Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio and how the tech
Speaker 1: are you. It's Friday, so it's time for another classic episode.
Speaker 1: This is the second part of a two parter called
Speaker 1: weather Tech Part two. So if you didn't listen to
Speaker 1: last week's classic episode of weather Tech Part one, I
Speaker 1: recommend you do that. That would have been last Friday.
Speaker 1: This episode originally published on April twenty first, two sixteen. Enjoy.
Speaker 1: We are now going to join the podcast already in progress.
Speaker 1: We were able to gather a lot of information once
Speaker 1: we had those basic tools available to us. But what
Speaker 1: really pushed meteorology forward is when we could stop relying
Speaker 1: upon the data that we can gather here on the
Speaker 1: ground and supplement that with information from the atmosphere itself.
Speaker 1: And that brings us to weather balloons. Weather balloons more
Speaker 1: than just fodder for your roswell conspiracy right right, Swamp
Speaker 1: gas and weather balloons and weather balloons do more than
Speaker 1: just act as a subplot in an X Files episode. Right, Yes,
Speaker 1: They're very important. Yeah, so they carry instrumentation that collects
Speaker 1: data about atmospheric conditions and weather balloons have been around
Speaker 1: for a long time, but more recently they typically carry
Speaker 1: instruments called radiosond, which is a battery powered device that
Speaker 1: can measure altitude, atmospheric pressure, temperature, humidity, wind speed. Sometimes
Speaker 1: there's a GPS element to it, so it can so
Speaker 1: people on the ground can track where the weather balloon is.
Speaker 1: Normally the you tether these devices. You don't just release
Speaker 1: a weather balloon and say sea, but sometimes you know
Speaker 1: you need to have that GPS element there too. And
Speaker 1: getting this information from the atmosphere is really important because
Speaker 1: it can tell you about how conditions may soon change
Speaker 1: on the ground. It's pretty interesting actually to ever if
Speaker 1: you've ever had a chance to look at some of
Speaker 1: the data pulled from these, because you see how different
Speaker 1: conditions in the atmosphere are compared to what we experience here,
Speaker 1: including some pretty intense winds at higher altitudes. So we've
Speaker 1: got all this information being collected, and before we get
Speaker 1: into space, because that'll be the next step outward, I
Speaker 1: wanted to talk a little bit about what we do
Speaker 1: with all that data. One of the things we do
Speaker 1: is we create databases that have all this information. So
Speaker 1: that let's say that we have a day with pretty
Speaker 1: nice weather, we collect all the information about that. What
Speaker 1: was the atmospheric pressure, what is the temperature, how much
Speaker 1: humidity was in the air, what was the wind speed,
Speaker 1: were there any higher low pressure systems nearby? Were the
Speaker 1: what kind of front had just moved through? All this
Speaker 1: sort of information, we feed it all into a database.
Speaker 1: Collecting that over and over and over again allows us
Speaker 1: to build a better virtual understanding of how weather works, right,
Speaker 1: and we can supplement that with more information as we
Speaker 1: learn more about the weather. Then we would end up
Speaker 1: using that to help us make some predictions about how
Speaker 1: weather might be in the future. And to do that,
Speaker 1: really we use computer models. Typically we would build what
Speaker 1: was called a numerical weather prediction model, the NWP. So
Speaker 1: this is really a model that's made up of a
Speaker 1: bunch of different calculations that take all of the different
Speaker 1: variables into account and tell you, based upon all the
Speaker 1: variables available to us, here's what it looks like the
Speaker 1: weather is going to be like in X amount of time. Right, So,
Speaker 1: whenever we're talking about forecasts, obviously we have to worry
Speaker 1: about what are the current conditions and how far out
Speaker 1: are we trying to predict the weather? And on TV
Speaker 1: you might see five or seven or even these days
Speaker 1: sometimes ten. Yeah. Like if you go to weather dot com,
Speaker 1: they have a ten day forecast, which I always think
Speaker 1: is hilarious. And the reason I think it's hilarious is
Speaker 1: here's how those predictions work. You take all the information
Speaker 1: available to you, you run it through your computer model,
Speaker 1: which factors in these different variables and gives you sort
Speaker 1: of a percentage of probability of what your weather is
Speaker 1: going to be like in the next let's say hour.
Speaker 1: What if you want to look two hours ahead, Well,
Speaker 1: then what they do is they take the prediction that
Speaker 1: they made for an hour from now and extrapolate from there, saying, well,
Speaker 1: if in fact the weather is what we think it's
Speaker 1: going to be like in an hour, this is what
Speaker 1: should look like two hours from now. Well, if you
Speaker 1: want to look at three hours from now, well let's
Speaker 1: take what the results were for two hours from now
Speaker 1: and extrapolate again, and that's what we think it's going
Speaker 1: to be three hours from now. Extend that out to
Speaker 1: ten days. Yeah, and it's going to become less and
Speaker 1: less reliable, Yes, because you're basing your predictions upon the
Speaker 1: results of a previous set of predictions, not upon a
Speaker 1: previous set of actual conditions. Right, So when you're tracing
Speaker 1: it all the way back and your starting point is
Speaker 1: right now, well, clearly, the further out we look, the
Speaker 1: more unreliable the information is going to be, the more
Speaker 1: likely some other variable that we have not anticipated will
Speaker 1: play a larger role or a smaller role, and that
Speaker 1: is going to affect the overall outcome of the of
Speaker 1: what will actually happen. The forecast is the same, but
Speaker 1: the actual thing we experience might be very different, which
Speaker 1: is why if you're planning a picnic and you've got
Speaker 1: ten days out from it and you're looking at the
Speaker 1: weather and it says it's going to be absolutely perfect,
Speaker 1: don't bet the house on it. Yes, that's not necessarily true.
Speaker 1: Not to discredit numerical weather predictions, because a lot of
Speaker 1: science and time goes into it. Yeah, but it's still
Speaker 1: you know, It's the way that I see modern meteorology
Speaker 1: is that over time we have continually built upon basically
Speaker 1: what our ancestors did and just gotten it's gotten more scientific,
Speaker 1: we've gotten better instruments, but it's still looking for patterns. Yeah,
Speaker 1: exactly right. So you might look at the patterns of
Speaker 1: when all of these conditions are in play. Out of
Speaker 1: the last one hundred times that that happened, this is
Speaker 1: how the weather turned out, and we're going to break
Speaker 1: it down. So maybe eighty days out of those one
Speaker 1: hundred days where the conditions were similar to today's, it
Speaker 1: didn't rain at all. It was perfectly sunny, so eighty
Speaker 1: out of one hundred it was lovely. The other twenty
Speaker 1: days it rained and it was just steady rain, and
Speaker 1: that's all there is to it. This is a super
Speaker 1: oversimplified version of what could happen. This is what would
Speaker 1: lead you to say there's a twenty percent chance of rain,
Speaker 1: because he would say, all right, now, the last hundred
Speaker 1: times the weather was exactly like it is today, twenty
Speaker 1: of those times it rained, eighty of those times it
Speaker 1: did not. Therefore, there is a twenty percent chance that
Speaker 1: it will rain. That again is oversimplifying the way it works,
Speaker 1: but generally speaking, that's kind of how they come to
Speaker 1: those determinations, And in fact, there are ways of bolstering
Speaker 1: the NWP by using something called model output statistics, which
Speaker 1: is I kind of just talked about it a little bit.
Speaker 1: I'll just go ahead and touch on it right now.
Speaker 1: It's essentially doing what we were talking about, looking at
Speaker 1: a specific region and the specific outcomes of days that
Speaker 1: had similar conditions to the one you're looking at right now,
Speaker 1: and then you're kind of making an educated guess based
Speaker 1: upon a computer model and actual localized history. I got
Speaker 1: that clear to something up for a lot of people.
Speaker 1: I sure hope so because I've just kind of gone
Speaker 1: with it. You know, I've just seen, oh, twenty percent
Speaker 1: chance of rain, okay, and never really thought about what
Speaker 1: goes into determining that. Well. And I know that there's
Speaker 1: some people who had, you know, when they saw twenty
Speaker 1: percent chance of rain, they thought it meant, oh, it's
Speaker 1: going to rain over twenty percent of the forecast area,
Speaker 1: which means that you know, that would be more like
Speaker 1: scattered showers. That's really what scattered showers means. When you're
Speaker 1: scattered showers, it means that parts of the forecast area
Speaker 1: are expected to get rain, but it will not necessarily
Speaker 1: rain over the entire forecast area. But if you hear
Speaker 1: twenty percent chance of rain, it does not mean that
Speaker 1: eighty percent of the forecast area is going to be
Speaker 1: dry and the other twenty percent it's going to be wet.
Speaker 1: Nor does it mean it will rain for twenty percent
Speaker 1: of the day. In fact, that's part of the problem.
Speaker 1: A prediction of precipitation, the good old pop, the pop
Speaker 1: so pop. That requires a time element to it as well.
Speaker 1: It doesn't mean anything without a time element. So if
Speaker 1: you say there's a twenty percent chance to rain, you
Speaker 1: also need to have an element of time attached to
Speaker 1: that to make it meaningful. So twenty percent chance to
Speaker 1: rain over the next six hours, then you know, all right,
Speaker 1: So it's not saying that's going to be twenty percent
Speaker 1: chance to rain or it's not gonna rain twenty percent
Speaker 1: of the day, just that for the next six hours
Speaker 1: there's a twenty percent chance it will be raining in
Speaker 1: the forecast area. We will be back with more weather
Speaker 1: technology after this quick break, so I hope that demystifies
Speaker 1: some of it. Also, we can talk about radar. One
Speaker 1: of my favorite things to talk about. Radar is awesome,
Speaker 1: favorite character on mash me too. Yeah, well, I think
Speaker 1: it's so adorable, right, Yeah, it's hard not to feel
Speaker 1: for him, and the fact that he can anticipate everything
Speaker 1: his commanding officer wants, and he can even say what
Speaker 1: the commanding officer is saying before the commanding officer has
Speaker 1: finished a sentence is obviously a key part of that operation.
Speaker 1: But we're talking about actual radar, using radar to detect weather.
Speaker 1: You've probably heard Doppler radar when looking at a weather report, like, well,
Speaker 1: let's look at the Doppler radar and see where this
Speaker 1: precipitation is moving in. Doppler radar for weather is different
Speaker 1: from Doppler radar used by say, police officers, who are
Speaker 1: trying to detect if you are speeding. The Doppler radar
Speaker 1: that meteorologists use actually shoots out radio waves in very
Speaker 1: short bursts called pulses, and then the radar listens for
Speaker 1: any echoing pulses coming back to the antenna, and the
Speaker 1: short pulses indicate not just the presence of something out there,
Speaker 1: but whether it's moving and which direction is it moving in.
Speaker 1: Is it moving toward the radar station or away from it.
Speaker 1: If a Doppler radar receiver detect waves of a higher frequency,
Speaker 1: the precipitation particles are moving toward the radar exactly, and
Speaker 1: lower frequencies they're moving away. Yes, Because what's happening is
Speaker 1: it's similar to a Doppler shift. And anyone who's ever
Speaker 1: heard a vehicle with a siren go past yeah, is
Speaker 1: familiar with this. It's a higher pitch as the vehicles
Speaker 1: coming toward you, and a lower pitch as it's moving away.
Speaker 1: What's actually happening is as a vehicle is moving toward you,
Speaker 1: the sound waves it's emitting are being compressed. Now that
Speaker 1: compression creates a higher frequency, which means we detect higher pitch.
Speaker 1: As the vehicle passes, those frequencies are elongated, which means
Speaker 1: a lower pitch. Same thing is true with the radar
Speaker 1: accept Instead of it being a pitch, it's a radio frequency.
Speaker 1: If so, a higher frequency will tell you, yeah, something's
Speaker 1: coming towards you, and a lower frequency will tell you
Speaker 1: something's moving away from you. And also the time between
Speaker 1: when the pulse goes out and when you detect it
Speaker 1: tells you the distance from the radar detection system and
Speaker 1: the precipitation. So you could even say there's a storm
Speaker 1: system that's five miles to the west, it's moving easterly
Speaker 1: at this speed because you've detected it through a series
Speaker 1: of pulses. If you have enough radar detection stations, you
Speaker 1: can even describe the shape of the weather system and
Speaker 1: talk about how some areas are more intense than others.
Speaker 1: You can get all of that information from this approach,
Speaker 1: and it's amazing how this thing works. First of all,
Speaker 1: it's super high power. These radar stations are they're they're
Speaker 1: generating or they're transmitting I should say, at four hundred
Speaker 1: and fifty thousand watts. So your typical microwave oven is
Speaker 1: a thousand watts. Wow, so you need a four hundred
Speaker 1: and fifty of those to equal one of these radar systems.
Speaker 1: So four and fifty thousand watts, and the pulse lasts
Speaker 1: so short as to be unimaginable. It is point zero
Speaker 1: zero zero zero zero one five seven seconds long, or
Speaker 1: one point five seven times ten to the minus six seconds.
Speaker 1: So if you hear the weatherman on TV bragging about
Speaker 1: Doppler radar, there's a reason. It's very impressive. Yeah. I mean,
Speaker 1: you're sitting at a burst of radio signals at such
Speaker 1: a fraction of a second that it is again impossible
Speaker 1: to even imagine. Meanwhile, then it listens for a longer period,
Speaker 1: and by longer I mean relatively longer. It's still a
Speaker 1: fraction of a second. It's point zero zero zero nine
Speaker 1: nine eight four three seconds. So it shoots out a pulse,
Speaker 1: listens for a little while, so it can detect when
Speaker 1: the pulse comes back and what frequency it's at, so
Speaker 1: it knows whether or not a body is moving toward
Speaker 1: it or away from it, and then it does it again.
Speaker 1: But that means, with that amount of time and the
Speaker 1: comparatively large amount of time of listening, for every hour
Speaker 1: of operation, the radio or the radar antenna is only
Speaker 1: shooting out signals for seven seconds out of an entire hour. Wow,
Speaker 1: that means for the fifty nine minutes fifty three seconds,
Speaker 1: it is not sending out a signal. It is listening.
Speaker 1: So for almost a full hour it's listening and only
Speaker 1: for seven seconds is it actively shooting out a signal.
Speaker 1: Can you mentioned it a conversation for someone who talks
Speaker 1: seven seconds out of an hour, it would I any
Speaker 1: conversation with me would last like a decade and before
Speaker 1: you could get a word in edgewise. Yeah, it's it's
Speaker 1: pretty amazing, and you usually would have one of these
Speaker 1: stations shooting out these radio bursts at different angles of elevation.
Speaker 1: These are called elevation slices, and when you go through
Speaker 1: the entire range, you get what was called volume coverage
Speaker 1: pattern or VCP, and that's what tells you what the
Speaker 1: activity is, not just at ground level, but up in
Speaker 1: the atmosphere as well. Toper radar can also detect tornadoes. Yeah. Yeah,
Speaker 1: if if the particles switch from moving toward and then
Speaker 1: away over a small distance, there's a good chance it
Speaker 1: could be a tornado. Yeah, we know a lot about
Speaker 1: those here in the southeast too. We get a lot
Speaker 1: of tornadoes, not as many as places in the you know,
Speaker 1: like in the Midwestern states, things like you know, Oklahoma
Speaker 1: and stuff. I mean, you guys get tornadoes even more
Speaker 1: frequently than we do, but we get them pretty serious.
Speaker 1: Actually this year hasn't been too bad. No, but there
Speaker 1: was one in November, yeah, which is weird because typically
Speaker 1: we get them in the spring. Yes, usually between March
Speaker 1: and June. That's kind of like our let's play it easy. Yeah,
Speaker 1: but you don't wash that on anybody. So no, I
Speaker 1: have been through a close call with a tornado while
Speaker 1: wearing Renaissance Festival gear. That sounds surreal. That was my
Speaker 1: final day when I did my first run at the
Speaker 1: festival in two thousand and one. Wow. Yeah, we had
Speaker 1: a really massive thunderstorm and at one point someone said
Speaker 1: that there was a tornado a tornado watch, but not
Speaker 1: a tornado warning watch, being that the conditions for a
Speaker 1: tornado forming are present, warning being that a tornado has
Speaker 1: actually been spotted in the region, in case you were wondering.
Speaker 1: So now let's talk about satellites and meteorology. So the
Speaker 1: computers are really good for building out those models and
Speaker 1: giving us predictions. The double radars really good at tracking precipitation.
Speaker 1: What do weather satellites do well, they're keeping an eye
Speaker 1: on global weather patterns. But there are two different types
Speaker 1: of weather satellites and they do this in different ways.
Speaker 1: So one is the geostationary weather satellite. Now, geostationary weather
Speaker 1: satellites maintain their relative position over a specific point on
Speaker 1: the Earth. They are at a very high orbit over
Speaker 1: the equator and they're always looking at the same thing
Speaker 1: because their orbit is at the same speed as Earth's rotation,
Speaker 1: not really the same speed, but relative speed. Because it's
Speaker 1: able to stay in that same point over that part
Speaker 1: of the Earth, and so they have to be on
Speaker 1: an equatorial orbit and they have to be at a
Speaker 1: particular altitude for this to work. It's great because it
Speaker 1: means they can keep an eye on a specific region.
Speaker 1: It's lousy because one, they're really far away, so the
Speaker 1: instrumentation you have to put on the satellites has to
Speaker 1: be incredibly sophisticated in order to get good readings from
Speaker 1: that altitude. Plus they have a limited view, right, They're
Speaker 1: always looking at one part of the Earth. They can't
Speaker 1: see anything else outside of that view. Always geostationary yep.
Speaker 1: So the other type you have are satellites that are
Speaker 1: in a polar orbit around the Earth. Polar orbits are interesting.
Speaker 1: So if you think of the Earth on its axis,
Speaker 1: the polar orbit is going parallel to the axis of
Speaker 1: the Earth. It's going perpendicular to the equator. So you
Speaker 1: would think of it as going from north to south
Speaker 1: and then south to north because once it crosses the
Speaker 1: south pole, you can only go north at that point.
Speaker 1: That's the only direction left to you, and it goes
Speaker 1: in that circle, which means these satellites get a full
Speaker 1: view of the entire Earth, because the Earth is rotating
Speaker 1: while it's going in this orbit north south orbit. But
Speaker 1: it also means that you only get a look at
Speaker 1: the same part of the Earth twice in a twenty
Speaker 1: four hour period, since once every twelve hours, you could
Speaker 1: always put another satellite up there, and that way you
Speaker 1: could get you put it on the opposite side of
Speaker 1: the Earth where it's in the same orbit, and then
Speaker 1: you get a look every six hours, just one from
Speaker 1: one satellite and then six hours later one from another satellite.
Speaker 1: But you also get to see everything on the planet,
Speaker 1: So there's your trade off is that you get a
Speaker 1: more comprehensive view, but you don't get a consistent view
Speaker 1: of any one part of the Earth with these kind
Speaker 1: of weather satellites. So a lot of weather surfaces depend
Speaker 1: upon both, yeah, so it's good to have both, yeah.
Speaker 1: And typically they carry devices called radiometers, which usually have
Speaker 1: a small telescope or some sort of antenna, a scanning
Speaker 1: device of some sort, and one or more detectors that
Speaker 1: can pick up visible, infrared or microwave radiation, and they
Speaker 1: use that to take measurements of the Earth and send
Speaker 1: that down to the planet's surface, so that weather stations
Speaker 1: around the world can take that data and crunch it
Speaker 1: and figure out what the heck is going out on
Speaker 1: out there when the frogs are raining from the sky
Speaker 1: apart from amphibious assault, and all of those measurements are
Speaker 1: actually done through little electrical voltages which then get digitized,
Speaker 1: so transformed into digital information before transmitted down to Earth,
Speaker 1: because you know, zapping electricity through space down to the planet,
Speaker 1: it's not the most efficient way of getting information across
Speaker 1: You certainly don't want to have a power chord stretch
Speaker 1: all the way there. That would just be such a pain,
Speaker 1: very inefficient. Yeah. Also with aircraft patterns. Yeah, and if
Speaker 1: you don't have geostationary orbit, it gets wrapped up around
Speaker 1: the planet pretty quickly. Yeah. We will conclude our two
Speaker 1: part episode series about weather attack after this quick break.
Speaker 1: So how do meteorologists do things like predict temperature, like
Speaker 1: predict highs and lows and that kind of stuff? For
Speaker 1: this I went to a website that was written by
Speaker 1: a meteorologist named Jeff Haby, and boy howdy, did it
Speaker 1: suddenly dawn on me how much more complicated this was
Speaker 1: than I had even anticipated. But according to Haby, he
Speaker 1: looks at everything from thermal advection, wind speed, cloud cover,
Speaker 1: dew point, and the number of daylight hours expected for
Speaker 1: that region in order to come up with the prediction
Speaker 1: for the high temperature of the day in the low
Speaker 1: temperature of the day. So what the heck does all
Speaker 1: that mean? So thermal advection, what is that? That refers
Speaker 1: to the transportation of heat by a moving fluid. So
Speaker 1: typically the stuff that affects the advection include the strength
Speaker 1: of wind, So how hard is the wind blowing in
Speaker 1: that region? The temperature gradient between the warmer and colder areas.
Speaker 1: So if one area is warmer than the other, is
Speaker 1: it warmer by like a couple of degrees or is
Speaker 1: it more significant than that is ten degrees fahrenheit, that
Speaker 1: would be a much larger gradient, right, And the angle
Speaker 1: between the wind direction and the temperature gradient, if that
Speaker 1: angle is more narrow, you're going to see a greater
Speaker 1: thermal advection, meaning you'll see more temperature changes moving into
Speaker 1: an area from a different region. So that's just advection.
Speaker 1: That's a lot of play. The other one that you
Speaker 1: other term you might be a little confused by. I mean,
Speaker 1: wind speed makes sense, cloudcover makes sense daylight hours. All
Speaker 1: of that makes sense, but what about dew point? That
Speaker 1: refers to the temperature at which air must be cooled
Speaker 1: at constant barometric pressure for water vapor to condense. So
Speaker 1: that temperature, again is dependent upon things like the actual
Speaker 1: air pressure, right, and so the dew point changes based
Speaker 1: upon those other factors as well. So all those have
Speaker 1: to be taken into account before a meteorologist can forecast
Speaker 1: what the temperature is going to be the next day.
Speaker 1: This is why we're so happy to have those complicated
Speaker 1: computers now, because if you were to keep track of
Speaker 1: this yourself, you probably go bonkers. And then we have
Speaker 1: like the idea of the probability of precipitation, which we
Speaker 1: kind of talked about already, but generally speaking, there's some
Speaker 1: weather services that will only predict rainfall if it's expected
Speaker 1: to be over a certain amount, like point two five millimeters.
Speaker 1: If it's going to be less than point two five millimeters,
Speaker 1: it doesn't even register as rainfall in predictions. You would
Speaker 1: say there's a zero percent chance or whatever, if that's
Speaker 1: what you think is going to be the accumulation. Some
Speaker 1: other ones are like, no any rain at all counts
Speaker 1: if it's one drop of rain it rained in that region,
Speaker 1: so it really depends upon the service. But that we
Speaker 1: already talked about the percentages and what those means, so
Speaker 1: hopefully that clears things up. And again that kind of
Speaker 1: goes into that concept of model output statistics, where you
Speaker 1: correct for your predictions based upon past conditions for a
Speaker 1: particular region. All of this comes together to create the
Speaker 1: weather report that you see. So I think the real
Speaker 1: takeaway here is it's incredible the amount of schooling and
Speaker 1: expertise a meteorologist has to have in order to do
Speaker 1: his or her job properly. Yes, right, like, because you
Speaker 1: see how complicated this is, and you start to have
Speaker 1: an appreciation of all right, they said it was a
Speaker 1: sixty percent chance of rain. I brought my umbrella and
Speaker 1: never rained. Now you realize, well, when you're talking about
Speaker 1: the system, this complicated and this unpredictable, something that can
Speaker 1: change dramatically just because something you did not anticipate happened,
Speaker 1: you start to appreciate more the challenge that they have
Speaker 1: to do their jobs properly. So give your meteorologist a
Speaker 1: hug and say thank you, because this stuff is hard. Yeah,
Speaker 1: and my you know if weather is the state of
Speaker 1: the atmosphere from day to day, and the atmosphere is
Speaker 1: super complex, you know, it's it's my favorite analogy from
Speaker 1: our article on our website about meteorologies, that the atmosphere
Speaker 1: is like a soup with too many cooks. Yeah. Yeah,
Speaker 1: there's a lot at play and so many different variables
Speaker 1: are working behind the scenes to give you a simple
Speaker 1: a simple weather forecast that is easily digestible. Yeah. This
Speaker 1: is also why when you hear about supercomputers running weather simulations,
Speaker 1: that's why you need a supercomputer because of this too
Speaker 1: many cooks. I mean, it takes a lot to make
Speaker 1: us stew. I hope, I hope at least some of
Speaker 1: you are singing along now, But yeah, it's it really
Speaker 1: does explain why you need that massive amount of computing
Speaker 1: power just to do something that you would think would
Speaker 1: be fairly simple. You know, you're thinking like, oh, it's
Speaker 1: like six or seven factors, and then you realize, oh, wait, no,
Speaker 1: there are these other things that also have an effect,
Speaker 1: and in some cases a measurable and meaningful effect, not
Speaker 1: just a potential effect. And it really does drive home
Speaker 1: that it's amazing we can have relatively accurate weather predictions
Speaker 1: in the first place. And also it makes me kind
Speaker 1: of sad that I no longer I used to be
Speaker 1: on television with a local weather guy. He did a
Speaker 1: show at five thirty in the morning, and I would
Speaker 1: show up on television and do a gadget segment with him,
Speaker 1: and it was great. He was very nice, and his
Speaker 1: ability to break down complicated concepts of weather in a
Speaker 1: way that was helpful to people so that they could
Speaker 1: plan their day was really amazing, especially when you start
Speaker 1: really thinking about all the things that come into play
Speaker 1: to make that, you know possible. So our hats are
Speaker 1: off to you meteorologists out there, keep doing the good work.
Speaker 1: I look forward to learning more about you know, when
Speaker 1: we when we figure out even more details about the
Speaker 1: complexities of the atmosphere and perhaps are able to make
Speaker 1: even more accurate models, maybe we will one day reach
Speaker 1: the back to the future too level where minute by
Speaker 1: minute it tells you what the weather is going to be.
Speaker 1: Of course, then I think they were actually suggesting that
Speaker 1: we would have weather control, which is a whole other
Speaker 1: thing that's opening and cannle worms. Yeah, I talked to
Speaker 1: Dylan that I said I had thought about doing a
Speaker 1: little discussion about weather control. But obviously we've gone pretty
Speaker 1: long already. So what I will say about weather control
Speaker 1: is weather systems represent a huge amount of energy, and
Speaker 1: in order for us to affect or manufacture weather events
Speaker 1: on a large scale, we would have to be able
Speaker 1: to generate that amount of energy and pour it into
Speaker 1: the atmosphere in a way that actually does what we
Speaker 1: wanted to do. And we're so far away from any
Speaker 1: of those things that it's absolutely unrealistic to think of
Speaker 1: weather control, even if you are a Cobra commander. Yeah,
Speaker 1: you know, going back to the beginning of this episode
Speaker 1: with that listener request for this, and yeah, their father
Speaker 1: said that in the sixties that they felt like the
Speaker 1: weather report was just a joke. And you fast forward
Speaker 1: to now and how you know, maybe some days you
Speaker 1: grab an umbrella and you don't need it, but that
Speaker 1: it's you know, you can track major weather patterns and
Speaker 1: incoming storms. That we have a pretty good ability to
Speaker 1: track hurricanes and like flash floods and things like that.
Speaker 1: I can't imagine where I'll be in forty or fifty years. Yeah,
Speaker 1: the fact that we can get at least heads up
Speaker 1: on stuff before it becomes critical to us, is really important.
Speaker 1: I mean, Dylan, you probably remember it wasn't that long
Speaker 1: ago here in Atlanta when we had the snow apocalypse. Yes,
Speaker 1: and because it was one of those things where the
Speaker 1: initial weather report suggested that the weather was going to
Speaker 1: miss the city and it didn't, that's an indication that, yeah,
Speaker 1: our predictions are not one hundred percent accurate, they're not infallible.
Speaker 1: And it also taught us a valuable lesson, which is
Speaker 1: that even when you feel like there's a pretty good
Speaker 1: chance that you're going to miss out on that bad weather,
Speaker 1: it doesn't hurt to prepare because the alternative is to
Speaker 1: spend eight hours on two eighty five, even if it
Speaker 1: is an inch of snow, because it's Atlanta. Yeah, and
Speaker 1: because we don't have a system in place to deal
Speaker 1: with an inch of snow, and we got a lot
Speaker 1: of hills. None of us have snow tires. Why would you?
Speaker 1: And you let out the private in the public sector
Speaker 1: at the same time, that was particularly bad. I remember
Speaker 1: I actually stayed here, not here, but in our old
Speaker 1: office location. I stayed there pretty much through the full
Speaker 1: day because it was like, I'm gonna take Martha, I'm
Speaker 1: gonna take the train. It's not gonna be a big deal.
Speaker 1: It took me three hours to get home, usually would
Speaker 1: take me forty five minutes, and I was getting I
Speaker 1: got online, got ready to complain, and then I started
Speaker 1: reading messages for my friends who were stuck in their
Speaker 1: cars and had been for six hours. I thought, yeah, okay,
Speaker 1: we're going to back away from the community slowly. Kids
Speaker 1: stuck in school buses overnight. Yeah. Yeah, pretty rough stuff.
Speaker 1: So yeah, we're We're not perfect, but it is getting better,
Speaker 1: and it is pretty impressive to see the amount of
Speaker 1: information you can get. I love, I mean I love.
Speaker 1: I find watching Doppler radar readouts to be fascinating, Like
Speaker 1: I could have that open on my desk all day
Speaker 1: if I if I didn't have other stuff I need
Speaker 1: to do. So Drees, thank you so much for sending
Speaker 1: that request in. It's a lot of fun to kind
Speaker 1: of read up on it and to go over Dylan,
Speaker 1: thank you for joining me in the studio. Thanks for
Speaker 1: having me greatly appreciate it. Well, that was it for WeatherTech,
Speaker 1: at least from back in twenty sixteen. Again, I'll probably
Speaker 1: need to do an update to this whenever I do
Speaker 1: these classic episode intros naltros. I'm always reminded, Oh yeah,
Speaker 1: I should really revisit this. This is one of those.
Speaker 1: But if there are topics like new ones, or maybe
Speaker 1: there's a topic you think I should revisit that I
Speaker 1: haven't really talked about in a long time, you should
Speaker 1: let me know. And there are a couple of different
Speaker 1: ways of doing that. You can go to Twitter and
Speaker 1: you can tweet to me. The show handle is tech
Speaker 1: Stuff HSW or if you prefer to actually talk to me,
Speaker 1: you can download the iHeartRadio app. It's free to downloads
Speaker 1: free to use. You can navigate over to tech Stuff
Speaker 1: by putting that into the little search field and you
Speaker 1: will see on the tech Stuff podcast page there's a
Speaker 1: little microphone icon. If you click on that icon, you
Speaker 1: can leave a voice message up to thirty seconds in
Speaker 1: length and let me know what you would like me
Speaker 1: to talk about in the future, and I will talk
Speaker 1: to you again really soon. Y text Stuff is an
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