Speaker 1: Technology with tech Stuff from stuff works dot com. Welcome
Speaker 1: to tech Stuff. I am your host, Jonathan Strickland. I'm
Speaker 1: a senior writer with how stuff works dot com and
Speaker 1: I'm so glad you could join me today here on
Speaker 1: tech Stuff, we like to cover all things technological and
Speaker 1: explain how they work and why they're important. And for
Speaker 1: the last couple of episodes, we've really been focusing on
Speaker 1: meteorology and weather forecasting and the types of technology that
Speaker 1: we used to try and predict the weather. UH. In
Speaker 1: the first episode in this series, we concentrated mainly on
Speaker 1: the science of weather itself, and that's important to understand
Speaker 1: because you begin to pick up on how complicated a
Speaker 1: system whether actually is, especially when you start expanding out
Speaker 1: from a small region to a larger region to a
Speaker 1: global region and you see how interdependent all of these
Speaker 1: different regions are on each other, the brain starts to
Speaker 1: swim a bit. In Part two, we looked at the
Speaker 1: various sensors and tools used to capture information about what
Speaker 1: is going on with the weather. These are the tools
Speaker 1: that meteorologists use in order to feed that information into
Speaker 1: their various weather models. So UH, Today's episode is going
Speaker 1: to focus on those weather models. These are based on
Speaker 1: our understanding of the behavior of weather under different conditions,
Speaker 1: and it's what gives us the confidence to make predictions
Speaker 1: of what will happen next. Now that being said, predictions,
Speaker 1: as we all know, are not guarantees. I'm sure there
Speaker 1: are many of you who have walked out of your
Speaker 1: homes with confidence, dressed in your best clothing, only to
Speaker 1: be plagued by an unexpected downpour at an inopportune time,
Speaker 1: turning into Charlie Brown with that one rain cloud just
Speaker 1: directly overhead. Or you might be one of those people
Speaker 1: that have convinced him or herself that if you have
Speaker 1: an umbrella in your hand, it virtually guarantees that not
Speaker 1: a single drop of rain will fall, that you, by
Speaker 1: virtue of holding the umbrella, have prevented rain from happening.
Speaker 1: As it turns out, predicting the weather is really hard
Speaker 1: for a lot of reasons, though the biggest one is
Speaker 1: that weather is just an incredibly complicated system affected by
Speaker 1: hundreds of variables, and those variables may have a lesser
Speaker 1: or greater effect in different situations. That means there's a
Speaker 1: lot of potential outcomes for any given scenario, and until
Speaker 1: we have a really comprehensive understanding of what's going on
Speaker 1: at all times in our atmosphere. Weather predictions will be
Speaker 1: based primarily on statistical probabilities, not certainties. Before we really
Speaker 1: had a handle on all of those variables, and to
Speaker 1: be honest, we don't completely have a full handle on
Speaker 1: them right now. We based weather predictions off of empirical
Speaker 1: rules that we formed observation. So, in other words, generally speaking,
Speaker 1: if you woke up and looked outside the window and
Speaker 1: you thought, hey, it looks like it might rain today,
Speaker 1: because a couple of weeks ago I looked out the
Speaker 1: window and it looked just like this and it rained
Speaker 1: that day, well that's about as complex as it got.
Speaker 1: I mean, you might have a weather map, like a
Speaker 1: literal map, and you have some things you've written down
Speaker 1: on it. You know that to the west of you
Speaker 1: there's a low pressure system, so you might start using
Speaker 1: that as a guide for what could end up happening.
Speaker 1: But it wasn't a very precise science. It wasn't what
Speaker 1: people were calling a rational approach. It was an empirical approach,
Speaker 1: and over time we began to understand that weather is
Speaker 1: dictated by a host of very complex variables. So meteorologists
Speaker 1: are gathering all of this data about air pressure, temperature, windspeed,
Speaker 1: weather patterns that are nearby, and tons of other factors.
Speaker 1: These things are changing quickly and constantly, meaning it's important
Speaker 1: to look over those observations regularly and adjust projections. And
Speaker 1: if you're not lucky, you may only have a few
Speaker 1: observation stations placed in strategic locations within your particular region,
Speaker 1: which gives you a limited on resolution. So weather forecasting
Speaker 1: is a lot like your displays or your televisions resolutions.
Speaker 1: Very important resolution is all about how many points of
Speaker 1: data do you have within that given area, how representative
Speaker 1: are those observation stations. If you have a single observation
Speaker 1: station for several square miles, well that's not going to
Speaker 1: give you very good resolution, right You're going to have
Speaker 1: a very specific idea of what's going on at one
Speaker 1: point within that area, but everything further out from there
Speaker 1: it's going to be some variation of the information you've
Speaker 1: pulled down. So if you want high resolution, you have
Speaker 1: to have lots of observation stations throughout that same area,
Speaker 1: and you collectively are able to determine what's happening by
Speaker 1: looking at all of them, but obviously that adds a
Speaker 1: lot more information to your calculations. In an ideal world,
Speaker 1: you have all areas densely packed with observation stations, giving
Speaker 1: you amazing consistent resolution and the processing power necessary to
Speaker 1: take all that raw data and crunch it to produce
Speaker 1: reliable weather forecasts at any given moment. But we just
Speaker 1: aren't there yet. So weather models, what's the story on those? Well,
Speaker 1: it helps to look back on the birth of meteorology
Speaker 1: and weather forecasting in the form of numerical weather prediction.
Speaker 1: That's really what we get down to when we start
Speaker 1: talking about weather models. And to do this we actually
Speaker 1: have to backtrack a little bit. I know, we talked
Speaker 1: a lot about the various tools in the last episode
Speaker 1: and we got pretty up to date, but we're gonna
Speaker 1: have to go back to talk about weather models specifically. Now.
Speaker 1: In the nineteenth century, so this is the eighteen hundreds,
Speaker 1: physicists were beginning to suss out the law of thermodynamics.
Speaker 1: These are the basic laws of energy that we UH
Speaker 1: that everything is is UH has to obey, at least
Speaker 1: everything on the macro scale has to obey. And it
Speaker 1: was relatively easy to put hypotheses to test with modest experiments, right, Like,
Speaker 1: you could do tests about fluid dynamics with small contained
Speaker 1: systems and you could limit the variables and make lots
Speaker 1: of observations. That was pretty easy, relatively speaking, But it
Speaker 1: was much more challenging to step back, and I mean
Speaker 1: way way back and see how those same laws applied
Speaker 1: to something as massive as our atmosphere. If you want
Speaker 1: to think about another way, it's one thing to look
Speaker 1: at a maze that has rats running in it. It's
Speaker 1: another thing to try and figure out what the maze
Speaker 1: is like. When you are inside the maze and you
Speaker 1: can only see a small part of it, How do
Speaker 1: you know what the entire layout of the mazes from
Speaker 1: that perspective? So, in other words, the perspective of the
Speaker 1: rats we are inside the actual environment that we want
Speaker 1: to describe. That makes it way more difficult for us
Speaker 1: to uh isolate and weigh every single variable in the system. Well,
Speaker 1: in nineteen o one, there was a professor named Cleveland Abbey.
Speaker 1: He published a piece in a journal called Monthly Weather Review.
Speaker 1: Now I was really sad to discover that this wasn't
Speaker 1: actually a list of reviews for actual weather like Weather
Speaker 1: Today was pretty good. I give it three stars. No
Speaker 1: it It actually was a scholarly journal on the subject
Speaker 1: of meteorology, and Abbey's piece had the title The Physical
Speaker 1: Basis of Long Range Weather Forecasts. And in that piece,
Speaker 1: Abbey pointed out that forecasts of the time were based
Speaker 1: on experience rather than any real knowledge of how weather works,
Speaker 1: and that the physical theories explained the development of whether
Speaker 1: we're either superficial or non existent. So it goes back
Speaker 1: to that example I gave earlier. Weather forecasting was based
Speaker 1: on people's experience with weather, but not knowing how the
Speaker 1: weather was actually working. So you might say, well, I
Speaker 1: think that it may snow tomorrow based upon what the
Speaker 1: conditions are right now, and the fact that I remember
Speaker 1: a day that was like this where it snowed the
Speaker 1: next day. But that's not a very scientific approach ultimately speaking,
Speaker 1: and it doesn't have It is not based upon understanding
Speaker 1: the factors that lead to things like a snowstorm, and
Speaker 1: so Abby was arguing that in order to have real
Speaker 1: weather forecasting prowess, we would have to gain that understanding
Speaker 1: of the underlying factors of weather. Abby asserted that we
Speaker 1: just had to understand the laws of mechanics and heat
Speaker 1: of the atmosphere. Only then, he posited, could we use
Speaker 1: the information to start making more accurate forecasts. And his
Speaker 1: peace would go on to outline what he saw as
Speaker 1: the necessary steps to get there, including a thorough investigation
Speaker 1: of the behaviors of the atmosphere, and he said that
Speaker 1: the science of meteorology is quote essentially the application of
Speaker 1: hydrodynamics and thermodynamics in the atmosphere end quote, So he
Speaker 1: was calling for the establishment of a new area of science,
Speaker 1: specifically within meteorology, something that that would require people to
Speaker 1: dedicate a lot of time to try and understand this
Speaker 1: complex system that is our atmosphere. Then you have a
Speaker 1: Norwegian scientist, Vilhelm Biekness. He was born in Christiania, Norway,
Speaker 1: in eighteen sixty two, and as a young man he
Speaker 1: worked with a notable physicist, Heinrich Hurts Hurts I mentioned
Speaker 1: in our episodes on the history of electricity, you know
Speaker 1: the Hurts. He went on to he being Bakness went
Speaker 1: on to teach applied mechanic and mathematical physics at the
Speaker 1: University of Stockholm, where he sussed out some theorems that
Speaker 1: helped him create a synthesis of hydrodynamics and thermodynamics for
Speaker 1: large scale atmospheric motions, the very thing that Abby was
Speaker 1: calling for. Bierk Nous was the one to to develop
Speaker 1: a very comprehensive model, the first really comprehensive model for that,
Speaker 1: and this led to the development of air mass theory,
Speaker 1: one of the principal ideas upon which we base weather forecasting.
Speaker 1: Now in n Berk Now published a work titled on
Speaker 1: the Dynamics of the Circular Vortex with Applications to the
Speaker 1: atmosphere and to atmospheric vortex and wave motion. And it's
Speaker 1: a real page turner, guys. This is a pretty dense
Speaker 1: piece of of scholarly work. It's considered one of the
Speaker 1: most important scholarly works in the field of meteorology, and
Speaker 1: it was the basis of our understanding of general weather
Speaker 1: pattern behaviors and why they take on the forms the
Speaker 1: way they do. In other words, it was pretty much
Speaker 1: what Professor Abbey was saying was necessary before we took
Speaker 1: a rationally scientific approach to forecasting the weather. Now, what
Speaker 1: Wilhelm illustrated was that the atmosphere and thus weather does
Speaker 1: can be described in math through fluid dynamics factors like
Speaker 1: temperature impact those behaviors, so you have to take that
Speaker 1: into account. And as things change, there's a sort of
Speaker 1: ripple effect. If you change one part of the the system,
Speaker 1: that ripples out and affects the rest of the system
Speaker 1: in different ways depending upon other factors. And nothing in
Speaker 1: the atmosphere is remaining completely unchanged, and as each element
Speaker 1: shifts or cools down, or heats up, or the density
Speaker 1: changes or whatever it may be, it affects other parts.
Speaker 1: So the math gets pretty challenging pretty fast. He further
Speaker 1: went on to create a two step process for rational
Speaker 1: forecast nesting of whether now. The first step was diagnostic,
Speaker 1: which is, in other words, using observations to determine what
Speaker 1: is the present state of the atmosphere. This is where
Speaker 1: you take all those readings and you say what is
Speaker 1: going on right now? That's the diagnostic step, and it's
Speaker 1: absolutely necessary before you can do anything else. You can't
Speaker 1: say what's going to happen next until you have an
Speaker 1: understanding of what is happening right now. The second step
Speaker 1: was prognostic, which meant that you would use that information
Speaker 1: from the diagnostic step and project outwards and say, all right, well,
Speaker 1: based upon what we know is happening right now, what
Speaker 1: is going to happen twelve hours from now, or a
Speaker 1: day from now or two days from now. And you
Speaker 1: would have to use the information you had gathered in
Speaker 1: the diagnostic step, combined with our knowledge of the laws
Speaker 1: of motion for atmospheric masses, to predict what would happen next. Now,
Speaker 1: if you could strip everything away and just look at
Speaker 1: the math, you'd be looking at a collection of what
Speaker 1: are called partial differential equations. The mathematicians out there know
Speaker 1: exactly what I'm talking about. These are equations that deal
Speaker 1: with rates of change with respect to continuous variables. So
Speaker 1: you're not just talking about variables like temperature, pressure, and velocity.
Speaker 1: You are talking about those, but you're also talking about
Speaker 1: the rate of change of those variables. How quickly is
Speaker 1: the temperature changing, how quickly is the pressure changing, etcetera. Now,
Speaker 1: the way you frame and solve these equations defines your
Speaker 1: weather model. Different weather models place different emphasis on these
Speaker 1: variables and the rates of change. All of this boils
Speaker 1: down to a computer program ultimately that solves these equations
Speaker 1: as you have directed. So one model might approximate different
Speaker 1: equations one way and another model does so in a
Speaker 1: different way, and thus you're going to get two different forecasts.
Speaker 1: Consulting these two different models, they might resemble one another,
Speaker 1: but they're taking different pathways to get to their destination,
Speaker 1: so sometimes they might be very different from one another.
Speaker 1: And it's all because of the way you have told
Speaker 1: the program to prioritize the various processes, which ones you know,
Speaker 1: which factors have the most weight and under what circumstances. Now,
Speaker 1: this doesn't necessarily mean one model is by its nature
Speaker 1: superior to the other. Some models are for specific regions
Speaker 1: in the world, and those regions, due to geography and
Speaker 1: general atmospheric motions, may require more importance to be placed
Speaker 1: on certain sets of variables rather than others. Now Berkness
Speaker 1: identified seven variables he saw as critical for accurate weather forecasting.
Speaker 1: That includes pressure, temperature, density, humidity, and then three different
Speaker 1: components of velocity. He also identified seven equations, three therma
Speaker 1: hydro dynamic equations I should say three hydro dynamic equation, emotion,
Speaker 1: the continuity equation, the equation of state, and equations expressing
Speaker 1: the first two laws of thermo dynamics. Now, keep in
Speaker 1: mind that the atmosphere is three dimensional. You have to
Speaker 1: essentially consider any region within that area a three dimensional grid.
Speaker 1: So your grid has an x, y, and z axis,
Speaker 1: and the events within one part of that grid can
Speaker 1: affect other parts of it, particularly atmospheric motion. And you
Speaker 1: need to figure out how to take partial derivatives which
Speaker 1: computers can't really handle, and then turn them into approximate
Speaker 1: partial derivatives that computers can handle instead. This approximation adds
Speaker 1: in a bit of imprecision by its very nature. But
Speaker 1: then's the brakes. And speaking of brakes, let's take a
Speaker 1: quick one right now to thank our sponsor. So getting
Speaker 1: back into forming weather models. There's a meteorologist named Lewis
Speaker 1: Fry Richardson who was influenced by Berkness, and he did
Speaker 1: his best to tackle the problem of numerical weather forecasting,
Speaker 1: but he stated that the sheer amount of computation was
Speaker 1: impractical for the time. This would be in the early
Speaker 1: twentieth century, first couple of decades of the nineteen hundreds.
Speaker 1: He did say, quote, perhaps someday in the dim future,
Speaker 1: it will be possible to advance the computations faster than
Speaker 1: the weather advances. But that is a dream end quote.
Speaker 1: So he's saying, by the time I'm able to work
Speaker 1: out the math, whatever the weather was gonna be has
Speaker 1: already happened. I'm predicting what happened hours ago. Uh. And
Speaker 1: that was a real problem. Was just that again, you
Speaker 1: had these very complex equations with lots of points of
Speaker 1: data and lots of variables that you had to solve for,
Speaker 1: and by the time you would be done with all
Speaker 1: the calculations, the time had passed. So he was saying,
Speaker 1: there is a need for some sort of engine that
Speaker 1: can do computations faster than what humans can do. So
Speaker 1: the math was just too complex to complete without the
Speaker 1: use of that computational engine. One person determined to help
Speaker 1: design such an engine was John von Neumann, who was
Speaker 1: a mathematician who made numerous contributions to the sciences, and
Speaker 1: he realized that some of the more advanced problems in
Speaker 1: hydrodynamics and weather forecasting would benefit from a powerful automatic
Speaker 1: computational machine. He worked on a project at Princeton at
Speaker 1: the Institute for Advanced Studies and it would become known
Speaker 1: as the Electronic Computer Project. Meanwhile, over at the University
Speaker 1: of Pennsylvania's More School of Electrical Engineering, you had J. G.
Speaker 1: Brainerd who was heading up a project, and J. Presspur
Speaker 1: Eckert and John W. Malchley who were working on what
Speaker 1: was called the Electronic Numerical Integrator and Computer or NIAC
Speaker 1: for short, and computer nerds out there, I consider myself
Speaker 1: one of them will kind of bristle at the sound
Speaker 1: of ENNIAC. They might prick up their ears and say, oh,
Speaker 1: I know, I've heard of ENNIAC. ENIAC being one of
Speaker 1: those early early computers in the dawn of the computing age.
Speaker 1: Well brain Nerd, the man who was heading this project,
Speaker 1: invited von Neumann over to the University of Pennsylvania to
Speaker 1: check out ENIAC, and von Neuman would end up having
Speaker 1: these very deep discussions with the team, and those discussions
Speaker 1: would help inform the design of the successor to ENIAC,
Speaker 1: and these early computers would become some of the first
Speaker 1: capable of tackling those difficult computational problems. I was mentioning
Speaker 1: a second ago, and that brings us to the concept
Speaker 1: of the weather model. Now, your weather model is an
Speaker 1: advanced computer program that runs all of these sorts of
Speaker 1: equations and then calculates outcomes. So, in other words, it
Speaker 1: generates your weather forecast based upon those points of data.
Speaker 1: Many of these weather models have been written in four
Speaker 1: chan for Tran, I should say, largely because that's how
Speaker 1: it's been done for decades. So if you've ever chatted
Speaker 1: with somebody and you're saying, why are we doing it
Speaker 1: this way, and they say, it's because it's how we've
Speaker 1: always done it, that's sort of the case with Fortran
Speaker 1: in weather models. Uh, that's one of the reasons. But
Speaker 1: it's also a very useful language that has evolved over time.
Speaker 1: It's not like it was developed and then forgotten about.
Speaker 1: It has received a lot of I hesitate to use
Speaker 1: the word love, but development over the years. Now, this
Speaker 1: four trend program is compiled into machine language. That's the
Speaker 1: language that computers understand, and we'll talk more about that
Speaker 1: in the Programming Languages episode that will be coming up soon.
Speaker 1: So keeping the year out for those episodes, they should
Speaker 1: be following this one shortly. The program, the weather model
Speaker 1: takes all this information, the data fed to it from
Speaker 1: multiple sources, all those observation stations, and all those equations
Speaker 1: based off of fluid dynamics and thermodynamics, and steps through
Speaker 1: in time to simulate what will happen next. So the
Speaker 1: computer is actively trying to simulate the behavior of weather
Speaker 1: patterns based upon our understanding of hydrodynamics, thermodynamics, and all
Speaker 1: of these variables. So it's it's actively simulating the outcomes.
Speaker 1: Now you're getting these simulations in the form of numeric answers.
Speaker 1: It's not like you're looking at a graphic representation of weather.
Speaker 1: You're not, you know, looking at your computer and you
Speaker 1: see a massive storm is roiling across the screen. I'm
Speaker 1: pretty sure that's how Hollywood would do it. But we're
Speaker 1: talking more about lots of numbers, so not as sexy
Speaker 1: as say, watching Twister on Netflix and you're saying, that's
Speaker 1: what the way, what's the what the weather is going
Speaker 1: to be? Uh, that's not exactly the case. Sadly, Maybe
Speaker 1: one day we'll get there, but not not right now now.
Speaker 1: Some simulations can project out for multiple days, and some
Speaker 1: are more immediate. Some look at short range forecast, some
Speaker 1: do mid range and long range as well, and the
Speaker 1: highest amount of accuracy typically is within the next several hours.
Speaker 1: And then, of course the further out you go from
Speaker 1: the moment you gathered all that data and did your
Speaker 1: diagnostic stuff, the more you are likely to diverge from
Speaker 1: reality for your forecast, more uncertainty enters into the picture
Speaker 1: because it's hard to predict how all of those different
Speaker 1: variables are going to uh what what their state will
Speaker 1: be at any given point in the future. And the
Speaker 1: further out you go, the more uncertain you're going to be. Typically,
Speaker 1: So let's make up a hypothetical situation to kind of
Speaker 1: explain what I'm talking about here. Let's say I'm in
Speaker 1: the north of wester Ross and I know winter is coming.
Speaker 1: My weather forecast model is very much focused on atmospheric
Speaker 1: movements from beyond the wall and less concerned with other
Speaker 1: variables like maybe humidity or I don't know, dragons, because
Speaker 1: humidity and dragons don't play such a large role in
Speaker 1: the weather patterns of my region, right, I mean, I'm
Speaker 1: one of the Starks in this hypothesis. The model I
Speaker 1: have created is as accurate a representation of how patterns
Speaker 1: emerge and behave in my region as I can get
Speaker 1: my hands on. So that's what I rely upon. But
Speaker 1: let's say you in your fancy pants King's landing house
Speaker 1: are concerned with trade winds coming in from out over
Speaker 1: the sea, because that's a large influencer of the weather
Speaker 1: in your area. So your weather model takes that into
Speaker 1: account and gives it greater weight than some of the
Speaker 1: variables that I'm concerned with. And this is so that
Speaker 1: you can create accurate forecasts for your area. Your model
Speaker 1: and my model aren't equivalent. Your model would not work
Speaker 1: as well in my region, and vice versa. And neither
Speaker 1: model is comp inhensive, which means neither model covers all
Speaker 1: of wester Roast. That's very much regional. Now in the
Speaker 1: real world spoiler alert, Game of Thrones isn't real. We
Speaker 1: sometimes find that our computer models are mostly good, but
Speaker 1: not perfect. Some may, under certain conditions under or over
Speaker 1: estimate the temperature for example. Now, this could happen for
Speaker 1: lots of different reasons, such as, you might have a
Speaker 1: region that's close to the ocean, and the ocean could
Speaker 1: affect the temperature in ways that the model is not
Speaker 1: quite capable of accounting for. So you might experience more
Speaker 1: windy conditions than other areas, and the wind may affect
Speaker 1: temperatures in ways that the model can anticipate. The wave
Speaker 1: dynamics could affect whether in ways that the model can't anticipate,
Speaker 1: so you still have meteorologists who are dealing with this,
Speaker 1: and they're fudging the numbers a bit once they've been processed,
Speaker 1: because you learn over time how well your model does
Speaker 1: versus reality. So you can look at the results of
Speaker 1: your model, what does the predictions say, and then you
Speaker 1: can compare that against the actual results that you get
Speaker 1: just by waiting around. Right, you wait around and you
Speaker 1: see what actually happens. You compare that to the forecast
Speaker 1: that your model gave, and you start to look and
Speaker 1: see if there's any any adjustment that needs to be made,
Speaker 1: or in some cases you may just say, well, this
Speaker 1: model is frequently about two degrees warmer than what really happens,
Speaker 1: so we're going to build in an adjustment. We will
Speaker 1: automatically no to decrease the temperature forecast by two degrees
Speaker 1: from this model, and that we are more likely to
Speaker 1: hit on what the actual temperature will be. This happens
Speaker 1: all the time with lots of computer models, not just
Speaker 1: for temperature, but for other variables as well, and really
Speaker 1: we should expect this to continue to happen because we
Speaker 1: cannot have a perfect understanding of how everything is going
Speaker 1: to be and builded into a computer, not yet, possibly
Speaker 1: not ever. It is so complex and so dependent upon
Speaker 1: so many different variables. But what we can do is
Speaker 1: we can correct for those known problems. If we know
Speaker 1: that there is an issue and it's not likely to
Speaker 1: cause a ripple effect, which I'll talk about a little
Speaker 1: bit later in this episode, then we can just correct
Speaker 1: for it at the end and say, all right, let's
Speaker 1: bump up or bump down the temperature by a couple
Speaker 1: of degrees based upon our knowledge of how this model
Speaker 1: performs against what really happens. It's kind of interesting because
Speaker 1: it really nails home how computers are all about precision
Speaker 1: and replication. They don't tend to give you vague guesses.
Speaker 1: They can create different answers that have different probably probabilities
Speaker 1: of being correct, and then choose whichever one is the
Speaker 1: most likely to be correct. But they are about making
Speaker 1: these precise uh outputs, and we as humans are the
Speaker 1: ones who have to add extra levels of interpretation on
Speaker 1: top of that, which means that there's still a human
Speaker 1: being associated with this process. It's kind of what you
Speaker 1: have to do with an old scale. If you had
Speaker 1: an old like weight scale and it was off of
Speaker 1: its calibration, you couldn't quite get it to reset at zero.
Speaker 1: So let's say you notice that your scales giving a
Speaker 1: reading that's always two pounds less than what it should be.
Speaker 1: You know that when you weigh something, you need to
Speaker 1: add two pounds to your scales reading whenever you weigh something.
Speaker 1: Meteorologists will often do the same thing, and sometimes they'll
Speaker 1: do it to just a very specific region within a
Speaker 1: computer models area of coverage. They know that the model
Speaker 1: has a history of under or overestimating things, and so
Speaker 1: they just correct for it. Now, for that reason, we
Speaker 1: still have human beings involved in meteorology and all these
Speaker 1: different phases. Meteorologists use their training and expertise to interpret
Speaker 1: the output from weather models, and they learned the quirks
Speaker 1: of the models, even as new versions of those models
Speaker 1: come out to correct for inaccuracies or to increase resolution
Speaker 1: or frequency. So remember whether forecasting depends upon the quality
Speaker 1: of the weather model, the accuracy of the information being
Speaker 1: fed into the model, and the frequency with which that
Speaker 1: information comes in, and the density of the observations within
Speaker 1: that region. All of these things will affect the accuracy
Speaker 1: of the ultimate weather forecast to come out of that
Speaker 1: computer model. If you have the best model in the world,
Speaker 1: it's still not going to give you a very accurate prediction.
Speaker 1: If either you don't have enough observation stations so your
Speaker 1: resolution is low, you aren't consulting your observation stations frequently enough,
Speaker 1: so you are relying on older information, or the information
Speaker 1: you're feeding into your model is somehow inaccurate. Let's say
Speaker 1: that you have some sensors that aren't working properly and
Speaker 1: are giving you, uh the wrong reading for some element here,
Speaker 1: whether it's air press or temperature, whatever it might be. Well,
Speaker 1: if that information gets fed into your computer model, then
Speaker 1: you would expect that the outcome is not going to
Speaker 1: be accurate because it wasn't accurate information going in, or,
Speaker 1: as some people say, garbage in garbage out. Your outcomes
Speaker 1: are only going to be as good as your data,
Speaker 1: as well as the fact that you have to worry
Speaker 1: about the the quality of your model itself. Now, if
Speaker 1: this sounds like it's a ton of processing, it is.
Speaker 1: Traditionally one of the big applications we have for supercomputers
Speaker 1: is for weather models. So whenever you hear about supercomputers
Speaker 1: and the massive amounts of processing power they have. Often
Speaker 1: these computers are being put towards the task of simulating weather,
Speaker 1: taking these weather models and trying to get more and
Speaker 1: more accurate simulations of what is going to happen. ANIAC
Speaker 1: itself was used to generate weather forecasts. But even though
Speaker 1: NIAC was a big jump forward on just working out
Speaker 1: the equations by hand, it was still limited and the
Speaker 1: weather model wasn't much more than a barotropic equation. A
Speaker 1: barotropic equation is a fluid dynamics problem in which density
Speaker 1: is a function of pressure only. Even this limited interpretation
Speaker 1: of the factors that affect weather was still a big
Speaker 1: leap forward. However, any act success led to the development
Speaker 1: of new models, including multi level models, and one such
Speaker 1: model was the product of several scientists work in the
Speaker 1: in the in the wake of a massive storm system
Speaker 1: that took place on Thanksgiving Day in nineteen fifty. So
Speaker 1: this big storm ended up being a great opportunity for
Speaker 1: the scientists who were trying to make a weather model,
Speaker 1: and the model they developed seemed to simulate actual events accurately.
Speaker 1: They were very excited. This multi level model appeared to
Speaker 1: be much more accurate than the barotropic model that had
Speaker 1: been used as it turned out their model was only
Speaker 1: really accurate for that one set of circumstances. They found
Speaker 1: that as they ran more simulations, it was not giving
Speaker 1: accurate forecasts, at least not in every situation. So it
Speaker 1: turned out that that weather model was really great for
Speaker 1: one set of circumstances, but it didn't handle other ones
Speaker 1: nearly as well. It didn't get nearly as accurate a result,
Speaker 1: and the barotropic model actually was superior. The older model
Speaker 1: that had been running on any act was superior to
Speaker 1: the multi level model, at least in some situations. So
Speaker 1: throughout the nineteen fifties, meteorologists were mostly relying on this
Speaker 1: older barotropic model because it was more accurate more often
Speaker 1: than multi level models that had been proposed. Starting in
Speaker 1: ninety eight, multi level models began to gain more acceptance
Speaker 1: as they tuned into the right waitings for the various
Speaker 1: variables and weather forecasting, and from that point forward we
Speaker 1: saw more varieties of weather models arise, each with its
Speaker 1: own pros and cons, And what followed were numerous symposes
Speaker 1: about computational models and the machines that would be needed
Speaker 1: to crunch the numbers in a reasonable amount of time.
Speaker 1: And it gets super duper technical. Now today we have
Speaker 1: many models, most of them covering specific regions. Creating a
Speaker 1: global weather model is an enormous task, and not just
Speaker 1: to combine our understanding of weather behavior from around the globe,
Speaker 1: but also to find a computer capable of processing such
Speaker 1: an enormous amount of data regularly enough to give us
Speaker 1: an accurate weather forecast at any given time for any
Speaker 1: given location. But here's some of the models that we
Speaker 1: use today. One of the big ones is the European
Speaker 1: Center for Medium Range Weather Forecasts or e c MWF.
Speaker 1: They provide one of the more important models in the world.
Speaker 1: The Journal of Computational Physics describes the model as quote
Speaker 1: a spectral primitive equation model with a semi Lagrangian, semi
Speaker 1: implicit time scheme and a comprehensive treatment of physical processes.
Speaker 1: I'm pretty sure that means it can summon cthulhu. In addition,
Speaker 1: this model is coupled with an ocean wave model, and
Speaker 1: the basis is the Integrated Forecast System or i f S.
Speaker 1: The model runs on high performance supercomputers capable of performing
Speaker 1: several terra flops of calculations, and just a reminder, a
Speaker 1: flop is a floating point operations per second. So generally speaking,
Speaker 1: the number of flops the computer can perform gives you
Speaker 1: an idea of its processing power or speed, and terra
Speaker 1: flops means a lot. All Right, we're in the home
Speaker 1: stretch for meteorology. But before we jump into that final section,
Speaker 1: let's take another quick break to thank our sponsor. Alright,
Speaker 1: I just talked about the weather model over the big
Speaker 1: one over in Europe, and keep in mind there are
Speaker 1: dozens of weather models, but over here in the good
Speaker 1: old US of A, you've got a big one with
Speaker 1: the National Centers for Environmental Prediction or in c e
Speaker 1: P as it is known, and it has a globally
Speaker 1: gridded set of data about the state of the Earth's atmosphere.
Speaker 1: And there are tons of other models too. As I
Speaker 1: was just saying, some of them are more localized than others.
Speaker 1: Some of them are capable of much higher resolution because
Speaker 1: they consult more observation systems with respect the area covered
Speaker 1: by the model, and those grids are important. You want
Speaker 1: um smaller grids, You want the the sides of each
Speaker 1: of the grids. To keep in mind, this is three dimensional.
Speaker 1: It's not just um land area, but elevation as well.
Speaker 1: You want smaller grids because that increases that resolution, right,
Speaker 1: because each grid represents an area where you understand what
Speaker 1: is going on inside of that area. The smaller you
Speaker 1: make the grids, the higher the resolution is. This is
Speaker 1: again a lot like your television or computer display. If
Speaker 1: you make a picture out of just a few pixels,
Speaker 1: it will be blocky. It has very low resolution. Like
Speaker 1: think about eight bit graphics back in the day. Every
Speaker 1: all the characters on video games were made up of
Speaker 1: these blocks. They all had very jagged edges. It was
Speaker 1: not a high resolution. If you use more pixels to
Speaker 1: make your photo, that improves the resolution to a point. Anyway,
Speaker 1: there gets to a point where we can't really perceive
Speaker 1: it anymore, but you certainly can perceive it at those
Speaker 1: early stages. So the smaller, the smaller, and more numerous
Speaker 1: the pixels, the higher the resolution is and the higher
Speaker 1: quality you get of an image up to a certain point.
Speaker 1: The same is true for weather models. So if you
Speaker 1: have a grid with small squares, such as on the
Speaker 1: order of a few kilometers per side, you would have
Speaker 1: a high resolution, and those grid points can have a
Speaker 1: single value per atmospheric variable per observation. So another words,
Speaker 1: you get a value for temperature, a value for wind direction,
Speaker 1: a value for wind speed, a value for atmospheric density,
Speaker 1: et cetera. Uh, all of that tends to be consulted
Speaker 1: about once an hour with most of these models. So
Speaker 1: once an hour you pull all that observational data for
Speaker 1: every square or cube if you prefer, within that grid.
Speaker 1: So you pull all of the information for all of
Speaker 1: the grids within that area or all the cubes within
Speaker 1: that grid, and you crunch the numbers from all of
Speaker 1: that to see how weather will progress from that moment forward. Now,
Speaker 1: if one variable from one grid is way off, it
Speaker 1: can cause bigger errors and forecasts further down the line.
Speaker 1: That garbage in, garbage out thing I was talking about,
Speaker 1: and this is the infamous butterfly effect. The butterfly effect
Speaker 1: refers to a small effect that can have much larger
Speaker 1: consequences further on in time, and you've probably heard about
Speaker 1: the effect before. The classic example is that you have
Speaker 1: a butterfly flapping its wings in South America and the
Speaker 1: force from the breeze generated from the flapping ends up
Speaker 1: contributing to a system that eventually grows in power and
Speaker 1: ultimately culminates in a massive typhoon in Asia, for example.
Speaker 1: Now that's just a thought experiment, obviously, but with weather
Speaker 1: models something similar can happen. If you have a large
Speaker 1: grid and each square in the grid is representing a
Speaker 1: relatively small area, and one of those areas within that
Speaker 1: grid produces data that doesn't reflect real conditions, your forecasts
Speaker 1: will be affected by this. Now, depending upon the weight
Speaker 1: of the variable in question, it could make the entire
Speaker 1: forecast inaccurate after a certain amount of time. Generally speaking,
Speaker 1: the further out you go in time, the more you
Speaker 1: have to depend upon numerical forecast models. A short term
Speaker 1: forecast might not require a full numerical analysis. It could
Speaker 1: depend and more on what's going on right now and
Speaker 1: the likelihood of how weather will change over the next
Speaker 1: few hours, so you can refer more on experience in
Speaker 1: those cases. Beyond that, however, you'll need some more numerical
Speaker 1: analysis to get a better than UH to do better
Speaker 1: than just giving a wild guess. So even so, you
Speaker 1: might run the data through a couple of different models
Speaker 1: to look at potential forecasts, and from that point and experience,
Speaker 1: meteorologists might look over the data to see which predictions
Speaker 1: appeared to be the most realistic. Sometimes computer models get
Speaker 1: stuff wrong. They might predict an extremely unlikely outcome. Other
Speaker 1: models might have a very different forecast. The meteorologist has
Speaker 1: to determine which of these outcomes best represents what is
Speaker 1: likely to actually happen, based upon how well they seem
Speaker 1: to handle the current weather situations. So you might look
Speaker 1: at a computer model and say, well, how is it
Speaker 1: handling what's going on right now. If it's doing that well,
Speaker 1: then we can at least lay some assumptions that the
Speaker 1: any predictions coming from this computer model are going to
Speaker 1: at least be semi accurate. If it's not handling it well,
Speaker 1: then we may need to consult a different weather model
Speaker 1: for this particular forecast. Now, if observation data is affected,
Speaker 1: as in, if there are problems with sensors, then the
Speaker 1: information you will get out of the models will not
Speaker 1: be dependable. The high resolution can help smooth this out.
Speaker 1: So if you're getting one sensor with erroneous data, but
Speaker 1: you've got lots of other sensors in the area, you
Speaker 1: could possibly smooth that out. You might say, well, this
Speaker 1: is clearly an anomaly. If most of your observation stations
Speaker 1: are reporting that the temperature is about seventy degrees fahrenheit,
Speaker 1: and one of them is saying it's degrees fahrenheit. You
Speaker 1: could say, well, this one clearly there's an anomaly. Maybe
Speaker 1: something is going on in that area. Maybe it's close
Speaker 1: to a fire or something not too close but fairly close.
Speaker 1: Because pretty off the track for everything else, you can
Speaker 1: perhaps week your model to ignore that particular sensor so
Speaker 1: that way it doesn't affect the rest of your forecast
Speaker 1: and throw things into disarray. But if you have just
Speaker 1: very few observation stations, then the loss of even one
Speaker 1: could be enough to throw your forecast off anyway, So
Speaker 1: you may end up having your forecast thrown off either
Speaker 1: because a sensor is giving you incorrect information or because
Speaker 1: without that sensor you don't have enough information to build
Speaker 1: a reliable prediction. So it's a delicate thing. Meteorologists also
Speaker 1: have to keep up with what is actually happening at
Speaker 1: any given time, and this seems pretty evident, but it's
Speaker 1: important to either verify that a computer model is in
Speaker 1: fact forecasting, whether accurately or if it's off track. And
Speaker 1: we learn more from our mistakes than we do from successes.
Speaker 1: Just like in that example of that first multi level model,
Speaker 1: the scientist thought that they had really hit on something
Speaker 1: because their models seemed to handle the conditions of that
Speaker 1: Thanksgiving Day storm and give very realistic outcomes, But as
Speaker 1: it turned out, it wasn't good at handling other situations.
Speaker 1: If we succeed, we might think that what we've done
Speaker 1: is perfect, that it's worked, but it may turn out
Speaker 1: that that's not the case. When we fail, we realize, oh,
Speaker 1: something is not quite right here. We need to figure
Speaker 1: out what that is and how to correct for it.
Speaker 1: This is true, by the way, in all areas of science,
Speaker 1: we learn more from our failures than we do from
Speaker 1: our successes. As computers get more powerful, the simulations can
Speaker 1: take in more data and in theory, will generate more
Speaker 1: accurate forecasts. The addition of some other elements, such as
Speaker 1: deep learning algorithms that can assign probabilities to outcomes, might
Speaker 1: also help. These probabilistic models assigned statistical probabilities to various outcomes,
Speaker 1: letting meteorologists or even an artificially intelligent program determine which
Speaker 1: one most likely represents what is really going to happen.
Speaker 1: But some challenges will remain. I'd like to end this
Speaker 1: episode by quoting from the second edition of Introduction to
Speaker 1: Tropical Meteorology to illustrate just how dann complex this is,
Speaker 1: and I quote from chapter nine of said textbook, Tropical
Speaker 1: weather is difficult to forecast. Mid latitude weather is dominated
Speaker 1: by synoptic systems moving in the westerly's, which formed the
Speaker 1: basis for the weather analysis methods developed in the nineteenth
Speaker 1: and twentieth centuries. In the mid latitudes, baro clinic instability
Speaker 1: results from air masses with contrasting temperature and density. Their
Speaker 1: energy is concentrated in extra tropical cyclones that can be
Speaker 1: tracked fairly easily. By comparison, the tropics have a relatively
Speaker 1: homogeneous air mass and fairly uniform distribution of surface temperature
Speaker 1: and pressure. Therefore, local and mesoscale effects are more dominant
Speaker 1: than synoptic influences, except for tropical cyclones. For example, service
Speaker 1: temperature and pressure can change quickly with convection and sea breezes.
Speaker 1: So you see, we cannot We cannot apply what works
Speaker 1: for one area across all areas of Earth because it
Speaker 1: just doesn't work that way. There are some areas where
Speaker 1: things that are of a major impact on weather patterns
Speaker 1: are almost non factors, and so until we develop very
Speaker 1: specific particular ways of observing, measuring, and predicting weather across
Speaker 1: all of Earth, and then synthesizing that so that we
Speaker 1: can give global weather forecasts that can then narrow down
Speaker 1: to the hyperlocal area, we're going to continue to have.
Speaker 1: This uncertainty is a phenomenal area of science. It is
Speaker 1: remarkable to see technology applied to that area of sience
Speaker 1: in such a way that is easy to illustrate the
Speaker 1: importance of pushing back the barriers of computational power. It's
Speaker 1: why a lot of people look at stuff like Moore's
Speaker 1: lawns said say, it's really important that we keep that going,
Speaker 1: even though it's getting harder and harder to keep Moore's
Speaker 1: law relevant, because we do have these needs for heavy
Speaker 1: computational loads that have real effects and and and powerful
Speaker 1: outcomes for billions of people on this planet. I mean,
Speaker 1: accurate weather forecasts have the the potential to affect or
Speaker 1: to save people from calamity, or to help businesses determine
Speaker 1: when and where they're going to transport goods to get
Speaker 1: to people more effectively, thus reducing lots of things like
Speaker 1: environmental impact and the economic impact. You start to see
Speaker 1: how intrinsic our weather is to everything else we do.
Speaker 1: It's more than just small talk, and it's more than
Speaker 1: just whether or not you need to grab your umbrella
Speaker 1: before you leave the house. And I hope that these
Speaker 1: episodes were interesting to you. I find meteorology to be
Speaker 1: absolutely fascinating and I would love to learn more about it.
Speaker 1: And I love chatting with meteorologists because even though they
Speaker 1: can get super technical and they can really talk about
Speaker 1: some heavy duty math that I can sometimes kind of
Speaker 1: sort of follow. There dedication to understanding something so complex
Speaker 1: I find inspiring. Now, our next episode will be about
Speaker 1: the history of programming languages. It's likely going to be
Speaker 1: a two part episode, and I'm going to study the
Speaker 1: origin and evolution of computer languages. But if you have
Speaker 1: suggestions for future episodes of tech Stuff, please share them
Speaker 1: with me. You can send me an email. The address
Speaker 1: is tech Stuff at how stuff works dot com, or
Speaker 1: you can drop me a line on Facebook or Twitter.
Speaker 1: The handle of both of those is tech Stuff hs W.
Speaker 1: You can always drop in and watch me record these
Speaker 1: episodes live that you can find at Twitch dot tv,
Speaker 1: slash tech Stuff. I record on Wednesdays and Friday's today.
Speaker 1: If you had joined us in the studio, you would
Speaker 1: see that I'm in a brand new studio, or at
Speaker 1: least a different one from the one I'm usually in,
Speaker 1: and that Dylan has been separated from me by a
Speaker 1: pain of soundproof glass, as nature intended. And I hope
Speaker 1: that you guys can join me for future episodes. Just
Speaker 1: go to twitch dot tv slash tech Stuff and you'll
Speaker 1: find a schedule right then and there, and I'll talk
Speaker 1: to you guys again really soon for more on this
Speaker 1: and of sense of other topics. Is it how stuff
Speaker 1: works dot com, wh