speaker 1: point there, but but just incidentally in the narrative, at
Speaker 1: least we know that some people back then we're saying
Speaker 1: this rule. Um so, so the author of this passage
Speaker 1: had heard of this before, and crazily enough, it is
Speaker 1: partially true. So what's the scientific basis for this? Why
Speaker 1: would the color of the sky at sunset or sunrise
Speaker 1: have anything to do with the weather? Well, strongly tinted
Speaker 1: red light at sunrise and sunset actually tells you something
Speaker 1: about the contents of the atmosphere between you and the sun.
Speaker 1: So specifically, it tends to indicate dry air filled with
Speaker 1: dust and solid particles which we would call aerosols. So
Speaker 1: these particles in the air are the cause of the
Speaker 1: reddening of the light because dust and aerosols in the
Speaker 1: atmosphere scatter visible light in a way that makes the
Speaker 1: light turn red. Uh And in turn, this dry, dusty
Speaker 1: air tends to indicate that you're in a high pressure region,
Speaker 1: which means less cloud formation and less likelihood of a storm.
Speaker 1: A low pressure region, on the other hand, would mean
Speaker 1: that that they're tended to be more cloud formation and
Speaker 1: more storms. So if you are looking through red tinted
Speaker 1: atmosphere to see the sun you're looking through a high
Speaker 1: pressure region that's less likely to rain on you. Uh
Speaker 1: And and the thing about the atmosphere is that it
Speaker 1: travels in in the same direction. Well yeah, and that's
Speaker 1: what this rule doesn't work everywhere, because while the Sun's
Speaker 1: path is unidirectional around the Earth, of course it's actually
Speaker 1: the Earth's rotation, but metaphorically, the Sun's path is unidirectional.
Speaker 1: I'm gonna need to see a site for that. The
Speaker 1: weather tends to travel in different directions depending on where
Speaker 1: you live. So if you're in the Arctic or the Antarctic,
Speaker 1: or in the tropics, the sort of three extreme bands,
Speaker 1: weather patterns more often move east to west, and this
Speaker 1: rule doesn't apply, or in fact, actually I guess the
Speaker 1: opposite would apply, right, But for the mid latitudes, you know,
Speaker 1: sort of the temperate zones between the tropics and the
Speaker 1: Arctic or the Antarctic, this is actually more often true
Speaker 1: because the weather patterns more often moved from west to east.
Speaker 1: And what that means is, if you look towards the sunset,
Speaker 1: you're looking west at the weather that's probably coming your way.
Speaker 1: And if a red light scattering patch is to the
Speaker 1: west of you, that's a high pressure area. Probably that's
Speaker 1: probably headed your way, meaning the weather will probably be fine. Uh.
Speaker 1: And of course why would red sky at morning be
Speaker 1: a problem. Well, that's because if you're in the mid
Speaker 1: latitudes again looking east toward a sunrise, you're seeing the
Speaker 1: weather that has probably already passed by you. And high
Speaker 1: and low pressure systems often do trade off in cycles.
Speaker 1: But you may have noticed that I kept saying the
Speaker 1: word probably over and over again there, And that's because
Speaker 1: like all weather prediction, this is probabilistic. Using the system,
Speaker 1: you can predict the weather better than random guessing, meaning
Speaker 1: better than with fifty percent accuracy, but still not anywhere
Speaker 1: near ad accuracy. Right, So there could be some mornings
Speaker 1: where you see a red sky and every it's perfectly fine,
Speaker 1: it's beautiful weather. And there might be some evenings where
Speaker 1: you see red sky and the next morning you're soaking
Speaker 1: in it. Yeah, So the weather, the weather is just
Speaker 1: very complex. It's it's difficult to it's difficult to protict
Speaker 1: with accuracy even now using the supercomputers and everything that
Speaker 1: we have involved in all the data we have, but
Speaker 1: this one piece of folk science and weather forecasting, it's
Speaker 1: not the only one that turns out to have some
Speaker 1: basis in truth. Right. Yeah, a few others that I
Speaker 1: wanted to touch on because they're they're kind of a favorite. Uh.
Speaker 1: Ring around the moon rain real soon. Have you guys
Speaker 1: ever heard this? This is the thing that you've heard. No, no,
Speaker 1: not at all, but I believe you. Yeah. Uh, there's
Speaker 1: there's another kind of version of it that goes when
Speaker 1: a halo rings the moon or sun rains approach and
Speaker 1: on the run. I love that sounds like something from
Speaker 1: one of their songs, and and and the thing that's
Speaker 1: going on here it is it does hold true more
Speaker 1: than fifty percent at the time. I think it's it's
Speaker 1: a similar probabilistic concept to to the red red sky
Speaker 1: at night Sailor's Delight sort of thing. But so, so
Speaker 1: what's going on here is that, um, when you've got
Speaker 1: a halo that frames the moon or the sun, it's
Speaker 1: produced by by moonlight or sunlight refracting through high whispy
Speaker 1: clouds that are made of ice crystals, and uh and
Speaker 1: those those ice crystals. That type of weather pattern typically
Speaker 1: occurs in siro stratus clouds that often move in ahead
Speaker 1: of weather fronts, where where temperature differentials are going to
Speaker 1: cause warm air to move upward, deensing moisture and potentially
Speaker 1: forming rain clouds potentially, So science science thumbs up on
Speaker 1: that one. And still not the only Moon related weather,
Speaker 1: you know, kind of folklore, right, sure, Sure there's also
Speaker 1: clear moon frost soon, Yeah, which which makes perfect sense
Speaker 1: because because clear nights do often mean that cold weather
Speaker 1: is on the way, Because as far as the planet
Speaker 1: is concerned, a cloudless sky is sort of like having
Speaker 1: a bed without blankets. Uh. You know, During the day,
Speaker 1: the Earth absorbs sunlight and and can converts it into
Speaker 1: into heat that we all appreciate to certain degrees um.
Speaker 1: When when the sun sets, the surface begins radiating that
Speaker 1: heat back out, and lacking clouds to capture the heat
Speaker 1: and snuggle it in all all tight and close, the
Speaker 1: surface and the lower atmosphere grow increasingly cold. In fact,
Speaker 1: I think in a tech stuff episode I talked about
Speaker 1: this as a means of creating ice in certain regions,
Speaker 1: where you'd leave out a pan a shallow pan of
Speaker 1: water outside because the heat radiates out and it actually
Speaker 1: becomes ice that way in certain regions of the world.
Speaker 1: That's how it was done before refrigeration reached those areas,
Speaker 1: so it's kind of neat. Yeah. My favorite one though,
Speaker 1: has to do with cows. Of course, there is there's
Speaker 1: folklore about uh or not, like a folk saying, but yeah,
Speaker 1: that cows will lie down when it's about to rain,
Speaker 1: mm hmm. And and I will, I will admit that
Speaker 1: cows lie down for probably many reasons, like they're tired.
Speaker 1: But um, but but this one, but this one might
Speaker 1: be due to to body heat. Okay, cows tend to
Speaker 1: stand more often when they're overheating, you know, in order
Speaker 1: to breathe everything out right. Sure, yeah, so so as
Speaker 1: seated cow could arguably I mean that the weather is
Speaker 1: cooling down and therefore a storm is a bruin. I
Speaker 1: also like in the notes you have, this one may
Speaker 1: have a leg to stand on. There. There are so
Speaker 1: many puns in this in this house stuff works article.
Speaker 1: And I yeah, I didn't write it, no, oddly enough,
Speaker 1: Yeah it was not it was not, I but but there.
Speaker 1: But there are definitely some some more systematic approaches that
Speaker 1: people have come up with over the years, sure, apart
Speaker 1: from just sayings in folk wisdom. One big one through
Speaker 1: in history is Aristotle's Meteorologica. That's Aristotle's hugely influential treatise
Speaker 1: on winds, water, weather, and some other stuff like earthquakes.
Speaker 1: Like much of Aristotle, it is both startling lye intelligent
Speaker 1: and hilariously wrong about lots of things. I enjoyed the
Speaker 1: section on how earthquakes are caused by evaporation of rains
Speaker 1: that have soaked into the earth and exhalations of breath
Speaker 1: from the ground. But until a few hundred years ago,
Speaker 1: I think the Aristotle's works were sort of the Western
Speaker 1: world's gold standard for knowledge about the causes of weather.
Speaker 1: And it wasn't until you know, fairly recent times that
Speaker 1: we started being able to do much better. Yeah. I
Speaker 1: mean generally speaking, you started getting into like the mid
Speaker 1: to late Renaissance, and you start seeing some other thinkers
Speaker 1: propose alternatives to some Aristotle's ideas. But yeah, his his
Speaker 1: approach or his his observations and his his uh writings
Speaker 1: held sway for centuries. Yeah yeah, um, And and some
Speaker 1: of those new ideas came about alongside changes in concepts
Speaker 1: about physics and also about astronomy, like like greater knowledge
Speaker 1: of astronomy um up to and including the publication of almanacs,
Speaker 1: which were very very popular publications back in the day.
Speaker 1: Apparently the only thing that outsold almanacs in the seventeenth
Speaker 1: century in England was the Bible, so lots of people
Speaker 1: were purchasing these things. Um. And back in the late
Speaker 1: seventeen hundreds and early eighteen hundreds, a couple different mathematicians
Speaker 1: slash astronomers started publishing yearly farmers almanacs here in the
Speaker 1: in the States and what would be the United States
Speaker 1: later on the North America continent. Yes. Um. The formulae,
Speaker 1: the formulas that they use in order to make these
Speaker 1: predictions are to this day guarded as family or company secrets.
Speaker 1: It turns out like it it could be something like
Speaker 1: consulting the family cat. We don't know, Yeah, and like
Speaker 1: intensely guarded. I love I I love stories about old
Speaker 1: farmers are almanac and UH and the Farmers Almanac, both
Speaker 1: of which are punctuated slightly differently in terms of the
Speaker 1: possessive s, but just the lower around all of this
Speaker 1: is is delightful. In the case of one of the
Speaker 1: two almanacs, I forget which one. UH, there is a
Speaker 1: Caleb Weatherbe who's sort of like the James Bond of
Speaker 1: of this of this company. Because Caleb Weatherby is not
Speaker 1: his real name, I'm not sure if it's a dude.
Speaker 1: Uh I there have been this series of Caleb Weatherby's
Speaker 1: who have been the one entrusted with the knowledge of
Speaker 1: how the of how the almanac does it stuff. It's
Speaker 1: like cecil atoms, yes, of straight of the straight dope. Yeah,
Speaker 1: there have been many cecil atoms. Yeah so, but so
Speaker 1: no one. No one knows exactly how they make their predictions,
Speaker 1: but supposedly take stuff like planetary positions and sun spots
Speaker 1: and lunar cycles and title patterns all into account, and
Speaker 1: I get the distinct idea reading stories about this that
Speaker 1: meteorologists find find almanax like this rather quaint. Uh what
Speaker 1: One researcher who looked into the accuracy of these kind
Speaker 1: of things found that they get their long ranging predictions
Speaker 1: because they make predictions a year or two out correct
Speaker 1: about of the time. Is that a high number or alone?
Speaker 1: Like how much variability is there and what they could
Speaker 1: be predicting? You can't because you wouldn't say, like is
Speaker 1: that better than chance? Because it's hard to say without
Speaker 1: knowing all the variables. Oh, sure, I'm not sure. They
Speaker 1: claim to get it right about eight percent of the time,
Speaker 1: and and that is that is sore a gap. Yes,
Speaker 1: but luckily we didn't. We we haven't had to continue
Speaker 1: relying just on stuff like this forever because eventually, UH physics, Yeah,
Speaker 1: people started figuring out how hydro dynamics therm thermodynamics both work,
Speaker 1: and once humanity got a really good grip on these concepts.
Speaker 1: Strangely enough, around the same time that the American farmers
Speaker 1: almanacs started publication, the science of meteorology could take off,
Speaker 1: and by the early nineteen hundreds, a Norwegian physicist by
Speaker 1: the name Wilhelm Erknus devised the first known seven equation
Speaker 1: formula for for using observations of existing weather conditions to
Speaker 1: solve for future conditions. Taking taking into consideration like like
Speaker 1: pressure and temperature and humidity and then three aspects of
Speaker 1: atmospheric motion. That forms the foundation. Definitely, I mean, the
Speaker 1: more information we have, obviously, the better picture picture we
Speaker 1: have what's going on right now, and the more um
Speaker 1: the more accurate we can make a forecast for the future.
Speaker 1: Of course, the further out you go from the current
Speaker 1: UH scenario, the current the current condition. Small differences in
Speaker 1: in what you've predicted versus what actually happened add up tremendous. Yes, yeah, well,
Speaker 1: I mean it's a it's a sort of principle of
Speaker 1: physics that you can extrapolate on a very simple scale
Speaker 1: or on a very huge scale. On the simple scale,
Speaker 1: imagine aiming an arrow at a target. If you shift
Speaker 1: your aim a millimeter over and the targets a foot
Speaker 1: of way a foot away, it's not gonna make much
Speaker 1: of difference. If the targets a hundred feet away, it
Speaker 1: will make a difference, right, So, same sort of idea
Speaker 1: is that you know the the temporal distance as opposed
Speaker 1: to physical distance, it does make a big difference. But
Speaker 1: of course, once you get into the modern history of
Speaker 1: our technological and scientific capabilities for predicting whether one big difference,
Speaker 1: of course is just going to be the scale of
Speaker 1: of observation, increasing the number and accuracy of observational platforms
Speaker 1: to collect data about the weather, so we have more
Speaker 1: information to work with, uh, And that's pretty easy. But
Speaker 1: another thing is that we can sometimes overlook the simple
Speaker 1: ways that common technological innovations help us in specific ways,
Speaker 1: And one would be communication technology such as the telegraph
Speaker 1: originally and then like the telephone facts and uh and
Speaker 1: the Internet, and these have allowed people to better understand
Speaker 1: global weather patterns in real time by rapidly sharing and
Speaker 1: comparing information about local weather. Yeah. Computer science also allowed
Speaker 1: prediction to to greatly advanced, starting in the fifties and
Speaker 1: sixties and really ramping up over the past say like
Speaker 1: twenty to thirty years, along with the rate of our
Speaker 1: processing power. So I mean, perhaps obviously, as our computational
Speaker 1: ability and our observational ability have increased, so has our
Speaker 1: forecast accuracy. There was an analysis that was published in
Speaker 1: Nature in and according to that, the forecast accuracy for
Speaker 1: the next three to ten days of weather has improved
Speaker 1: by about a day per decade um, meaning that right
Speaker 1: now our ten day forecasts are as accurate as nine
Speaker 1: day forecasts were in the early odts. So, in other words,
Speaker 1: every decade we go by, we're getting one day better. Yeah.
Speaker 1: I like it. So if I can figure out whether
Speaker 1: or not I need to carry an umbrella with me
Speaker 1: on Friday when it's Monday, and and be reasonably certain
Speaker 1: that that is in fact the right answer, the better
Speaker 1: because I'm not carrying it. If I don't have to write.
Speaker 1: In a decade from now, you'll you'll be able to
Speaker 1: know pretty well on Tuesday. I'm looking forward to that.
Speaker 1: So my suggestion, Jonathan, is that you need to get
Speaker 1: a cooler umbrella that you feel better about carrying all
Speaker 1: the time, Like maybe like a penguin's umbrella, you know
Speaker 1: that shoots machine machine gun fire or has a big
Speaker 1: sword that comes out the end of it. I have
Speaker 1: a blade runner umbrella that's great glowing. Yeah, I've got
Speaker 1: one of those. Um So, who is really in charge
Speaker 1: of gathering and crunching all this data? I mean, I'm
Speaker 1: assuming when I turn on the local news and I
Speaker 1: see the local weather corresponded on the news, that person
Speaker 1: hasn't personally been responsible for gathering and analyzing all that information.
Speaker 1: He has no, no, no, no. The guy I'm imagine
Speaker 1: very specific, that guy launched the satellite uh and has
Speaker 1: collected the data. He built all of the computers himself.
Speaker 1: Uh No. Modernly, weather prediction is a joint public like
Speaker 1: governmental and private industry type of business because that the satellites,
Speaker 1: the computers, the software, and the the human compilation of
Speaker 1: all of this data that go into it is each
Speaker 1: each of those separately are huge expensive arms of the venture.
Speaker 1: So and and going into it, you know, like, of
Speaker 1: course you've got local news stations, which are private companies
Speaker 1: that are reporting on whether but it's also a public service.
Speaker 1: It's it's not just about personal convenience. It's absolutely a
Speaker 1: very critical public service about getting information about big storms, danger, tornadoes, hurricane,
Speaker 1: stuff like that out to the public um And it's
Speaker 1: also partially a a tool for commerce. The more that
Speaker 1: companies can learn about what the weather is going to do,
Speaker 1: the better that they can adjust whatever it is that
Speaker 1: they need to adjust depending on what's sure. Like if
Speaker 1: if you're part of the shipping company, whether you're shipping
Speaker 1: stuff across land or see you need to know these
Speaker 1: sort of things because that can have a real impact
Speaker 1: on everything from a delivery date to the safety of
Speaker 1: the people and the products that you're moving. Weather is important,
Speaker 1: I mean, it's important to have this as accurate a
Speaker 1: picture of what's going to happen. And of course the
Speaker 1: further out you can do that, the more beneficial it
Speaker 1: is for everybody. So that kind of leads us over
Speaker 1: into the discussion of some of the current attempts to
Speaker 1: get an even deeper, more keen understanding of the factors
Speaker 1: that influence whether UM and that kind of brings us
Speaker 1: also to Bobble, to that project we were talking about
Speaker 1: off the coast of India. So Bobble is pretty cool
Speaker 1: in that it's it's relying upon multiple sources to gather
Speaker 1: information UM also that we can get a better understanding
Speaker 1: of the monsoon season in India. So that includes satellite data,
Speaker 1: atmospheric measurements courtesy of an f A a M aircraft
Speaker 1: and I'll go into that in a second, and some
Speaker 1: floats that are carrying scientific equipment, as well as those
Speaker 1: underwater robots that Lauren mentioned that are incredibly cool. I
Speaker 1: was so interested to hear, mostly just about how they
Speaker 1: move through the water because it's a brilliant and simple
Speaker 1: means of propulsion. But first of all, the project has
Speaker 1: a collaboration between India researchers and scientists from the UK,
Speaker 1: specifically the University of East Anglia and the University of Reading,
Speaker 1: and the research will take place during the two thousand
Speaker 1: sixteen monsoon season, which has technically started as we record
Speaker 1: this podcast. It's June and July. So the monsoon season
Speaker 1: is India's rainy season. India gets a lot of its
Speaker 1: rain during the season. Of the rain that falls in
Speaker 1: India falls during the monsoon season, and there is a
Speaker 1: lot of Yeah, we're talking ten ms annually of rain.
Speaker 1: Ten ms, it's thirty three ft or so. In some
Speaker 1: places it's up to eleven ms. It depends on the
Speaker 1: region of India. Um. So the project's goal is to
Speaker 1: gain a deeper understanding of the factors that influence this
Speaker 1: monsoon season and that way we can make better predictive
Speaker 1: models of what areas of India are going to get
Speaker 1: what amount of rain, and that will help subsistence farmers
Speaker 1: plan out there they're farming to make certain that they
Speaker 1: take the best advantage of that. It also will help
Speaker 1: in the case of figuring out this particular region might
Speaker 1: be very susceptible to flooding and we need to take
Speaker 1: measures to protect the people who live there. Right, So
Speaker 1: there's there stands to be a really incredible benefit too.
Speaker 1: Like we said earlier, up to a billion people to
Speaker 1: to cracking this code, to figuring out better how it
Speaker 1: works and therefore how to predict it. Right. So first
Speaker 1: step of course is you gotta get the data right.
Speaker 1: You have to collect the data before you can do
Speaker 1: anything with it, and that's where all of that equipment
Speaker 1: I mentioned comes into play. So first we have the
Speaker 1: f A a M aircraft. F a a M stands
Speaker 1: for a Facility for Airborne Atmospheric Measurements, So it's flying
Speaker 1: through the atmosphere gathering data on the atmosphere as it
Speaker 1: moves through. It's pretty uh interesting. You need to take
Speaker 1: a little look at the picture of of these things
Speaker 1: as a special refitted B a E Systems aircraft out
Speaker 1: of the UK and uh it's the result of a
Speaker 1: collaboration between the Natural Environmental Research Council and the Met
Speaker 1: Office in the United Kingdom. Now, the f a a
Speaker 1: M has a collection of sophisticated instrumentation aboard it. They
Speaker 1: can those instruments can measure everything from radiative transfer so
Speaker 1: essentially the way heat is moving through the troposphere, the
Speaker 1: chemical composition of the atmosphere, humidity, tem sure turbulence, cloud
Speaker 1: physics and more that turbulence in the cloud physics that's
Speaker 1: really important. Things like vertical sheer that has a huge
Speaker 1: impact on weather patterns and it's one of those things
Speaker 1: that we need to have a lot of data on
Speaker 1: in order to really understand what's happening, and the team
Speaker 1: will actually compare the data gathered by the aircraft to
Speaker 1: that from the other sources the floats, the weather satellites
Speaker 1: and underwater robots to get a complete picture of what's
Speaker 1: happening in the bay during the monsoon season. Uh So
Speaker 1: some of that other equipment that the ARGO floats. Now,
Speaker 1: ARGO floats are deployed all around the world, not just
Speaker 1: off the coast of India. In fact, there are more
Speaker 1: than three thousand of them floating in the oceans, and
Speaker 1: they measure temperature, ocean velocity, so the actual velocity of
Speaker 1: the water, the salinity of the upper two thousand meters
Speaker 1: of the ocean. Scientists primarily use ARGO to monitor climate change,
Speaker 1: so they're doing it to see how conditions are changing
Speaker 1: over time to get a better idea of what is
Speaker 1: the x will practical effect of climate change. The data
Speaker 1: data gathered by ARGO is publicly available within a few
Speaker 1: hours of its collection, so um, the scientists on this
Speaker 1: project are going to rely on obviously on the ones
Speaker 1: that are specifically off the coast of India. Then you've
Speaker 1: got those underwater robots, they're called sea gliders. They look
Speaker 1: kind of like um, almost like a torpedo shape. Some
Speaker 1: sometimes they're referred to as like an robotic dolphin, which
Speaker 1: is odd because they don't really have like they're not
Speaker 1: jointed where you have a t now they've got they've
Speaker 1: got a pair of wings that can tilt. But they
Speaker 1: use changes in buoyancy and those wings to create forward
Speaker 1: momentum so they can move through the water. And they
Speaker 1: have a battery inside of them that can actually shift
Speaker 1: around as ballast, and that will allow them to change
Speaker 1: their pitch and roll so they can dive down. They
Speaker 1: can they can move through the water. They do so
Speaker 1: very slowly compared to say a propeller, But unlike a propeller,
Speaker 1: it's in incredibly energy efficient. Yeah, it doesn't have to
Speaker 1: use a lot of energy to change. Uh, it's it's
Speaker 1: position because of the buoyancy and use of its own
Speaker 1: battery is ballast. So therefore, if it's energy efficient, that
Speaker 1: means that it can travel quite a great distance, probably
Speaker 1: on a single charge, without having to go back to
Speaker 1: home base and h and be juice up again. Exactly,
Speaker 1: it can stay under water for a long time and
Speaker 1: can travel a great distance. Really essentially only has to
Speaker 1: surface if you do have to recharge it or for
Speaker 1: it to beam the data back. It's got a radio
Speaker 1: antenna at the tip of it that will poke out
Speaker 1: the water beams that information and the team can gather it. Uh.
Speaker 1: It's really a neat looking device and there are videos
Speaker 1: online that you can watch of it in action. Um.
Speaker 1: They're they're a little expensive there, about a hundred fifty
Speaker 1: pounds sterling each. Uh. The University of East Anglia used
Speaker 1: to have six of them and then lost two of them.
Speaker 1: One of them got run over by a boat. What
Speaker 1: a ship really well, because these things tend to stay
Speaker 1: fairly close to the surface in order to beam information
Speaker 1: back and one and they don't move very quickly and
Speaker 1: they're hard to see. They're not huge right there, about
Speaker 1: the size of a person, but if you're operating a
Speaker 1: large ship like a cargo vessel, you may not see it.
Speaker 1: And a cargo vessel collided with one and destroyed it.
Speaker 1: The second one was lost in Arctic ice. I believe so.
Speaker 1: But there are actually seven of them in operation for
Speaker 1: the Bubble project. UM, so really interesting. They also can
Speaker 1: hold lots of different types of sensors, not just ones
Speaker 1: to measure the various factors in the ocean, but others
Speaker 1: as well, for for things like marine biology. Now, of course,
Speaker 1: in the case of bubble marine biology was not really
Speaker 1: one of the things they were necessarily concerned with. So
Speaker 1: that's not the that's not in the instrumentation um for
Speaker 1: those particular seed gliders. Instead, they're looking at sensors that
Speaker 1: are going to measure stuff like the turbidity of the water,
Speaker 1: the temperature, salinity, and the oxygen content. Now you collect
Speaker 1: all this data with the floats, the robots, the satellites,
Speaker 1: the aircraft, and now you know everything. Now you gotta
Speaker 1: do stuff with it. That's the problem is like like
Speaker 1: for one thing, like you know, just just that information
Speaker 1: alone is incredibly valuable, but without knowing how it all
Speaker 1: interacts with one another, which factors are more important, which
Speaker 1: ones are really impacting the monsoon season the most, which
Speaker 1: are causitive versus just correlative, Right, Like, there may be
Speaker 1: some things that change. Maybe they're changed because the monsoons
Speaker 1: are moving through, not because they change and then cause
Speaker 1: the monsoon. Right, So you've got you've got to determine
Speaker 1: all this. You have to crunch all that information, and
Speaker 1: that's gonna be the next big challenges grabbing all that
Speaker 1: data and doing something useful with it so that then
Speaker 1: you can take that knowledge and communicate it to people,
Speaker 1: so that you can make actual, uh, real world actions
Speaker 1: based upon that data. And this is where we start
Speaker 1: to shift over to a very important tool in weather
Speaker 1: forecasting and weather modeling and climate science supercomputers. Yeah, because
Speaker 1: if you haven't cotton on yet, the problem of weather
Speaker 1: is is a big data problem. Yes, it's a it's
Speaker 1: a huge data problem because we know lots of different
Speaker 1: variables affect weather. We know those variables change greatly over
Speaker 1: spans of time. Right, So you've got a lot of
Speaker 1: information and that information is constantly in flux. So how
Speaker 1: do you process that in a reasonable way. Supercomputers have
Speaker 1: proven to be a really important element of this analysis.
Speaker 1: So part of understanding this is knowing what a super
Speaker 1: supercomputer really is. It's not just a really beefed up PC. Right,
Speaker 1: It's not a beefed up Mac. It's not a beefed
Speaker 1: up Mac either um also known as big Mac. Yeah,
Speaker 1: it's none of those things. Although I mean, Mr Hodgeman,
Speaker 1: if you're listening, you don't need to beef up. We
Speaker 1: like you the way you are. So supercomputers tend to
Speaker 1: be organized in a way where you've got nodes, which
Speaker 1: is essentially either a CPU or a GPU um and
Speaker 1: those are organized into blades. Those blades are further organized
Speaker 1: into racks, which are cooled in some interesting way, usually
Speaker 1: water cooled. Because you get that many processors in a
Speaker 1: place together, they generate a lot of heat. Heat and
Speaker 1: electronics over the long term are not good friends with
Speaker 1: one another. So the in effect is you've got a
Speaker 1: supercomputer that acts kind of like a multi core processor.
Speaker 1: So if you have a multi core processor, you might wonder, well,
Speaker 1: how does this make my computer faster? Well, it works
Speaker 1: really well for certain types of computational problems. Those will
Speaker 1: be problems that could be broken up into smaller bits.
Speaker 1: It works less well for problems where you have to
Speaker 1: solve one problem before you can start on the next problem. Right,
Speaker 1: So if you were to have the first type where
Speaker 1: you have a problem, you can split up into little bit.
Speaker 1: So you can think of that as imagine you've got
Speaker 1: uh A, I like to use this analogy. You've got
Speaker 1: a math class, and in that math class is a
Speaker 1: math genius, and then you've got a bunch of decent
Speaker 1: math students, but they're not of genius level. You've got
Speaker 1: a math problem that's that first type one that could
Speaker 1: be broken up into several smaller problems, and you give
Speaker 1: the math genius the full thing, and you give each
Speaker 1: of the math the good math students part of that problem.
Speaker 1: The group of good math students are more likely going
Speaker 1: to finish it before the math genius, even though the
Speaker 1: math genius has a grasp of mathematics that far outpaces
Speaker 1: that the rest of the class. If the second type
Speaker 1: of problem, like you were talking Joe, then the math
Speaker 1: genius is more likely to finish it because you can't
Speaker 1: divide that problem up and and give each little piece
Speaker 1: to all the different math students. So the math students
Speaker 1: represent that multi core processor, right with a supercomputer. You've
Speaker 1: just got thousands of these processors, like more than eighty
Speaker 1: thousand for some big supercomputers. Right, And so you take
Speaker 1: this problem, the problem being, here are all these variables
Speaker 1: in weather, and I want My solution is I want
Speaker 1: to create a weather simulation so that I can forecast
Speaker 1: what will happen in the future based upon the current situation. Now,
Speaker 1: so that's your first step. You create your model, then
Speaker 1: you look and see if your model is any good.
Speaker 1: One way you can do this actually is to feed
Speaker 1: in data from the past. So let's say that you
Speaker 1: have collected a huge amount of information from two weeks ago. Well,
Speaker 1: you already know what happened after that because it's in
Speaker 1: the past. Sure, so you can you can feed all
Speaker 1: of the information from two weeks in the past into
Speaker 1: the computer and say, h if I modeled this a
Speaker 1: certain way, then do I get like, like, how close
Speaker 1: do I get to what actually occurred after that first week? Right?
Speaker 1: And if and if it turns out that it didn't
Speaker 1: come very close, you start making adjustments. You start saying,
Speaker 1: all right, this one factor that I thought was really
Speaker 1: important turns out maybe it's not so important. And this
Speaker 1: other thing that I kind of overlooked turns out as
Speaker 1: much more instrumental than I had anticipated. And and this
Speaker 1: is a long process, but you you refine that simulation.
Speaker 1: This is a cool way in which weather prediction, I
Speaker 1: think has the potential to be a constantly improving science
Speaker 1: because unlike some disciplines, uh, this is not a field
Speaker 1: in which testing the predictive power of your theory or
Speaker 1: in this case, your algorithm is difficult because compared to
Speaker 1: something like psychology, where the results of your experiment might
Speaker 1: often be very fuzzy and indeterminate, or like particle physics,
Speaker 1: where you might have to test the predictions of your
Speaker 1: theory by building some giant experimental instrument that operates at
Speaker 1: the giga electron volt scale or something like that, the
Speaker 1: weather is not like that. We have tons of data
Speaker 1: on it, always new data coming in. We've got plenty already,
Speaker 1: and we have lots of good ways of measuring it already. Yeah,
Speaker 1: And the problem is really that we have a wealth.
Speaker 1: We have to we have we are we are befuddled
Speaker 1: by our wealth of information, right, Yeah, No, I just
Speaker 1: like I just like the like we have plenty of
Speaker 1: it already. Like I was just thinking, like, well, not
Speaker 1: much weather today. I had a lot of weather yesterday,
Speaker 1: which uh oh there it's from the Mystery Science Theater
Speaker 1: episode of pod People Were the Best. One of the
Speaker 1: characters asks, uh, do you think the weather all hold in?
Speaker 1: One of the viewers comments, no, I think it's just
Speaker 1: gonna stop. That was Tom Servo who said that. I
Speaker 1: remember that. Yeah, No, that's a fantastic episode. So Tangent
Speaker 1: go watch that episode of MST three K. It's one
Speaker 1: of the best ones they ever did. Back to back
Speaker 1: to the their forecasting. So, according to Science Daily, supercomputers
Speaker 1: spend about equal amount of time running their simulations to
Speaker 1: assimilating new real world data into the models. So, in
Speaker 1: other words, half the time you're simulating whether the other
Speaker 1: halftime you're adjusting that simulation so that it more accurately
Speaker 1: reflects the real world. And as we get a better
Speaker 1: understanding of the things that affect whether, we can refine
Speaker 1: that um. A study in Japan ran a global atmosphere
Speaker 1: simulation and found that a weather event event in one
Speaker 1: part of the world can affect other weather events thousands
Speaker 1: of kilometers away. And so it starts to dawn on
Speaker 1: you that in order for you to accurately forecast a
Speaker 1: local weather system, you have to actually look well beyond
Speaker 1: the immediate region, because there are factors that will affect
Speaker 1: that local weather system that are happening really far away.
Speaker 1: And it may be that it's it's something that's not
Speaker 1: i mean, gonna instantaneously affect your local weather, but it
Speaker 1: will have an impact. So maybe something that would have
Speaker 1: normally been a rainstorm, but that's it could potentially turn
Speaker 1: into something much more severe like tornadoes. So it was
Speaker 1: really interesting. And the study included ten thousand, two hundred
Speaker 1: forty simulations, and they divided the global model into twelve
Speaker 1: kilometer sectors, so like a grid of a hundred twelve kilometers.
Speaker 1: Now that's also important because the smaller those squares are
Speaker 1: in the grid, the more data you're feeding into the simulation,
Speaker 1: and the more powerful the supercomputer has to be. Yeah,
Speaker 1: and of course we're always expanding our hardware and software capability.
Speaker 1: So in January, the n o a A announced a
Speaker 1: major upgrade and its Weather and Climate Operational supercomputer system. Uh,
Speaker 1: and this was interesting. The two computers they have are
Speaker 1: called Luna and Surge Urge not like the soda, like
Speaker 1: a wave. Well, yeah, like the soda. Sorry. The Luna
Speaker 1: and Surge are based in Florida and Virginia and each
Speaker 1: one runs at two point eight nine pedal flops for
Speaker 1: a combined five point seven eight pedal flops of computing capacity.
Speaker 1: And that is up from the system's capacity of just
Speaker 1: seven seventy six terra flops. Nothing to sniff at, but
Speaker 1: significantly lower last year. Flops, by the way, stands for
Speaker 1: floating floating point operations per second exactly. So in the
Speaker 1: press release, the you know, administrator Dr Catherine Sullivan said
Speaker 1: that this upgrade would help the organization deal with quote
Speaker 1: the tidal wave of data that new observing platforms will generate.
Speaker 1: Just once again, I think we've sort of said this before,
Speaker 1: but uh, indicating that the problem in weather prediction these
Speaker 1: days is not a data problem, but it's an analysis problem.
Speaker 1: It's the what we do with the data that's where
Speaker 1: the bottleneck is. Right. So also from n o A
Speaker 1: A NOAH, the National Oceanic and Atmosphere Administration. In other words, uh,
Speaker 1: they're running fifteen hour forecasts using something called the high
Speaker 1: resolution Rapid Refresh model, also known as the H triple
Speaker 1: R in meteorological circles. So if you have a meteorologist
Speaker 1: in your family, just ask them how the H triple
Speaker 1: R is going her her or if you want to
Speaker 1: put model in there, it's the HERB. Anyway, the model
Speaker 1: divides the map, the global map up into three kilometer sections.
Speaker 1: So you remember I was talking about the Japanese study
Speaker 1: that was a hunter and twelve kilometers, So this one's
Speaker 1: more precise. It's divided the the entire world into smaller sections,
Speaker 1: which increases the amount of data significantly that they have
Speaker 1: to handle in order to make this fifteen hour forecast.
Speaker 1: That's also why it's only fifteen hours out, because to
Speaker 1: to extend the forecast further would require even greater processing challenges,
Speaker 1: which they're working to overcome and slowly push that number
Speaker 1: further and further out. Um. But it's really interesting that
Speaker 1: they are looking at the world in three kilometer sections.
Speaker 1: It blows my mind because you think how huge uh
Speaker 1: an amount of data that must be that they're dealing
Speaker 1: with consistently, and they're refreshing this hour by hour to
Speaker 1: look another fifteen hours ahead. UM. So, in Europe weather
Speaker 1: satellites are actually more advanced than the ones that we're
Speaker 1: using here in the United States right now, but that
Speaker 1: will change. The US has plans to launch the Geo
Speaker 1: Stationary Operational Environmental Satellite are also known as GOES ER.
Speaker 1: Did they come in the form of a giant slore
Speaker 1: as as GOES are the destructor or? Um? Yeah, I'm
Speaker 1: having Ghostbusters flashbacks on that. But it's scheduled to launch
Speaker 1: in the fall of this year. It will actually become
Speaker 1: the most advanced meteorological satellite in orbit for at least
Speaker 1: a short time, finally outpacing the ones that are are
Speaker 1: currently over Japan and Europe. Other other recent news involved
Speaker 1: IBM spent about two billion dollars acquiring basically everything in
Speaker 1: the Weather Company except for the Weather Channel itself. And uh,
Speaker 1: and so they're apparently gonna pitt Watson against all that
Speaker 1: data and just kind of see what they can do.
Speaker 1: Interesting Watson takes it down? What Watson Watson will take
Speaker 1: all that data and make yet another bizarre and unimaginable
Speaker 1: recipe that involves pot stickers that don't have any of
Speaker 1: the ingredients in them that they claimed that is it
Speaker 1: going to rain next year? First? Grill you r let us?
Speaker 1: Oh man, I still think we have to each take
Speaker 1: one of those recipes, make it and bring it in.
Speaker 1: We never did do that. We should do a live
Speaker 1: show where we subject each other to cooking grill your
Speaker 1: perade olives. I think we all we all will need
Speaker 1: to have a chef hats and and aprons with humorous
Speaker 1: sayings on them. Uh, that's that's what I suggest. All right,
Speaker 1: Well, well well we'll work on that at any rate. Let's
Speaker 1: look at again kind of further off, like what was
Speaker 1: the future going to bring. So once we have these
Speaker 1: more advanced satellites, we're constantly working on building better supercomputers,
Speaker 1: which often are used for this kind of thing, as
Speaker 1: well as other branches of science as well. Uh So,
Speaker 1: for one thing, as we get this greater understanding of
Speaker 1: the global influences of whether we can we can improve
Speaker 1: our forecasting when we understand that an event happening thousands
Speaker 1: of miles away will have an impact on the weather
Speaker 1: in our area and we have a better way of
Speaker 1: of predicting what that impact will be. That's gonna benefit
Speaker 1: people in ways that we can't even really get a
Speaker 1: grip on right now. Um. One of the other things
Speaker 1: we have to remember is that it's a lot easier
Speaker 1: to predict weather in general, that is severe weather. Um.
Speaker 1: So you'll see this on lots of different sites that
Speaker 1: are talking about meteorology. They'll say like, oh, you know,
Speaker 1: we can predict general weather systems out maybe as far
Speaker 1: as a couple of weeks or further. But when you
Speaker 1: start getting into the the the possibility of severe weather,
Speaker 1: it's closer to like five days, and each day out
Speaker 1: is less accurate than the day before, which means that
Speaker 1: when you're looking at the tail end of that forecast,
Speaker 1: you have to keep that in mind. Um. I tried
Speaker 1: to do that all the time when I'm thinking, like, oh,
Speaker 1: I'm going on vacation in two weeks, let me see
Speaker 1: what the weather is gonna be like in ten days,
Speaker 1: And and often I go in with a false sense
Speaker 1: of security, or I'm end up preparing for a rainstorm
Speaker 1: that had just doesn't happen. But as we get more information,
Speaker 1: we get better at anticipating these things and predicting them accurately. Obviously,
Speaker 1: this could help lots in lots of ways, like in
Speaker 1: that commerce that we were talking about, or in travel.
Speaker 1: Absolutely having better weather prediction could have all kinds of
Speaker 1: commercial and environmental bonuses, like imagine being able to reboot
Speaker 1: flights around bad weather systems before storms hit, thus preventing
Speaker 1: having to sit around at the airport all day, or
Speaker 1: or having to have your flight canceled, or even allowing
Speaker 1: pilots to save on fuel by plotting better courses. Also,
Speaker 1: as as Julie brought up, in our prior weather episodes,
Speaker 1: changes in whether change our buying habits, supermarkets could plan
Speaker 1: to stock up on those frier chickens or whatever it is,
Speaker 1: way more in advance. Apparently, apparently during certain disasters, fried
Speaker 1: chicken just flies off the shelves unless which is weird
Speaker 1: because chicken rarely flies even when it's not fried. But
Speaker 1: also there's the issue here in Atlanta. I made the
Speaker 1: joke in our notes that it's not really a joke.
Speaker 1: It's actually just a fact that if there's even the
Speaker 1: hint of snow, you can expect a run on supermarkets
Speaker 1: for all the milk, bread, sometimes bleach bleaches, big yeah,
Speaker 1: and then people get home toilet paper. What do you
Speaker 1: do with this? Yeah, I never buy this to begin with,
Speaker 1: exactly lots of French toast. That's what we're gonna be
Speaker 1: having kids. So yeah, But they having those predicting those
Speaker 1: better forecast means that you know, you can actually prepare
Speaker 1: for that sort of stuff and uh and hopefully not
Speaker 1: encounter things like shortages or or or you know, where
Speaker 1: people go to a store and then they realize that
Speaker 1: they're out of luck because everybody has rushed it. If
Speaker 1: you've got more time to prepare for that, then you
Speaker 1: can build up your inventory and make better profit and
Speaker 1: people can be happy that they can you know, get
Speaker 1: their bread and milk and eggs and make that French
Speaker 1: toast and then when it doesn't snow, everyone complains about
Speaker 1: it the bread and milk and eggs go bad, but
Speaker 1: you don't care. You sold them already. Yeah, yeah, capitalism. So, uh,
Speaker 1: it was fun to kind of look into this. I always,
Speaker 1: I always really enjoy discussing, uh, the idea behind weather science.
Speaker 1: I'm not big on talking about the weather in general,
Speaker 1: but whether science to me, is really neat because you
Speaker 1: start to realize how incredibly complicated it is and how
Speaker 1: much energy are the the energy that are that happens
Speaker 1: to be in these big weather systems like you know,
Speaker 1: we we if you talk about hurricanes, the amount of
Speaker 1: energy and a hurricane is phenomenal. Right, as Lauren has
Speaker 1: so succinctly put it before, there's more wind than truck
Speaker 1: fair enough. So to me, that's why I love talking
Speaker 1: about these things and why I felt that it was
Speaker 1: fun to to come back and revisit this. Plus I
Speaker 1: wasn't in the last couple, so I really wanted to
Speaker 1: kind of jump into it. But guys, if you have
Speaker 1: any suggestions for future episodes of our podcast, let us know,
Speaker 1: send us an email. That address is FW Thinking at
Speaker 1: how Stuff Works dot com, or you can drop us
Speaker 1: a line on Twitter. The handle there is f W Thinking,
Speaker 1: or search f W Thinking and Facebook. Our profile should
Speaker 1: pop right up. You can leave us a message there
Speaker 1: and we look forward to hearing from you, and we'll
Speaker 1: talk to you again really soon. For more on this
Speaker 1: topic and the future of technology, visit forward Thinking dot
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