The Future of Weather Forecasts

about weather and sometimes when I wasn't here. Yes, we had a two parter about the potential future of weather

control with special guest Julie Douglas back in February. Yeah, And one of the interesting things about that episode is I think in the end we decided, after all of our research that really the best avenue for humans to sort of get a grip on the weather is not to try to control it, because in many ways that is a fool's are end, it's physically impossible, Yeah, but to instead try to understand it, just to have a better better idea of what's coming your way and win right.

The further out and the more accurately you can forecast the weather, the better prepared you are for the various eventualities that will unfold, things like flooding. Like if you know ahead of time that flooding is is almost certainly going to affect a certain region, you can start to take steps to protect people and property in that area. Sandbags are an amazingly effective low tech solution to things or maybe out there. They don't do much good if

they're not there, Yes that's true. If they're they're somewhere else. If they're in a warehouse, that warehouse maybe nice and dry, but the area that you were hoping to say, will

be rather squishy. Uh same same sort of thing that if you're talking about, like you're looking ahead at a very long term forecast and you were to say, oh, it looks like there's not going to be any rainfall for quite some time, you can start to make plans for that so that you're not stuck in a situation where it happened but you weren't aware that that was going to you know that was going to be the case. Uh So, in other words, we don't necessarily try and

control it. We just get a better idea of what is going to happen, so we're more prepared for that. Yeah, And we glanced across that topic in those Future of Weather Control episodes. But uh yeah, so we wanted to talk about that today. And we were also inspired by a video episode that you did, Jonathan about Bubble Yeah, the Bay of Bengal Boundary Layer Experiment or Bubble Yes. I I said in the video that I consider myself a bubblehead because I'm a huge fan of this project.

The video and that just came out this week. You can check it out on YouTube this very day if you would like to, or on fw thinking dot com. But specifically, Bobble is a very particular regional weather predicting project. Yeah. It's a study of how a number of complex factors in the Bay of Bengal come together to create a monsoon season of heavy rains in northern India every year.

And it's a particular interest to researchers because that monsoon season drives the agriculture and the water supply and the energy supply for about a billion people, so seven of the world's population, no big uh, And and clearly variations in the seasonal norm of rainfall either too wet or too dry reek havoc on this region. So what if we could predict those variations before they happen, disaster could hypothetically be if not prevented, then then perhaps mitigated. Uh Okay.

And so besides being a project that could hypothetically change the lives of a billion people, Bobble is really cool because it's kind of a microcosm of weather prediction research in general, because it's it's so multi disciplinary. You've got ships and satellites making classic observations in the bay. You've got robotic submarines that are checking out the situation under

the surface. You've got researchers designing did little simulations to crunch the data, and they'll compare their models to the actual season's results to see where they went right and where they need to make improvements. Right. So we're going to talk more about Bubble in detail a little bit later, But first, as per our usual m O, we like to go back and look at how we got to where we are now. Like, like, obviously, when you look back to the ways humans tried to forecast the weather

centuries ago, they're supercomputers were sorely underperforming. Yeah, so those advocacies didn't process it quite the same thing exactly. You have all of your little scribes working in parallel, attempting in vain to simulate weather well, and the thing they were trying to calculate was how angry the god was. So there were several steps along they were going, going

off the path in a few different ways. So let's let's talk about let's talk about you know, kind of the a shoudn't approach to forecasting weather and work our way up to what we tend to do today. Okay, well, joking aside, there were of course lots of just straight up magical thoughts about how to control the weather or predict the weather originally, and so that's you know, that goes that's a tradition that goes way way back into

the ancient world. Has to do a lot with that with astrology, um and as kind of an offshoot of astronomy, but mostly it was astrological yeah, um, But those those sort of magical predictive interpretations. Aside, there were actually throughout history plenty of weather superstitions and sort of rules of

thumb that actually do have grains of truth to them. Yeah, there's a really great article on how stuff works Dot com about this, and I did a what the Stuff video about it once and and it's it's interesting, how many of them really do hold water? Yeah? Yeah, Well it makes sense because you figure people are paying attention to what has happened, and they realize that there's a pattern where when once a circumstances happened, then typically you

might get a lot of rain. And so you start to make a rule about that, and you know it's in some cases it can be you can be completely off base. It's just coincidence, or you do what I like to call you. It's it's called, you know, a confirmation bias, but I would call it. I would say the van is always at the corner, which is where whenever there's a van parked at the corner, you notice it. Whenever there's not a van parked at the corner, you don't register it. So to you, the van is always

at the corner. Uh. In those cases, obviously it may be that you've made an observation, but it's a faulty one. However, there's some that are at least somewhat you know, reliable. Yeah, here's one. You've probably heard some version of this weather prediction before and often in couplet form about red sky at morning, sailor take warning, red sky at night, sailor's delight.

I feel this. I feel badly for that one sailor right like it's just like like, oh man is going to be awful because it says sailor was singular, so it's one guy. Well, that's the way I always heard that. There are other versions. But this is old, old, old, It goes way back. People have been using this forecasting

rule for at least a couple of thousand years. We know because it shows up in the Bible, shows up in the Gospel of Matthew chapter sixteen, where uh it says quote the in RSV, the Pharisees and sad Juicees came and to test Jesus. They asked him to show them a sign from heaven, and he answered them, when it is evening, you say it will be fair weather, for the sky is red, and in the morning it will be stormy today for the sky is red and threatening.

You know how to interpret the appearance of the sky, but you cannot interpret the signs of the times. So obviously that they're trying to make a spiritual or religious point there, but but just incidentally in the narrative, at least we know that some people back then we're saying this rule. Um so, so the author of this passage had heard of this before, and crazily enough, it is partially true. So what's the scientific basis for this? Why would the color of the sky at sunset or sunrise

have anything to do with the weather? Well, strongly tinted red light at sunrise and sunset actually tells you something about the contents of the atmosphere between you and the sun. So specifically, it tends to indicate dry air filled with dust and solid particles which we would call aerosols. So these particles in the air are the cause of the reddening of the light because dust and aerosols in the atmosphere scatter visible light in a way that makes the

light turn red. Uh And in turn, this dry, dusty air tends to indicate that you're in a high pressure region, which means less cloud formation and less likelihood of a storm. A low pressure region, on the other hand, would mean that that they're tended to be more cloud formation and more storms. So if you are looking through red tinted atmosphere to see the sun you're looking through a high pressure region that's less likely to rain on you. Uh And and the thing about the atmosphere is that it

travels in in the same direction. Well yeah, and that's what this rule doesn't work everywhere, because while the Sun's path is unidirectional around the Earth, of course it's actually the Earth's rotation, but metaphorically, the Sun's path is unidirectional. I'm gonna need to see a site for that. The weather tends to travel in different directions depending on where

you live. So if you're in the Arctic or the Antarctic, or in the tropics, the sort of three extreme bands, weather patterns more often move east to west, and this rule doesn't apply, or in fact, actually I guess the opposite would apply, right, But for the mid latitudes, you know, sort of the temperate zones between the tropics and the Arctic or the Antarctic, this is actually more often true because the weather patterns more often moved from west to east.

And what that means is, if you look towards the sunset, you're looking west at the weather that's probably coming your way. And if a red light scattering patch is to the west of you, that's a high pressure area. Probably that's probably headed your way, meaning the weather will probably be fine. Uh. And of course why would red sky at morning be a problem. Well, that's because if you're in the mid latitudes again looking east toward a sunrise, you're seeing the

weather that has probably already passed by you. And high and low pressure systems often do trade off in cycles. But you may have noticed that I kept saying the word probably over and over again there, And that's because like all weather prediction, this is probabilistic. Using the system, you can predict the weather better than random guessing, meaning better than with fifty percent accuracy, but still not anywhere

near ad accuracy. Right, So there could be some mornings where you see a red sky and every it's perfectly fine, it's beautiful weather. And there might be some evenings where you see red sky and the next morning you're soaking in it. Yeah, So the weather, the weather is just

very complex. It's it's difficult to it's difficult to protict with accuracy even now using the supercomputers and everything that we have involved in all the data we have, but this one piece of folk science and weather forecasting, it's not the only one that turns out to have some basis in truth. Right. Yeah, a few others that I wanted to touch on because they're they're kind of a favorite. Uh. Ring around the moon rain real soon. Have you guys

ever heard this? This is the thing that you've heard. No, no, not at all, but I believe you. Yeah. Uh, there's there's another kind of version of it that goes when a halo rings the moon or sun rains approach and on the run. I love that sounds like something from one of their songs, and and and the thing that's going on here it is it does hold true more than fifty percent at the time. I think it's it's a similar probabilistic concept to to the red red sky

at night Sailor's Delight sort of thing. But so, so what's going on here is that, um, when you've got a halo that frames the moon or the sun, it's produced by by moonlight or sunlight refracting through high whispy clouds that are made of ice crystals, and uh and

those those ice crystals. That type of weather pattern typically occurs in siro stratus clouds that often move in ahead of weather fronts, where where temperature differentials are going to cause warm air to move upward, deensing moisture and potentially forming rain clouds potentially, So science science thumbs up on

that one. And still not the only Moon related weather, you know, kind of folklore, right, sure, Sure there's also clear moon frost soon, Yeah, which which makes perfect sense because because clear nights do often mean that cold weather is on the way, Because as far as the planet is concerned, a cloudless sky is sort of like having a bed without blankets. Uh. You know, During the day, the Earth absorbs sunlight and and can converts it into into heat that we all appreciate to certain degrees um.

When when the sun sets, the surface begins radiating that heat back out, and lacking clouds to capture the heat and snuggle it in all all tight and close, the surface and the lower atmosphere grow increasingly cold. In fact, I think in a tech stuff episode I talked about this as a means of creating ice in certain regions, where you'd leave out a pan a shallow pan of water outside because the heat radiates out and it actually becomes ice that way in certain regions of the world.

That's how it was done before refrigeration reached those areas, so it's kind of neat. Yeah. My favorite one though, has to do with cows. Of course, there is there's folklore about uh or not, like a folk saying, but yeah, that cows will lie down when it's about to rain, mm hmm. And and I will, I will admit that cows lie down for probably many reasons, like they're tired. But um, but but this one, but this one might

be due to to body heat. Okay, cows tend to stand more often when they're overheating, you know, in order to breathe everything out right. Sure, yeah, so so as seated cow could arguably I mean that the weather is cooling down and therefore a storm is a bruin. I also like in the notes you have, this one may have a leg to stand on. There. There are so many puns in this in this house stuff works article. And I yeah, I didn't write it, no, oddly enough, Yeah it was not it was not, I but but there.

But there are definitely some some more systematic approaches that people have come up with over the years, sure, apart from just sayings in folk wisdom. One big one through in history is Aristotle's Meteorologica. That's Aristotle's hugely influential treatise on winds, water, weather, and some other stuff like earthquakes. Like much of Aristotle, it is both startling lye intelligent

and hilariously wrong about lots of things. I enjoyed the section on how earthquakes are caused by evaporation of rains that have soaked into the earth and exhalations of breath from the ground. But until a few hundred years ago, I think the Aristotle's works were sort of the Western world's gold standard for knowledge about the causes of weather. And it wasn't until you know, fairly recent times that

we started being able to do much better. Yeah. I mean generally speaking, you started getting into like the mid to late Renaissance, and you start seeing some other thinkers propose alternatives to some Aristotle's ideas. But yeah, his his approach or his his observations and his his uh writings

held sway for centuries. Yeah yeah, um, And and some of those new ideas came about alongside changes in concepts about physics and also about astronomy, like like greater knowledge of astronomy um up to and including the publication of almanacs, which were very very popular publications back in the day. Apparently the only thing that outsold almanacs in the seventeenth century in England was the Bible, so lots of people

were purchasing these things. Um. And back in the late seventeen hundreds and early eighteen hundreds, a couple different mathematicians slash astronomers started publishing yearly farmers almanacs here in the in the States and what would be the United States later on the North America continent. Yes. Um. The formulae, the formulas that they use in order to make these predictions are to this day guarded as family or company secrets. It turns out like it it could be something like

consulting the family cat. We don't know, Yeah, and like intensely guarded. I love I I love stories about old farmers are almanac and UH and the Farmers Almanac, both of which are punctuated slightly differently in terms of the possessive s, but just the lower around all of this is is delightful. In the case of one of the two almanacs, I forget which one. UH, there is a Caleb Weatherbe who's sort of like the James Bond of

of this of this company. Because Caleb Weatherby is not his real name, I'm not sure if it's a dude. Uh I there have been this series of Caleb Weatherby's who have been the one entrusted with the knowledge of how the of how the almanac does it stuff. It's like cecil atoms, yes, of straight of the straight dope. Yeah, there have been many cecil atoms. Yeah so, but so

no one. No one knows exactly how they make their predictions, but supposedly take stuff like planetary positions and sun spots and lunar cycles and title patterns all into account, and I get the distinct idea reading stories about this that meteorologists find find almanax like this rather quaint. Uh what One researcher who looked into the accuracy of these kind of things found that they get their long ranging predictions because they make predictions a year or two out correct

about of the time. Is that a high number or alone? Like how much variability is there and what they could be predicting? You can't because you wouldn't say, like is that better than chance? Because it's hard to say without knowing all the variables. Oh, sure, I'm not sure. They claim to get it right about eight percent of the time, and and that is that is sore a gap. Yes,

but luckily we didn't. We we haven't had to continue relying just on stuff like this forever because eventually, UH physics, Yeah, people started figuring out how hydro dynamics therm thermodynamics both work, and once humanity got a really good grip on these concepts.

Strangely enough, around the same time that the American farmers almanacs started publication, the science of meteorology could take off, and by the early nineteen hundreds, a Norwegian physicist by the name Wilhelm Erknus devised the first known seven equation formula for for using observations of existing weather conditions to solve for future conditions. Taking taking into consideration like like pressure and temperature and humidity and then three aspects of

atmospheric motion. That forms the foundation. Definitely, I mean, the more information we have, obviously, the better picture picture we have what's going on right now, and the more um the more accurate we can make a forecast for the future. Of course, the further out you go from the current UH scenario, the current the current condition. Small differences in

in what you've predicted versus what actually happened add up tremendous. Yes, yeah, well, I mean it's a it's a sort of principle of physics that you can extrapolate on a very simple scale or on a very huge scale. On the simple scale, imagine aiming an arrow at a target. If you shift your aim a millimeter over and the targets a foot of way a foot away, it's not gonna make much

of difference. If the targets a hundred feet away, it will make a difference, right, So, same sort of idea is that you know the the temporal distance as opposed

to physical distance, it does make a big difference. But of course, once you get into the modern history of our technological and scientific capabilities for predicting whether one big difference, of course is just going to be the scale of of observation, increasing the number and accuracy of observational platforms to collect data about the weather, so we have more

information to work with, uh, And that's pretty easy. But another thing is that we can sometimes overlook the simple ways that common technological innovations help us in specific ways, And one would be communication technology such as the telegraph originally and then like the telephone facts and uh and the Internet, and these have allowed people to better understand global weather patterns in real time by rapidly sharing and

comparing information about local weather. Yeah. Computer science also allowed prediction to to greatly advanced, starting in the fifties and sixties and really ramping up over the past say like twenty to thirty years, along with the rate of our processing power. So I mean, perhaps obviously, as our computational ability and our observational ability have increased, so has our

forecast accuracy. There was an analysis that was published in Nature in and according to that, the forecast accuracy for the next three to ten days of weather has improved by about a day per decade um, meaning that right now our ten day forecasts are as accurate as nine day forecasts were in the early odts. So, in other words, every decade we go by, we're getting one day better. Yeah.

I like it. So if I can figure out whether or not I need to carry an umbrella with me on Friday when it's Monday, and and be reasonably certain that that is in fact the right answer, the better because I'm not carrying it. If I don't have to write. In a decade from now, you'll you'll be able to know pretty well on Tuesday. I'm looking forward to that.

So my suggestion, Jonathan, is that you need to get a cooler umbrella that you feel better about carrying all the time, Like maybe like a penguin's umbrella, you know that shoots machine machine gun fire or has a big sword that comes out the end of it. I have a blade runner umbrella that's great glowing. Yeah, I've got one of those. Um So, who is really in charge

of gathering and crunching all this data? I mean, I'm assuming when I turn on the local news and I see the local weather corresponded on the news, that person hasn't personally been responsible for gathering and analyzing all that information. He has no, no, no, no. The guy I'm imagine very specific, that guy launched the satellite uh and has collected the data. He built all of the computers himself.

Uh No. Modernly, weather prediction is a joint public like governmental and private industry type of business because that the satellites, the computers, the software, and the the human compilation of all of this data that go into it is each each of those separately are huge expensive arms of the venture. So and and going into it, you know, like, of course you've got local news stations, which are private companies that are reporting on whether but it's also a public service.

It's it's not just about personal convenience. It's absolutely a very critical public service about getting information about big storms, danger, tornadoes, hurricane, stuff like that out to the public um And it's also partially a a tool for commerce. The more that companies can learn about what the weather is going to do, the better that they can adjust whatever it is that

they need to adjust depending on what's sure. Like if if you're part of the shipping company, whether you're shipping stuff across land or see you need to know these sort of things because that can have a real impact on everything from a delivery date to the safety of the people and the products that you're moving. Weather is important, I mean, it's important to have this as accurate a picture of what's going to happen. And of course the further out you can do that, the more beneficial it

is for everybody. So that kind of leads us over into the discussion of some of the current attempts to get an even deeper, more keen understanding of the factors that influence whether UM and that kind of brings us also to Bobble, to that project we were talking about off the coast of India. So Bobble is pretty cool in that it's it's relying upon multiple sources to gather information UM also that we can get a better understanding

of the monsoon season in India. So that includes satellite data, atmospheric measurements courtesy of an f A a M aircraft and I'll go into that in a second, and some floats that are carrying scientific equipment, as well as those underwater robots that Lauren mentioned that are incredibly cool. I was so interested to hear, mostly just about how they move through the water because it's a brilliant and simple

means of propulsion. But first of all, the project has a collaboration between India researchers and scientists from the UK, specifically the University of East Anglia and the University of Reading, and the research will take place during the two thousand sixteen monsoon season, which has technically started as we record this podcast. It's June and July. So the monsoon season is India's rainy season. India gets a lot of its

rain during the season. Of the rain that falls in India falls during the monsoon season, and there is a lot of Yeah, we're talking ten ms annually of rain. Ten ms, it's thirty three ft or so. In some places it's up to eleven ms. It depends on the

region of India. Um. So the project's goal is to gain a deeper understanding of the factors that influence this monsoon season and that way we can make better predictive models of what areas of India are going to get what amount of rain, and that will help subsistence farmers plan out there they're farming to make certain that they take the best advantage of that. It also will help in the case of figuring out this particular region might be very susceptible to flooding and we need to take

measures to protect the people who live there. Right, So there's there stands to be a really incredible benefit too. Like we said earlier, up to a billion people to to cracking this code, to figuring out better how it works and therefore how to predict it. Right. So first step of course is you gotta get the data right. You have to collect the data before you can do anything with it, and that's where all of that equipment I mentioned comes into play. So first we have the

f A a M aircraft. F a a M stands for a Facility for Airborne Atmospheric Measurements, So it's flying through the atmosphere gathering data on the atmosphere as it moves through. It's pretty uh interesting. You need to take a little look at the picture of of these things as a special refitted B a E Systems aircraft out of the UK and uh it's the result of a collaboration between the Natural Environmental Research Council and the Met Office in the United Kingdom. Now, the f a a

M has a collection of sophisticated instrumentation aboard it. They can those instruments can measure everything from radiative transfer so essentially the way heat is moving through the troposphere, the chemical composition of the atmosphere, humidity, tem sure turbulence, cloud physics and more that turbulence in the cloud physics that's

really important. Things like vertical sheer that has a huge impact on weather patterns and it's one of those things that we need to have a lot of data on in order to really understand what's happening, and the team will actually compare the data gathered by the aircraft to that from the other sources the floats, the weather satellites and underwater robots to get a complete picture of what's happening in the bay during the monsoon season. Uh So

some of that other equipment that the ARGO floats. Now, ARGO floats are deployed all around the world, not just off the coast of India. In fact, there are more than three thousand of them floating in the oceans, and they measure temperature, ocean velocity, so the actual velocity of the water, the salinity of the upper two thousand meters

of the ocean. Scientists primarily use ARGO to monitor climate change, so they're doing it to see how conditions are changing over time to get a better idea of what is the x will practical effect of climate change. The data data gathered by ARGO is publicly available within a few hours of its collection, so um, the scientists on this project are going to rely on obviously on the ones that are specifically off the coast of India. Then you've

got those underwater robots, they're called sea gliders. They look kind of like um, almost like a torpedo shape. Some sometimes they're referred to as like an robotic dolphin, which is odd because they don't really have like they're not jointed where you have a t now they've got they've got a pair of wings that can tilt. But they use changes in buoyancy and those wings to create forward

momentum so they can move through the water. And they have a battery inside of them that can actually shift around as ballast, and that will allow them to change their pitch and roll so they can dive down. They can they can move through the water. They do so very slowly compared to say a propeller, But unlike a propeller, it's in incredibly energy efficient. Yeah, it doesn't have to use a lot of energy to change. Uh, it's it's position because of the buoyancy and use of its own

battery is ballast. So therefore, if it's energy efficient, that means that it can travel quite a great distance, probably on a single charge, without having to go back to home base and h and be juice up again. Exactly, it can stay under water for a long time and can travel a great distance. Really essentially only has to surface if you do have to recharge it or for it to beam the data back. It's got a radio antenna at the tip of it that will poke out

the water beams that information and the team can gather it. Uh. It's really a neat looking device and there are videos online that you can watch of it in action. Um. They're they're a little expensive there, about a hundred fifty pounds sterling each. Uh. The University of East Anglia used to have six of them and then lost two of them.

One of them got run over by a boat. What a ship really well, because these things tend to stay fairly close to the surface in order to beam information back and one and they don't move very quickly and they're hard to see. They're not huge right there, about the size of a person, but if you're operating a large ship like a cargo vessel, you may not see it. And a cargo vessel collided with one and destroyed it. The second one was lost in Arctic ice. I believe so.

But there are actually seven of them in operation for the Bubble project. UM, so really interesting. They also can hold lots of different types of sensors, not just ones to measure the various factors in the ocean, but others as well, for for things like marine biology. Now, of course, in the case of bubble marine biology was not really one of the things they were necessarily concerned with. So that's not the that's not in the instrumentation um for

those particular seed gliders. Instead, they're looking at sensors that are going to measure stuff like the turbidity of the water, the temperature, salinity, and the oxygen content. Now you collect all this data with the floats, the robots, the satellites, the aircraft, and now you know everything. Now you gotta

do stuff with it. That's the problem is like like for one thing, like you know, just just that information alone is incredibly valuable, but without knowing how it all interacts with one another, which factors are more important, which ones are really impacting the monsoon season the most, which are causitive versus just correlative, Right, Like, there may be some things that change. Maybe they're changed because the monsoons are moving through, not because they change and then cause

the monsoon. Right, So you've got you've got to determine all this. You have to crunch all that information, and that's gonna be the next big challenges grabbing all that data and doing something useful with it so that then you can take that knowledge and communicate it to people, so that you can make actual, uh, real world actions based upon that data. And this is where we start to shift over to a very important tool in weather

forecasting and weather modeling and climate science supercomputers. Yeah, because if you haven't cotton on yet, the problem of weather is is a big data problem. Yes, it's a it's a huge data problem because we know lots of different variables affect weather. We know those variables change greatly over spans of time. Right, So you've got a lot of information and that information is constantly in flux. So how

do you process that in a reasonable way. Supercomputers have proven to be a really important element of this analysis. So part of understanding this is knowing what a super supercomputer really is. It's not just a really beefed up PC. Right, It's not a beefed up Mac. It's not a beefed up Mac either um also known as big Mac. Yeah, it's none of those things. Although I mean, Mr Hodgeman, if you're listening, you don't need to beef up. We

like you the way you are. So supercomputers tend to be organized in a way where you've got nodes, which is essentially either a CPU or a GPU um and those are organized into blades. Those blades are further organized into racks, which are cooled in some interesting way, usually water cooled. Because you get that many processors in a place together, they generate a lot of heat. Heat and electronics over the long term are not good friends with

one another. So the in effect is you've got a supercomputer that acts kind of like a multi core processor. So if you have a multi core processor, you might wonder, well, how does this make my computer faster? Well, it works really well for certain types of computational problems. Those will be problems that could be broken up into smaller bits. It works less well for problems where you have to

solve one problem before you can start on the next problem. Right, So if you were to have the first type where you have a problem, you can split up into little bit. So you can think of that as imagine you've got uh A, I like to use this analogy. You've got a math class, and in that math class is a math genius, and then you've got a bunch of decent

math students, but they're not of genius level. You've got a math problem that's that first type one that could be broken up into several smaller problems, and you give the math genius the full thing, and you give each of the math the good math students part of that problem. The group of good math students are more likely going to finish it before the math genius, even though the math genius has a grasp of mathematics that far outpaces

that the rest of the class. If the second type of problem, like you were talking Joe, then the math genius is more likely to finish it because you can't divide that problem up and and give each little piece to all the different math students. So the math students represent that multi core processor, right with a supercomputer. You've just got thousands of these processors, like more than eighty

thousand for some big supercomputers. Right, And so you take this problem, the problem being, here are all these variables in weather, and I want My solution is I want to create a weather simulation so that I can forecast what will happen in the future based upon the current situation. Now, so that's your first step. You create your model, then you look and see if your model is any good. One way you can do this actually is to feed in data from the past. So let's say that you

have collected a huge amount of information from two weeks ago. Well, you already know what happened after that because it's in the past. Sure, so you can you can feed all of the information from two weeks in the past into the computer and say, h if I modeled this a certain way, then do I get like, like, how close do I get to what actually occurred after that first week? Right? And if and if it turns out that it didn't

come very close, you start making adjustments. You start saying, all right, this one factor that I thought was really important turns out maybe it's not so important. And this other thing that I kind of overlooked turns out as much more instrumental than I had anticipated. And and this is a long process, but you you refine that simulation.

This is a cool way in which weather prediction, I think has the potential to be a constantly improving science because unlike some disciplines, uh, this is not a field in which testing the predictive power of your theory or in this case, your algorithm is difficult because compared to something like psychology, where the results of your experiment might often be very fuzzy and indeterminate, or like particle physics, where you might have to test the predictions of your

theory by building some giant experimental instrument that operates at the giga electron volt scale or something like that, the weather is not like that. We have tons of data on it, always new data coming in. We've got plenty already, and we have lots of good ways of measuring it already. Yeah, And the problem is really that we have a wealth. We have to we have we are we are befuddled by our wealth of information, right, Yeah, No, I just like I just like the like we have plenty of

it already. Like I was just thinking, like, well, not much weather today. I had a lot of weather yesterday, which uh oh there it's from the Mystery Science Theater episode of pod People Were the Best. One of the characters asks, uh, do you think the weather all hold in? One of the viewers comments, no, I think it's just gonna stop. That was Tom Servo who said that. I remember that. Yeah, No, that's a fantastic episode. So Tangent go watch that episode of MST three K. It's one

of the best ones they ever did. Back to back to the their forecasting. So, according to Science Daily, supercomputers spend about equal amount of time running their simulations to assimilating new real world data into the models. So, in other words, half the time you're simulating whether the other halftime you're adjusting that simulation so that it more accurately reflects the real world. And as we get a better understanding of the things that affect whether, we can refine

that um. A study in Japan ran a global atmosphere simulation and found that a weather event event in one part of the world can affect other weather events thousands of kilometers away. And so it starts to dawn on you that in order for you to accurately forecast a local weather system, you have to actually look well beyond the immediate region, because there are factors that will affect that local weather system that are happening really far away.

And it may be that it's it's something that's not i mean, gonna instantaneously affect your local weather, but it will have an impact. So maybe something that would have normally been a rainstorm, but that's it could potentially turn into something much more severe like tornadoes. So it was really interesting. And the study included ten thousand, two hundred forty simulations, and they divided the global model into twelve kilometer sectors, so like a grid of a hundred twelve kilometers.

Now that's also important because the smaller those squares are in the grid, the more data you're feeding into the simulation, and the more powerful the supercomputer has to be. Yeah, and of course we're always expanding our hardware and software capability. So in January, the n o a A announced a major upgrade and its Weather and Climate Operational supercomputer system. Uh, and this was interesting. The two computers they have are called Luna and Surge Urge not like the soda, like

a wave. Well, yeah, like the soda. Sorry. The Luna and Surge are based in Florida and Virginia and each one runs at two point eight nine pedal flops for a combined five point seven eight pedal flops of computing capacity. And that is up from the system's capacity of just seven seventy six terra flops. Nothing to sniff at, but significantly lower last year. Flops, by the way, stands for

floating floating point operations per second exactly. So in the press release, the you know, administrator Dr Catherine Sullivan said that this upgrade would help the organization deal with quote the tidal wave of data that new observing platforms will generate. Just once again, I think we've sort of said this before, but uh, indicating that the problem in weather prediction these days is not a data problem, but it's an analysis problem. It's the what we do with the data that's where

the bottleneck is. Right. So also from n o A A NOAH, the National Oceanic and Atmosphere Administration. In other words, uh, they're running fifteen hour forecasts using something called the high resolution Rapid Refresh model, also known as the H triple R in meteorological circles. So if you have a meteorologist in your family, just ask them how the H triple R is going her her or if you want to

put model in there, it's the HERB. Anyway, the model divides the map, the global map up into three kilometer sections. So you remember I was talking about the Japanese study that was a hunter and twelve kilometers, So this one's more precise. It's divided the the entire world into smaller sections, which increases the amount of data significantly that they have to handle in order to make this fifteen hour forecast.

That's also why it's only fifteen hours out, because to to extend the forecast further would require even greater processing challenges, which they're working to overcome and slowly push that number further and further out. Um. But it's really interesting that they are looking at the world in three kilometer sections. It blows my mind because you think how huge uh an amount of data that must be that they're dealing with consistently, and they're refreshing this hour by hour to

look another fifteen hours ahead. UM. So, in Europe weather satellites are actually more advanced than the ones that we're using here in the United States right now, but that will change. The US has plans to launch the Geo Stationary Operational Environmental Satellite are also known as GOES ER. Did they come in the form of a giant slore as as GOES are the destructor or? Um? Yeah, I'm having Ghostbusters flashbacks on that. But it's scheduled to launch

in the fall of this year. It will actually become the most advanced meteorological satellite in orbit for at least a short time, finally outpacing the ones that are are currently over Japan and Europe. Other other recent news involved IBM spent about two billion dollars acquiring basically everything in the Weather Company except for the Weather Channel itself. And uh, and so they're apparently gonna pitt Watson against all that data and just kind of see what they can do.

Interesting Watson takes it down? What Watson Watson will take all that data and make yet another bizarre and unimaginable recipe that involves pot stickers that don't have any of the ingredients in them that they claimed that is it going to rain next year? First? Grill you r let us? Oh man, I still think we have to each take one of those recipes, make it and bring it in. We never did do that. We should do a live show where we subject each other to cooking grill your

perade olives. I think we all we all will need to have a chef hats and and aprons with humorous sayings on them. Uh, that's that's what I suggest. All right, Well, well well we'll work on that at any rate. Let's look at again kind of further off, like what was the future going to bring. So once we have these more advanced satellites, we're constantly working on building better supercomputers, which often are used for this kind of thing, as

well as other branches of science as well. Uh So, for one thing, as we get this greater understanding of the global influences of whether we can we can improve our forecasting when we understand that an event happening thousands of miles away will have an impact on the weather in our area and we have a better way of of predicting what that impact will be. That's gonna benefit people in ways that we can't even really get a

grip on right now. Um. One of the other things we have to remember is that it's a lot easier to predict weather in general, that is severe weather. Um. So you'll see this on lots of different sites that are talking about meteorology. They'll say like, oh, you know, we can predict general weather systems out maybe as far

as a couple of weeks or further. But when you start getting into the the the possibility of severe weather, it's closer to like five days, and each day out is less accurate than the day before, which means that when you're looking at the tail end of that forecast,

you have to keep that in mind. Um. I tried to do that all the time when I'm thinking, like, oh, I'm going on vacation in two weeks, let me see what the weather is gonna be like in ten days, And and often I go in with a false sense of security, or I'm end up preparing for a rainstorm that had just doesn't happen. But as we get more information,

we get better at anticipating these things and predicting them accurately. Obviously, this could help lots in lots of ways, like in that commerce that we were talking about, or in travel.

Absolutely having better weather prediction could have all kinds of commercial and environmental bonuses, like imagine being able to reboot flights around bad weather systems before storms hit, thus preventing having to sit around at the airport all day, or or having to have your flight canceled, or even allowing

pilots to save on fuel by plotting better courses. Also, as as Julie brought up, in our prior weather episodes, changes in whether change our buying habits, supermarkets could plan to stock up on those frier chickens or whatever it is, way more in advance. Apparently, apparently during certain disasters, fried chicken just flies off the shelves unless which is weird because chicken rarely flies even when it's not fried. But

also there's the issue here in Atlanta. I made the joke in our notes that it's not really a joke. It's actually just a fact that if there's even the hint of snow, you can expect a run on supermarkets for all the milk, bread, sometimes bleach bleaches, big yeah, and then people get home toilet paper. What do you do with this? Yeah, I never buy this to begin with, exactly lots of French toast. That's what we're gonna be

having kids. So yeah, But they having those predicting those better forecast means that you know, you can actually prepare for that sort of stuff and uh and hopefully not encounter things like shortages or or or you know, where people go to a store and then they realize that

they're out of luck because everybody has rushed it. If you've got more time to prepare for that, then you can build up your inventory and make better profit and people can be happy that they can you know, get their bread and milk and eggs and make that French toast and then when it doesn't snow, everyone complains about it the bread and milk and eggs go bad, but you don't care. You sold them already. Yeah, yeah, capitalism. So, uh,

it was fun to kind of look into this. I always, I always really enjoy discussing, uh, the idea behind weather science. I'm not big on talking about the weather in general, but whether science to me, is really neat because you start to realize how incredibly complicated it is and how much energy are the the energy that are that happens to be in these big weather systems like you know, we we if you talk about hurricanes, the amount of

energy and a hurricane is phenomenal. Right, as Lauren has so succinctly put it before, there's more wind than truck fair enough. So to me, that's why I love talking about these things and why I felt that it was fun to to come back and revisit this. Plus I wasn't in the last couple, so I really wanted to kind of jump into it. But guys, if you have any suggestions for future episodes of our podcast, let us know,

send us an email. That address is FW Thinking at how Stuff Works dot com, or you can drop us a line on Twitter. The handle there is f W Thinking, or search f W Thinking and Facebook. Our profile should pop right up. You can leave us a message there and we look forward to hearing from you, and we'll talk to you again really soon. For more on this topic and the future of technology, visit forward Thinking dot Com problem brought to you by Toyota. Let's Go Places,