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