1 00:00:04,519 --> 00:00:13,480 Speaker 1: Technology with tex Stuff from Hastuff. Hey there, and welcome 2 00:00:13,520 --> 00:00:16,400 Speaker 1: to Tech Stuff. I am your host, Jonathan Strickland, and 3 00:00:16,440 --> 00:00:20,439 Speaker 1: now I'm going to bring you the exciting conclusion about 4 00:00:20,640 --> 00:00:25,680 Speaker 1: whether Technology Dylan has joined me for this episode. This 5 00:00:25,840 --> 00:00:28,120 Speaker 1: was a show that we recorded all in one go, 6 00:00:28,320 --> 00:00:30,080 Speaker 1: but it turned out to be too long for a 7 00:00:30,120 --> 00:00:33,120 Speaker 1: single episode, so we've broken it up and that means 8 00:00:33,159 --> 00:00:36,760 Speaker 1: we are now going to join the podcast already in progress. 9 00:00:38,640 --> 00:00:41,560 Speaker 1: We were able to gather a lot of information once 10 00:00:41,640 --> 00:00:45,199 Speaker 1: we had those basic tools available to us. But what 11 00:00:45,479 --> 00:00:50,720 Speaker 1: really pushed meteorology forward is when we could stop relying 12 00:00:50,840 --> 00:00:53,400 Speaker 1: upon the data that we can gather here on the 13 00:00:53,440 --> 00:00:58,520 Speaker 1: ground and supplement that with information from the atmosphere itself. 14 00:00:59,080 --> 00:01:02,840 Speaker 1: And that brings us to weather balloons. Weather balloons more 15 00:01:02,920 --> 00:01:07,160 Speaker 1: than just fodder for your roswald uh concy right right, 16 00:01:07,440 --> 00:01:10,520 Speaker 1: swamp gas and weather balloons and uh, you know, weather 17 00:01:10,560 --> 00:01:13,319 Speaker 1: balloons do more than just act as a subplot in 18 00:01:13,360 --> 00:01:16,520 Speaker 1: an X Files episode. Right, Yes, They're very important. Yeah, 19 00:01:16,600 --> 00:01:23,640 Speaker 1: So they carry instrumentation that collects data about atmospheric conditions 20 00:01:23,720 --> 00:01:26,080 Speaker 1: and weather balloons have been around for a long time, 21 00:01:26,240 --> 00:01:31,560 Speaker 1: but more recently they typically carry instruments called radio SOND, 22 00:01:31,720 --> 00:01:34,400 Speaker 1: which is a bettery powered device that can measure altitude, 23 00:01:34,440 --> 00:01:39,400 Speaker 1: atmospheric pressure, temperature, humidity, wind speed. Sometimes there's a GPS 24 00:01:39,480 --> 00:01:41,960 Speaker 1: element to it, so it can so people on the 25 00:01:42,000 --> 00:01:44,600 Speaker 1: ground can track where the weather balloon is. Normally, the 26 00:01:45,000 --> 00:01:48,000 Speaker 1: you tether these devices. You don't just release a weather 27 00:01:48,000 --> 00:01:51,480 Speaker 1: balloon and say sia uh. But sometimes you know you 28 00:01:51,480 --> 00:01:55,280 Speaker 1: need to have that GPS element there too. And getting 29 00:01:55,280 --> 00:01:57,760 Speaker 1: this information from the atmosphere is really important because it 30 00:01:57,800 --> 00:02:00,760 Speaker 1: can tell you about how conditions may soon change on 31 00:02:00,840 --> 00:02:06,200 Speaker 1: the ground. Um. It's pretty interesting actually to ever if 32 00:02:06,240 --> 00:02:08,480 Speaker 1: you've ever had a chance to to look at some 33 00:02:08,560 --> 00:02:12,240 Speaker 1: of the data pulled from these because you you see 34 00:02:12,320 --> 00:02:15,480 Speaker 1: how different conditions in the atmosphere are compared to what 35 00:02:15,560 --> 00:02:23,320 Speaker 1: we experience here, including some pretty intense winds at higher altitudes. UM. 36 00:02:23,400 --> 00:02:27,880 Speaker 1: So we've got all this information being collected. Uh. And 37 00:02:27,960 --> 00:02:30,640 Speaker 1: before we get into space, because that'll be the next 38 00:02:30,720 --> 00:02:33,520 Speaker 1: step outward, I wanted to talk a little bit about 39 00:02:33,520 --> 00:02:36,160 Speaker 1: what we do with all that data. One of the 40 00:02:36,200 --> 00:02:39,880 Speaker 1: things we do is we create databases that have all 41 00:02:39,880 --> 00:02:42,480 Speaker 1: this information, so that let's say that we have a 42 00:02:42,600 --> 00:02:47,680 Speaker 1: day with pretty nice weather, we collect all the information 43 00:02:47,720 --> 00:02:50,880 Speaker 1: about that what was the atmospheric pressure, what was the temperature, 44 00:02:50,880 --> 00:02:53,160 Speaker 1: how much humidity was in the air, what was the 45 00:02:53,160 --> 00:02:57,119 Speaker 1: wind speed, were there any higher low pressure systems nearby? 46 00:02:57,360 --> 00:02:59,519 Speaker 1: What were the what kind of front had just moved through? 47 00:02:59,560 --> 00:03:01,560 Speaker 1: All this sort of information, we feed it all into 48 00:03:01,680 --> 00:03:05,280 Speaker 1: a database. Collecting that over and over and over again 49 00:03:05,320 --> 00:03:09,960 Speaker 1: allows us to build a better virtual understanding of how 50 00:03:10,040 --> 00:03:14,480 Speaker 1: weather works, right um, and we can supplement that with 51 00:03:14,520 --> 00:03:17,920 Speaker 1: more information as we learn more about the weather. Then 52 00:03:18,440 --> 00:03:21,880 Speaker 1: we would end up using that to help us make 53 00:03:21,919 --> 00:03:24,440 Speaker 1: some predictions about how weather weather it might be in 54 00:03:24,480 --> 00:03:27,280 Speaker 1: the future. And we to do that, really we use 55 00:03:27,320 --> 00:03:32,280 Speaker 1: computer models. Uh. Typically we would build what it's called 56 00:03:32,320 --> 00:03:39,800 Speaker 1: a numerical weather prediction model, the NWP. So this is 57 00:03:39,960 --> 00:03:42,880 Speaker 1: really a model that's made up of a bunch of 58 00:03:42,920 --> 00:03:46,920 Speaker 1: different calculations that take all of the different variables into 59 00:03:46,960 --> 00:03:51,160 Speaker 1: account and tell you, based upon all the variables available 60 00:03:51,200 --> 00:03:54,120 Speaker 1: to us, here's what it looks like the weather is 61 00:03:54,160 --> 00:03:58,200 Speaker 1: going to be like in X amount of time. Right. So, 62 00:03:58,280 --> 00:04:00,760 Speaker 1: whenever we're talking about forecasts, obviously we have to worry 63 00:04:00,760 --> 00:04:03,520 Speaker 1: about what are the current conditions and how far out 64 00:04:03,560 --> 00:04:07,760 Speaker 1: are we trying to predict the weather? And on TV, 65 00:04:07,920 --> 00:04:10,839 Speaker 1: you might see five or seven or even these days 66 00:04:10,880 --> 00:04:13,200 Speaker 1: sometimes ten. Yeah. Like if you go to go to 67 00:04:13,280 --> 00:04:15,720 Speaker 1: weather dot com, they have a ten day forecast, which 68 00:04:15,720 --> 00:04:18,960 Speaker 1: I always think is hilarious. And the reason I think 69 00:04:18,960 --> 00:04:23,520 Speaker 1: it's hilarious is here's how those predictions work. You take 70 00:04:23,560 --> 00:04:26,200 Speaker 1: all the information available to you, You run it through 71 00:04:26,279 --> 00:04:30,839 Speaker 1: your computer model, which factors in these different variables and 72 00:04:30,960 --> 00:04:34,960 Speaker 1: gives you sort of a a percentage of probability of 73 00:04:34,960 --> 00:04:36,800 Speaker 1: what your weather is going to be like in the 74 00:04:36,839 --> 00:04:40,960 Speaker 1: next let's say hour. What if you want to look 75 00:04:41,000 --> 00:04:43,560 Speaker 1: two hours ahead, Well, then what they do is they 76 00:04:43,560 --> 00:04:46,480 Speaker 1: take the prediction that they made for an hour from 77 00:04:46,520 --> 00:04:52,880 Speaker 1: now and extrapolate from there, saying, well, if in fact 78 00:04:53,000 --> 00:04:54,800 Speaker 1: the weather is what we think it's going to be 79 00:04:54,880 --> 00:04:58,280 Speaker 1: like in an hour, this is what should look like 80 00:04:58,400 --> 00:05:01,160 Speaker 1: two hours from now. Well if you want to look 81 00:05:01,200 --> 00:05:03,400 Speaker 1: at three hours from now, well let's take what the 82 00:05:03,440 --> 00:05:06,119 Speaker 1: results were for two hours from now and extrapolate again, 83 00:05:06,200 --> 00:05:07,599 Speaker 1: and that's what we think it's going to be three 84 00:05:07,600 --> 00:05:11,400 Speaker 1: hours from now. Extend that out to ten days. Yeah, 85 00:05:11,400 --> 00:05:15,559 Speaker 1: and it's going to become less and less reliable, Yes, 86 00:05:16,000 --> 00:05:20,279 Speaker 1: because you're you're basing your predictions upon the results of 87 00:05:20,279 --> 00:05:24,680 Speaker 1: a previous set of predictions, not upon a previous set 88 00:05:24,720 --> 00:05:30,200 Speaker 1: of actual conditions. Right, So when you're tracing it all 89 00:05:30,200 --> 00:05:35,159 Speaker 1: the way back and you're starting point is right now, like, well, clearly, 90 00:05:35,200 --> 00:05:38,520 Speaker 1: the further out we look, the more unreliable the information 91 00:05:38,560 --> 00:05:42,080 Speaker 1: is going to be, the more likely some other variable 92 00:05:42,120 --> 00:05:45,480 Speaker 1: that we have not anticipated will play a larger role 93 00:05:45,680 --> 00:05:48,880 Speaker 1: or a smaller role. Uh, and that is going to 94 00:05:48,960 --> 00:05:52,840 Speaker 1: affect the overall outcome of the fourth of what will 95 00:05:52,880 --> 00:05:56,000 Speaker 1: actually happen. The forecast is the same, but the actual 96 00:05:56,240 --> 00:05:59,160 Speaker 1: thing we experience might be very different. Which is why 97 00:05:59,360 --> 00:06:02,559 Speaker 1: if you're planning a picnic and you've got ten days 98 00:06:02,640 --> 00:06:04,880 Speaker 1: out from it and you're looking at the weather and 99 00:06:04,920 --> 00:06:09,159 Speaker 1: it says it's going to be absolutely perfect, don't bet 100 00:06:09,200 --> 00:06:12,200 Speaker 1: the house on it. That's not necessarily true. Not to 101 00:06:12,279 --> 00:06:15,840 Speaker 1: discredit numerical weather predictions, because a lot of science and 102 00:06:16,240 --> 00:06:20,960 Speaker 1: time goes into it, but it's still you know, it's 103 00:06:21,000 --> 00:06:23,920 Speaker 1: it's the way that my I see modern meteorology is 104 00:06:23,960 --> 00:06:28,200 Speaker 1: that over time we have continually built upon basically what 105 00:06:28,320 --> 00:06:31,599 Speaker 1: our ancestors did, and it's gotten it's gotten more scientific, 106 00:06:31,720 --> 00:06:35,000 Speaker 1: we've gotten better instruments, uh, but it's still looking for 107 00:06:35,080 --> 00:06:39,479 Speaker 1: patterns yeah, exactly right. So you might look at the 108 00:06:39,520 --> 00:06:43,240 Speaker 1: patterns of when all of these conditions are in play. 109 00:06:44,400 --> 00:06:48,280 Speaker 1: Out of the last hundred times that that happened, this 110 00:06:48,400 --> 00:06:51,400 Speaker 1: is how the weather turned out. Uh, And we're gonna 111 00:06:51,440 --> 00:06:54,960 Speaker 1: break it down. So maybe eighty days out of those 112 00:06:55,000 --> 00:06:58,280 Speaker 1: one days where the conditions were similar to today's, it 113 00:06:58,320 --> 00:07:01,839 Speaker 1: didn't rain at all. It was perfectly sunny, So eight 114 00:07:01,960 --> 00:07:04,960 Speaker 1: out of a hundred it was lovely. The other twenty 115 00:07:05,080 --> 00:07:09,479 Speaker 1: days it rained and it was just steady rain. And 116 00:07:09,840 --> 00:07:12,040 Speaker 1: that's all there is to it. This is a super 117 00:07:12,160 --> 00:07:16,200 Speaker 1: oversimplified version of what could happen. This is what would 118 00:07:16,240 --> 00:07:19,160 Speaker 1: lead you to say there's a chance of rain, because 119 00:07:19,200 --> 00:07:22,040 Speaker 1: he would say, all right, now, the last hundred times 120 00:07:22,120 --> 00:07:25,840 Speaker 1: the weather was exactly like it is today, twenty of 121 00:07:25,840 --> 00:07:28,840 Speaker 1: those times it rained, eighty of those times it did not. 122 00:07:29,360 --> 00:07:31,920 Speaker 1: Therefore there is a twenty chance that it will rain. 123 00:07:32,320 --> 00:07:36,240 Speaker 1: That again is oversimplifying the way it works, but generally speaking, 124 00:07:36,640 --> 00:07:40,240 Speaker 1: that's kind of how they come to those determinations, and 125 00:07:40,280 --> 00:07:44,000 Speaker 1: in fact that there are ways of bolstering the NWP 126 00:07:44,160 --> 00:07:48,200 Speaker 1: by using something called model output statistics, which is I 127 00:07:48,280 --> 00:07:50,120 Speaker 1: kind of just talked about a little bit. I'll just 128 00:07:50,160 --> 00:07:52,640 Speaker 1: go ahead and touch on it right now. It's essentially 129 00:07:52,760 --> 00:07:55,160 Speaker 1: doing what we were talking about, looking at a specific 130 00:07:55,240 --> 00:08:00,360 Speaker 1: region and the specific outcomes of days that had emmeler 131 00:08:00,400 --> 00:08:03,480 Speaker 1: conditions to the one you're looking at right now, and 132 00:08:03,520 --> 00:08:06,680 Speaker 1: then you're kind of making an educated guess based upon 133 00:08:06,680 --> 00:08:11,520 Speaker 1: a computer model and actual localized history. But that clear 134 00:08:11,560 --> 00:08:13,560 Speaker 1: to something up for a lot of people. I sure 135 00:08:13,600 --> 00:08:16,360 Speaker 1: hope so, because I've just kind of gone with it, 136 00:08:16,720 --> 00:08:19,160 Speaker 1: you know, I've just seen chance of rain, okay, and 137 00:08:19,240 --> 00:08:22,920 Speaker 1: never really thought about what goes into determining that well. 138 00:08:22,920 --> 00:08:25,720 Speaker 1: And I know that there's some people who had, you know, 139 00:08:25,920 --> 00:08:28,440 Speaker 1: when they saw chance of rain, they thought it meant, oh, 140 00:08:28,480 --> 00:08:32,120 Speaker 1: it's going to rain over of the forecast area, which 141 00:08:32,120 --> 00:08:33,640 Speaker 1: means that you know that that would be more like 142 00:08:33,679 --> 00:08:36,520 Speaker 1: scattered showers. That's really what scattered showers means. When you're 143 00:08:36,520 --> 00:08:40,720 Speaker 1: scattered showers, it means that parts of the forecast area 144 00:08:40,760 --> 00:08:43,280 Speaker 1: are expected to get rain, but it will not necessarily 145 00:08:43,360 --> 00:08:47,640 Speaker 1: rain over the entire forecast area. Um. But if you 146 00:08:47,679 --> 00:08:49,440 Speaker 1: hear twenty percent chance of rain, it does not mean 147 00:08:49,480 --> 00:08:52,200 Speaker 1: that of the forecast area is going to be dry 148 00:08:52,200 --> 00:08:53,720 Speaker 1: and the other twenty percent is gonna be wet, Nor 149 00:08:53,800 --> 00:08:56,320 Speaker 1: does it mean it will rain for twenty of the day. 150 00:08:56,559 --> 00:09:00,800 Speaker 1: In fact, that's part of the problem, uh, a prediction 151 00:09:00,880 --> 00:09:05,960 Speaker 1: of precipitation, the good old pop, the pop so pop 152 00:09:06,360 --> 00:09:10,080 Speaker 1: that that requires a time element to it as well. 153 00:09:10,240 --> 00:09:12,800 Speaker 1: It doesn't mean anything without a time element. So if 154 00:09:12,840 --> 00:09:15,320 Speaker 1: you say there's a twenty percent chance to rain, you 155 00:09:15,360 --> 00:09:17,640 Speaker 1: also need to have an element of time attached to 156 00:09:17,679 --> 00:09:20,600 Speaker 1: that to make it meaningful. So chance to rain over 157 00:09:20,640 --> 00:09:23,760 Speaker 1: the next six hours, then you know, all right. So 158 00:09:23,800 --> 00:09:26,360 Speaker 1: it's not saying that's gonna be chance to rain or 159 00:09:26,360 --> 00:09:29,400 Speaker 1: it's gonna not gonna rain of the day, just that 160 00:09:29,520 --> 00:09:31,640 Speaker 1: for the next six hours there's a twenty percent chance 161 00:09:31,679 --> 00:09:35,560 Speaker 1: it will be raining in the forecast area. So I 162 00:09:35,640 --> 00:09:38,719 Speaker 1: hope that demystifies some of it. Also, we can talk 163 00:09:38,760 --> 00:09:41,560 Speaker 1: about Radar, one of my favorite things to talk about. 164 00:09:42,040 --> 00:09:47,160 Speaker 1: Radar is awesome, favorite character on nash Yeah, well, I 165 00:09:47,200 --> 00:09:50,520 Speaker 1: think it's so adorable, right, It's hard not to feel 166 00:09:50,559 --> 00:09:54,080 Speaker 1: for him, and the fact that he can anticipate everything 167 00:09:54,240 --> 00:09:58,640 Speaker 1: his commanding officer wants, and he can even say what 168 00:09:58,679 --> 00:10:01,240 Speaker 1: the commanding officer is saying for the commanding officer has 169 00:10:01,280 --> 00:10:07,320 Speaker 1: finished a sentence. Obviously a key part of that operation 170 00:10:07,920 --> 00:10:11,520 Speaker 1: um so, but we're talking about actual radar, using radar 171 00:10:11,600 --> 00:10:15,880 Speaker 1: to detect weather. You've probably heard Doppler radar when looking 172 00:10:15,920 --> 00:10:17,400 Speaker 1: at a weather report, like, well, let's look at the 173 00:10:17,400 --> 00:10:21,120 Speaker 1: Doppler radar and see where the this, this precipitation is 174 00:10:21,160 --> 00:10:25,960 Speaker 1: moving in. Doppler radar for weather is different from Doppler 175 00:10:26,080 --> 00:10:29,360 Speaker 1: radar used by say, police officers who are trying to 176 00:10:29,400 --> 00:10:33,839 Speaker 1: detect if you are speeding. The Doppler radar that meteorologists 177 00:10:33,920 --> 00:10:38,600 Speaker 1: use actually shoots out radio waves in very short bursts 178 00:10:38,640 --> 00:10:43,360 Speaker 1: called pulses, and then the radar listens for any echoing 179 00:10:43,400 --> 00:10:46,640 Speaker 1: pulses coming back to the antenna, and the short pulses 180 00:10:46,679 --> 00:10:50,560 Speaker 1: indicate not just the presence of something out there, but 181 00:10:50,679 --> 00:10:54,079 Speaker 1: whether it's moving and which direction is it moving in? 182 00:10:55,280 --> 00:10:58,520 Speaker 1: Um is it moving toward the radar station or away 183 00:10:58,520 --> 00:11:01,520 Speaker 1: from it? If a Doppler radar receiver detect waves of 184 00:11:01,520 --> 00:11:05,839 Speaker 1: a higher frequency, the precipitation particles are moving towards the 185 00:11:05,960 --> 00:11:10,280 Speaker 1: radar exactly, and lower frequencies they're moving away. Yes, because 186 00:11:10,280 --> 00:11:13,960 Speaker 1: what's happening is it's similar to a Doppler shift. And 187 00:11:13,960 --> 00:11:17,640 Speaker 1: and anyone who's ever heard a vehicle with a siren 188 00:11:17,760 --> 00:11:21,280 Speaker 1: go past is familiar with this. It's a higher pitch 189 00:11:21,360 --> 00:11:24,040 Speaker 1: as the vehicles coming toward you and a lower pitch 190 00:11:24,120 --> 00:11:27,240 Speaker 1: as it's moving away. What's actually happening is, as the 191 00:11:27,360 --> 00:11:30,280 Speaker 1: vehicle is moving towards you, the sound waves it's emitting 192 00:11:30,280 --> 00:11:34,320 Speaker 1: are being compressed. Now that compression creates a higher frequency, 193 00:11:34,360 --> 00:11:38,520 Speaker 1: which means we detect a higher pitch. As the vehicle passes, 194 00:11:38,640 --> 00:11:43,360 Speaker 1: those frequencies are elongated, which means a lower pitch. Same 195 00:11:43,360 --> 00:11:45,280 Speaker 1: thing is true with the radar accept Instead of it 196 00:11:45,360 --> 00:11:47,599 Speaker 1: being a pitch, it's a radio frequency. If so, a 197 00:11:47,679 --> 00:11:50,760 Speaker 1: higher frequency will tell you, yeah, something's coming towards you, 198 00:11:50,960 --> 00:11:53,760 Speaker 1: and a lower frequency will tell you something's moving away 199 00:11:53,840 --> 00:11:57,800 Speaker 1: from you. And also the time between when the pulse 200 00:11:57,880 --> 00:12:00,400 Speaker 1: goes out and when you detect it tells you the 201 00:12:00,440 --> 00:12:06,360 Speaker 1: distance from the radar detection system and the precipitation. So 202 00:12:06,400 --> 00:12:10,559 Speaker 1: you could even say there's a storm system that's five 203 00:12:10,679 --> 00:12:14,920 Speaker 1: miles to the west, it's moving easterly at this speed 204 00:12:15,200 --> 00:12:18,240 Speaker 1: because you've detected it through a series of pulses UH. 205 00:12:18,280 --> 00:12:21,400 Speaker 1: If you have enough radar detections UH stations, you can 206 00:12:21,440 --> 00:12:25,760 Speaker 1: even describe the shape of the weather system and talk 207 00:12:25,840 --> 00:12:29,680 Speaker 1: about how some areas are more intense than others. You 208 00:12:29,679 --> 00:12:32,560 Speaker 1: can get all of that information from this approach, and 209 00:12:32,640 --> 00:12:38,240 Speaker 1: it's amazing how this thing works. First of all, it's 210 00:12:38,280 --> 00:12:43,040 Speaker 1: super high power. Uh. These radar stations are are they're 211 00:12:43,200 --> 00:12:45,720 Speaker 1: they're generating or they're transmitting I should say at four 212 00:12:45,800 --> 00:12:50,320 Speaker 1: hundred fifty thousand watts. So your typical microwave oven is 213 00:12:50,320 --> 00:12:53,040 Speaker 1: a thousand watts, So you need a four hundred and 214 00:12:53,080 --> 00:12:56,160 Speaker 1: fifty of those to equal one of these radar systems. 215 00:12:57,080 --> 00:13:01,120 Speaker 1: So four and fifty thou wats. And the pulse lasts 216 00:13:01,160 --> 00:13:05,440 Speaker 1: so short as to be unimaginable. It is point zero 217 00:13:05,679 --> 00:13:11,800 Speaker 1: zero zero zero zero seven seconds long, or one point 218 00:13:11,880 --> 00:13:14,560 Speaker 1: five seven times ten to the minus six seconds. So 219 00:13:14,600 --> 00:13:17,800 Speaker 1: if you hear the weather man on TV bragging about 220 00:13:17,840 --> 00:13:21,040 Speaker 1: Doppler radar, there's a reason. It's very impressive. Yeah. I 221 00:13:21,080 --> 00:13:25,640 Speaker 1: mean you're sitting at a a burst of radio signals 222 00:13:26,640 --> 00:13:29,560 Speaker 1: at such a fraction of a second that it is 223 00:13:30,240 --> 00:13:34,000 Speaker 1: again impossible to even imagine. Meanwhile, then it listens for 224 00:13:34,120 --> 00:13:37,040 Speaker 1: a longer period, and by longer I mean relatively longer. 225 00:13:37,080 --> 00:13:39,480 Speaker 1: It's still a fraction of a second. It's point zero 226 00:13:39,640 --> 00:13:45,280 Speaker 1: zero zero four three seconds. So it shoots out a 227 00:13:45,360 --> 00:13:48,720 Speaker 1: pulse listens for a little while, so I can detect 228 00:13:48,920 --> 00:13:51,880 Speaker 1: when the pulse comes back and what frequency it's at, 229 00:13:51,880 --> 00:13:53,600 Speaker 1: so it knows whether or not a body is moving 230 00:13:53,600 --> 00:13:56,040 Speaker 1: toward it or away from it, and then it does 231 00:13:56,080 --> 00:13:59,240 Speaker 1: it again. But that means with that amount of time 232 00:13:59,600 --> 00:14:03,480 Speaker 1: and the the comparatively large amount of time of listening, 233 00:14:04,160 --> 00:14:09,480 Speaker 1: for every hour of operation, the radio or the radar 234 00:14:09,520 --> 00:14:13,319 Speaker 1: antenna is only shooting out signals for seven seconds out 235 00:14:13,320 --> 00:14:16,600 Speaker 1: of an entire hour. That means for the fifty nine 236 00:14:16,600 --> 00:14:19,000 Speaker 1: minutes fifty three seconds, it is not sending out a signal. 237 00:14:19,040 --> 00:14:24,960 Speaker 1: It is listening. So for almost a full hour it's listening, 238 00:14:25,040 --> 00:14:28,160 Speaker 1: and only for seven seven seconds is actively shooting out 239 00:14:28,160 --> 00:14:32,280 Speaker 1: a signal. A conversation for someone who talks seven seconds, 240 00:14:32,320 --> 00:14:35,240 Speaker 1: it would I any conversation with me would last like 241 00:14:35,280 --> 00:14:39,720 Speaker 1: a decade before you could get a word in edgewise. Yeah, 242 00:14:39,880 --> 00:14:43,680 Speaker 1: it's uh, it's pretty amazing. And you usually would have 243 00:14:43,760 --> 00:14:48,080 Speaker 1: one of these stations shooting out these radio bursts at 244 00:14:48,240 --> 00:14:52,400 Speaker 1: different angles of elevation. These are called elevation slices, and 245 00:14:52,480 --> 00:14:56,360 Speaker 1: when you go through the entire range, you get what 246 00:14:56,480 --> 00:15:00,360 Speaker 1: was called volume coverage pattern or vc P. That's what 247 00:15:00,440 --> 00:15:04,360 Speaker 1: tells you what the activity is, not just at ground level, 248 00:15:04,640 --> 00:15:07,480 Speaker 1: but up in the atmosphere as well. Toppler radar can 249 00:15:07,520 --> 00:15:11,880 Speaker 1: also detect tornadoes. Yeah. Yeah, if if, if the particles 250 00:15:11,920 --> 00:15:16,800 Speaker 1: switch from moving toward and then away over a small distance, uh, 251 00:15:16,840 --> 00:15:19,840 Speaker 1: there's a good chance it could be a tornado. Yeah, 252 00:15:19,840 --> 00:15:22,080 Speaker 1: we know a lot about those. Here in the southeast too. 253 00:15:22,240 --> 00:15:24,200 Speaker 1: We get a lot of tornadoes, not as many as 254 00:15:24,240 --> 00:15:28,360 Speaker 1: places in the uh, you know, like in the Midwestern States, 255 00:15:28,400 --> 00:15:30,640 Speaker 1: things like you know, Oklahoma and stuff, and you guys 256 00:15:30,640 --> 00:15:33,920 Speaker 1: get tornadoes even more frequently than we do, but we 257 00:15:34,000 --> 00:15:38,320 Speaker 1: get them pretty seriously. Actually this year hasn't been too bad, 258 00:15:38,640 --> 00:15:41,160 Speaker 1: but there was one in November, yeah, which is weird 259 00:15:41,200 --> 00:15:44,080 Speaker 1: because typically we get them in the spring. Yes, um, 260 00:15:44,200 --> 00:15:47,080 Speaker 1: usually between March and June. That's kind of like our 261 00:15:47,800 --> 00:15:50,640 Speaker 1: let's play it easy. Yeah, but you don't waste that 262 00:15:50,720 --> 00:15:55,200 Speaker 1: on anybody, So no, I have. I have been through 263 00:15:55,320 --> 00:16:00,280 Speaker 1: a close call with a tornado while wearing Renaissance festival year. 264 00:16:01,000 --> 00:16:04,240 Speaker 1: That sounds surreal. That was my final day when I 265 00:16:04,280 --> 00:16:08,640 Speaker 1: did my first run at the festival in two thousand one. Yeah, 266 00:16:08,800 --> 00:16:11,360 Speaker 1: we had a really massive thunderstorm and at one point 267 00:16:11,400 --> 00:16:15,000 Speaker 1: someone said that there was a tornado a tornado watch, 268 00:16:15,160 --> 00:16:18,880 Speaker 1: but not a tornado warning watch, being that the conditions 269 00:16:18,920 --> 00:16:23,000 Speaker 1: for a tornado forming. Our present warning being that a 270 00:16:23,040 --> 00:16:26,840 Speaker 1: tornado has actually been spotted in the region, in case 271 00:16:26,880 --> 00:16:30,880 Speaker 1: you were wondering. So now let's talk about satellites and meteorology. 272 00:16:30,920 --> 00:16:33,200 Speaker 1: So the computers are really good for building out those 273 00:16:33,240 --> 00:16:36,320 Speaker 1: models and giving us predictions. The double radars really good 274 00:16:36,320 --> 00:16:40,920 Speaker 1: at tracking precipitation. What do weather satellites do, Uh, well, 275 00:16:40,960 --> 00:16:45,720 Speaker 1: they're they're keeping an eye on global weather patterns. But 276 00:16:45,920 --> 00:16:49,560 Speaker 1: there are two different types of weather satellites and they 277 00:16:49,600 --> 00:16:53,040 Speaker 1: do this in different ways. So one is the geostationary 278 00:16:53,080 --> 00:16:58,080 Speaker 1: weather satellite. Now, geo stationary weather satellites maintain their relative 279 00:16:58,080 --> 00:17:01,560 Speaker 1: position over a specific point on the Earth. They are 280 00:17:01,640 --> 00:17:06,320 Speaker 1: at a very high orbit over the equator, and they 281 00:17:06,720 --> 00:17:09,439 Speaker 1: they're always looking at the same thing because their orbit 282 00:17:09,560 --> 00:17:13,399 Speaker 1: is at the same speed as Earth's rotation um not 283 00:17:13,480 --> 00:17:15,880 Speaker 1: really the same speed, but relative speed because it's able 284 00:17:15,920 --> 00:17:18,960 Speaker 1: to stay in that same point over that part of 285 00:17:19,000 --> 00:17:21,720 Speaker 1: the Earth. And so they have to be on an 286 00:17:21,720 --> 00:17:23,920 Speaker 1: equatorial orbit and they have to be at a particular 287 00:17:23,960 --> 00:17:26,880 Speaker 1: altitude for this to work. It's great because it means 288 00:17:26,880 --> 00:17:29,080 Speaker 1: they can keep an eye on a specific region. It's 289 00:17:29,160 --> 00:17:33,240 Speaker 1: lousy because one they're really far away, so the instrumentation 290 00:17:33,280 --> 00:17:35,040 Speaker 1: you have to put on the satellites has to be 291 00:17:35,160 --> 00:17:39,560 Speaker 1: incredibly sophisticated in order to get good readings from that altitude. 292 00:17:40,160 --> 00:17:42,639 Speaker 1: Plus they have a limited view, right, They're always looking 293 00:17:42,640 --> 00:17:44,479 Speaker 1: at one part of the Earth. They can't see anything 294 00:17:44,480 --> 00:17:48,679 Speaker 1: else outside of that view. So the other type you 295 00:17:48,720 --> 00:17:51,920 Speaker 1: have are satellites they are in a polar orbit around 296 00:17:51,960 --> 00:17:55,359 Speaker 1: the Earth. Polar orbits are interesting. So if you think 297 00:17:55,400 --> 00:17:59,040 Speaker 1: of the Earth on its axis, the polar orbit is 298 00:17:59,040 --> 00:18:02,480 Speaker 1: going parallel to the axis of the Earth. It's going 299 00:18:02,520 --> 00:18:06,040 Speaker 1: perpendicular to the equator, so you would think of it 300 00:18:06,080 --> 00:18:08,960 Speaker 1: as going from north to south and then south to 301 00:18:09,080 --> 00:18:12,320 Speaker 1: north because once it crosses the south pole, you can 302 00:18:12,359 --> 00:18:14,840 Speaker 1: only go north at that point, that's the only direction 303 00:18:14,920 --> 00:18:17,600 Speaker 1: left to you. UM, and it goes in that circle, 304 00:18:17,640 --> 00:18:21,560 Speaker 1: which means these satellites get a full view of the 305 00:18:21,800 --> 00:18:25,080 Speaker 1: entire Earth because the Earth is rotating while it's going 306 00:18:25,080 --> 00:18:29,040 Speaker 1: in this orbit north south orbit UM. But it also 307 00:18:29,080 --> 00:18:31,920 Speaker 1: means that you only get a look at the same 308 00:18:31,960 --> 00:18:34,800 Speaker 1: part of the Earth twice in a twenty four hour period, 309 00:18:35,000 --> 00:18:38,040 Speaker 1: since once every twelve hours. UM. You can always put 310 00:18:38,040 --> 00:18:40,480 Speaker 1: another satellite up there, and that way you could get 311 00:18:40,800 --> 00:18:43,960 Speaker 1: you know, put it on the the opposite uh side 312 00:18:43,960 --> 00:18:46,040 Speaker 1: of the Earth where it's in the same orbit, and 313 00:18:46,080 --> 00:18:48,520 Speaker 1: then you get a look every six hours, just one 314 00:18:48,600 --> 00:18:51,359 Speaker 1: from one satellite and then six hours later one from 315 00:18:51,400 --> 00:18:54,879 Speaker 1: another satellite. But you also get to see everything on 316 00:18:55,000 --> 00:18:58,800 Speaker 1: the planet. So there's your trade off is that you 317 00:18:58,840 --> 00:19:01,600 Speaker 1: get you get a more conference of view, but you 318 00:19:01,640 --> 00:19:04,560 Speaker 1: don't get a consistent view of any one part of 319 00:19:04,560 --> 00:19:06,439 Speaker 1: the Earth with these kind of weather sellites. So a 320 00:19:06,480 --> 00:19:12,080 Speaker 1: lot of of weather services depend upon both yeah, and 321 00:19:12,119 --> 00:19:17,080 Speaker 1: typically they carry devices called radiometers, which usually have a 322 00:19:17,119 --> 00:19:20,840 Speaker 1: small telescope or some sort of antenna, a scanning device 323 00:19:20,920 --> 00:19:23,399 Speaker 1: of some sort, and one or more detectors that can 324 00:19:23,400 --> 00:19:26,960 Speaker 1: pick up visible, infrared or microwave radiation, and they use 325 00:19:27,040 --> 00:19:29,439 Speaker 1: that to take measurements of the Earth and send that 326 00:19:29,520 --> 00:19:33,399 Speaker 1: down to the planet's surface so that weather stations around 327 00:19:33,400 --> 00:19:36,399 Speaker 1: the world can take that data and crunch it and 328 00:19:36,440 --> 00:19:38,880 Speaker 1: figure out what the heck is going out on out 329 00:19:38,920 --> 00:19:41,919 Speaker 1: there when the frogs are raining from the sky. Apart 330 00:19:41,960 --> 00:19:46,720 Speaker 1: from amphibious assault UM, and all of those measurements are 331 00:19:46,720 --> 00:19:50,359 Speaker 1: actually done through little electrical voltages which then get digitized, 332 00:19:50,440 --> 00:19:53,960 Speaker 1: so transformed into digital information before transmitted down to Earth, 333 00:19:53,960 --> 00:19:56,840 Speaker 1: because you know, zapping electricity through space down to the 334 00:19:56,840 --> 00:20:00,520 Speaker 1: planet's not the most efficient way of getting information across UM. 335 00:20:00,560 --> 00:20:02,080 Speaker 1: And he certainly don't want to have a power chord 336 00:20:02,119 --> 00:20:04,080 Speaker 1: stretch all the way there. That would just be such 337 00:20:04,119 --> 00:20:08,400 Speaker 1: a pain. Yeahs with aircraft patterns, and if you don't 338 00:20:08,400 --> 00:20:11,360 Speaker 1: have geostationary orbit, it gets wrapped up around the planet 339 00:20:11,359 --> 00:20:16,320 Speaker 1: pretty quickly. Yeah, So how do meteorologists do things like 340 00:20:16,400 --> 00:20:19,679 Speaker 1: predict temperature, like predict highs and lows and that kind 341 00:20:19,720 --> 00:20:22,479 Speaker 1: of stuff. For this I went to a website that 342 00:20:22,560 --> 00:20:27,800 Speaker 1: was written by a meteorologist named Jeff Haby And boy, howdy, 343 00:20:27,920 --> 00:20:30,840 Speaker 1: did it suddenly dawn on me how much more complicated 344 00:20:30,880 --> 00:20:35,080 Speaker 1: this was than I had even anticipated. But according to Haby, uh, 345 00:20:35,240 --> 00:20:41,400 Speaker 1: he looks at everything from um thermal advection, wind speed, 346 00:20:41,520 --> 00:20:45,120 Speaker 1: cloud cover, de point, and the number of daylight hours 347 00:20:45,119 --> 00:20:47,520 Speaker 1: expected for that region in order to come up with 348 00:20:47,920 --> 00:20:49,760 Speaker 1: the prediction for the high temperature of the day in 349 00:20:49,760 --> 00:20:52,280 Speaker 1: the low temperature of the day. So, what the heck 350 00:20:52,320 --> 00:20:56,200 Speaker 1: does all that mean? So thermal infection, what is that 351 00:20:56,200 --> 00:20:59,520 Speaker 1: that refers to the transportation of heat by a moving fluid. 352 00:21:00,160 --> 00:21:04,280 Speaker 1: So typically the stuff that affects the infection include the 353 00:21:04,320 --> 00:21:07,800 Speaker 1: strength of wind, So how hard is the wind blowing 354 00:21:07,800 --> 00:21:11,639 Speaker 1: in that region? The temperature gradient between the warmer and 355 00:21:11,680 --> 00:21:15,480 Speaker 1: colder areas, So if one area is warmer than the other, 356 00:21:15,680 --> 00:21:18,199 Speaker 1: is it warmer by like a couple of degrees or 357 00:21:18,280 --> 00:21:20,960 Speaker 1: is it more significant than that, is, like ten degrees fahrenheit, 358 00:21:21,160 --> 00:21:25,520 Speaker 1: That would be a much larger gradient, right, And the 359 00:21:25,600 --> 00:21:28,880 Speaker 1: angle between the wind direction and the temperature gradient, If 360 00:21:28,920 --> 00:21:31,840 Speaker 1: that angle is more narrow, you're going to see a 361 00:21:31,840 --> 00:21:36,399 Speaker 1: greater thermal infection, meaning you'll see more temperature changes moving 362 00:21:36,440 --> 00:21:41,280 Speaker 1: into an area from a different region. So that's just infection, 363 00:21:42,520 --> 00:21:45,200 Speaker 1: that's all I play. Uh, the other one that you 364 00:21:45,440 --> 00:21:48,120 Speaker 1: other term you might be a little confused by. I mean, 365 00:21:48,160 --> 00:21:51,200 Speaker 1: wind speed makes sense, cloud cover makes sense, daylight hours, 366 00:21:51,240 --> 00:21:52,960 Speaker 1: All of that makes sense. But what about do point? 367 00:21:53,880 --> 00:21:56,119 Speaker 1: That refers to the temperature at which air must be 368 00:21:56,240 --> 00:22:00,480 Speaker 1: cooled at constant barometric pressure for water vapor to and dense. 369 00:22:01,520 --> 00:22:04,400 Speaker 1: So that temperature again has depended upon things like the 370 00:22:04,440 --> 00:22:08,560 Speaker 1: actual air pressure right um, and so the dow point 371 00:22:08,640 --> 00:22:12,640 Speaker 1: changes based upon those other factors as well. So all 372 00:22:12,680 --> 00:22:15,400 Speaker 1: those have to be taken into account before a meteorologist 373 00:22:15,440 --> 00:22:17,439 Speaker 1: can forecast what the temperature is going to be the 374 00:22:17,480 --> 00:22:20,320 Speaker 1: next day. This is why we're so happy to have 375 00:22:20,400 --> 00:22:24,080 Speaker 1: those complicated computers now, because if you were to keep 376 00:22:24,080 --> 00:22:27,800 Speaker 1: track of this yourself, you probably go bonkers. And then 377 00:22:27,800 --> 00:22:31,080 Speaker 1: we have like the idea of the the probability of precipitation, 378 00:22:31,080 --> 00:22:33,640 Speaker 1: which we kind of talked about already, but generally speaking, 379 00:22:33,720 --> 00:22:39,680 Speaker 1: there's some weather services that will only predict rainfall if 380 00:22:39,800 --> 00:22:42,480 Speaker 1: it's expected to be over a certain amount, like point 381 00:22:42,520 --> 00:22:44,960 Speaker 1: to five millimeters. If it's going to be less than 382 00:22:45,000 --> 00:22:49,520 Speaker 1: point to five millimeters, it doesn't even register as rainfall 383 00:22:49,600 --> 00:22:52,439 Speaker 1: in predictions. You would say there's a zero percent chance 384 00:22:52,600 --> 00:22:55,600 Speaker 1: or or whatever, if that's what you think is going 385 00:22:55,640 --> 00:22:58,800 Speaker 1: to be the the accumulation. Some other ones are like, 386 00:22:58,840 --> 00:23:03,480 Speaker 1: no any rain at all counts. If it's one drop 387 00:23:03,520 --> 00:23:06,480 Speaker 1: of rain it rained in that in that region. Um 388 00:23:06,760 --> 00:23:10,800 Speaker 1: so it really depends upon the service. But uh that 389 00:23:10,920 --> 00:23:13,360 Speaker 1: we already talked about the percentages and what those means, 390 00:23:13,400 --> 00:23:16,200 Speaker 1: so hopefully that clears things up. And again that kind 391 00:23:16,240 --> 00:23:20,240 Speaker 1: of goes into that concept of model output statistics, where 392 00:23:20,320 --> 00:23:25,400 Speaker 1: you you correct for your predictions based upon past conditions 393 00:23:25,400 --> 00:23:29,159 Speaker 1: for a particular region. All of this comes together to 394 00:23:29,320 --> 00:23:34,480 Speaker 1: create the weather report that you see. So I think 395 00:23:34,560 --> 00:23:38,879 Speaker 1: I think the real takeaway here is it's incredible the 396 00:23:38,920 --> 00:23:45,280 Speaker 1: amount of of schooling and expertise a meteorologist has to 397 00:23:45,320 --> 00:23:47,400 Speaker 1: have in order to do his or her job properly, 398 00:23:48,240 --> 00:23:50,600 Speaker 1: right like, because you see how complicated this is, and 399 00:23:50,600 --> 00:23:53,720 Speaker 1: you start to have an appreciation of all right, and 400 00:23:53,800 --> 00:23:56,159 Speaker 1: they said it was a six chance of rain, I 401 00:23:56,200 --> 00:24:00,000 Speaker 1: brought my umbrella and never rained. Now you realize, well, 402 00:24:00,000 --> 00:24:03,720 Speaker 1: when you're talking about the system this complicated and this unpredictable, 403 00:24:04,280 --> 00:24:08,240 Speaker 1: something that can change dramatically just because something you did 404 00:24:08,240 --> 00:24:13,320 Speaker 1: not anticipate happened, you start to appreciate more the challenge 405 00:24:13,400 --> 00:24:17,000 Speaker 1: that they have to do their jobs properly. So give 406 00:24:17,040 --> 00:24:21,880 Speaker 1: your meteorologist a hug and say thank you because this 407 00:24:21,920 --> 00:24:26,680 Speaker 1: stuff is hard. Yeah, and my you know, if weather 408 00:24:26,880 --> 00:24:29,280 Speaker 1: is the state of the atmosphere from day to day, 409 00:24:29,280 --> 00:24:33,600 Speaker 1: and the atmosphere is super complex, you know it's it's 410 00:24:33,680 --> 00:24:38,160 Speaker 1: my favorite analogy from our our article on our website, 411 00:24:38,359 --> 00:24:42,119 Speaker 1: UM about meteorology is that the atmosphere is like a 412 00:24:42,320 --> 00:24:45,640 Speaker 1: soup with too many cooks. Yeah, yeah, there's a lot 413 00:24:45,720 --> 00:24:51,119 Speaker 1: at play, and so many different variables are working behind 414 00:24:51,160 --> 00:24:55,160 Speaker 1: the scenes to give you a simple, a simple weather 415 00:24:55,240 --> 00:24:58,720 Speaker 1: forecast that is easily digestible. Yeah. This is also why 416 00:24:58,880 --> 00:25:04,960 Speaker 1: when you hear about uh uh supercomputers running weather simulations, 417 00:25:05,040 --> 00:25:07,520 Speaker 1: that's why you need a supercomputer because of this. Too 418 00:25:07,560 --> 00:25:10,080 Speaker 1: many cooks. I mean, it takes a lot to make 419 00:25:10,119 --> 00:25:13,840 Speaker 1: us stew. I hope, I hope at least some of 420 00:25:13,880 --> 00:25:17,159 Speaker 1: you are singing along now. Um. But yeah, it's it 421 00:25:17,760 --> 00:25:20,639 Speaker 1: really does explain why you need that massive amount of 422 00:25:20,680 --> 00:25:22,760 Speaker 1: computing power just to do something that you would think 423 00:25:22,840 --> 00:25:25,640 Speaker 1: would be fairly simple. You know, you're thinking like, oh, 424 00:25:25,680 --> 00:25:28,639 Speaker 1: it's like six or seven factors, and you realize, oh, wait, no, 425 00:25:28,760 --> 00:25:31,600 Speaker 1: there are these other things that also have an effect, 426 00:25:31,640 --> 00:25:35,560 Speaker 1: and in some cases a measurable and meaningful effect, not 427 00:25:35,680 --> 00:25:40,680 Speaker 1: just a potential effect. Um. And it really does drive 428 00:25:40,720 --> 00:25:45,920 Speaker 1: home that it's amazing we can have relatively accurate weather 429 00:25:45,960 --> 00:25:50,680 Speaker 1: predictions in the first place. Um. And also it makes 430 00:25:50,720 --> 00:25:53,960 Speaker 1: me kind of sad that I no longer I used 431 00:25:53,960 --> 00:25:57,240 Speaker 1: to be on television with a local weather guy. He 432 00:25:57,680 --> 00:26:00,919 Speaker 1: did a show at five thirty in the morning, and 433 00:26:00,960 --> 00:26:02,800 Speaker 1: I would show up on television and do a Gadget 434 00:26:02,840 --> 00:26:05,440 Speaker 1: segment with him, and it was great. He was very nice, 435 00:26:05,720 --> 00:26:10,800 Speaker 1: and his ability to break down complicated concepts of weather 436 00:26:11,400 --> 00:26:14,320 Speaker 1: in a way that was helpful to people so that 437 00:26:14,400 --> 00:26:19,000 Speaker 1: they could plan their day was really amazing, especially when 438 00:26:19,000 --> 00:26:21,200 Speaker 1: you you start really thinking about all the things that 439 00:26:21,280 --> 00:26:25,439 Speaker 1: come into play to make that you know possible. So 440 00:26:25,840 --> 00:26:28,840 Speaker 1: our hats are off to you meteorologists out there, keep 441 00:26:28,880 --> 00:26:32,120 Speaker 1: doing the good work. I look forward to learning more 442 00:26:32,160 --> 00:26:35,040 Speaker 1: about you know, when we when we figure out even 443 00:26:35,080 --> 00:26:38,479 Speaker 1: more details about the complexities of of the atmosphere and 444 00:26:38,520 --> 00:26:42,159 Speaker 1: perhaps are able to make even more accurate models. Um, 445 00:26:42,320 --> 00:26:45,320 Speaker 1: maybe we will one day reach the back to the 446 00:26:45,359 --> 00:26:49,719 Speaker 1: future to level where minute by minute it tells you 447 00:26:49,760 --> 00:26:52,119 Speaker 1: what the the weather is going to be. Of course, 448 00:26:52,160 --> 00:26:54,400 Speaker 1: then I think they were actually suggesting that we would 449 00:26:54,400 --> 00:27:01,359 Speaker 1: have weather control, which is a whole other thing handle worms. Yeah, 450 00:27:01,760 --> 00:27:04,120 Speaker 1: I talked to Dylan that I said I had thought 451 00:27:04,160 --> 00:27:08,880 Speaker 1: about doing a little discussion about weather control, but obviously 452 00:27:08,920 --> 00:27:11,320 Speaker 1: we've gone pretty long already. So what I will say 453 00:27:11,320 --> 00:27:15,359 Speaker 1: about weather control is weather systems represent a huge amount 454 00:27:15,400 --> 00:27:20,000 Speaker 1: of energy, and in order for us to affect or 455 00:27:20,080 --> 00:27:24,760 Speaker 1: manufacture weather events on a large scale, we would have 456 00:27:24,840 --> 00:27:27,800 Speaker 1: to be able to generate that amount of energy and 457 00:27:27,840 --> 00:27:30,280 Speaker 1: pour it into the atmosphere in a way that actually 458 00:27:30,920 --> 00:27:33,400 Speaker 1: does what we wanted to do. And we're so far 459 00:27:33,440 --> 00:27:37,760 Speaker 1: away from any of those things that it's absolutely unrealistic 460 00:27:37,800 --> 00:27:39,560 Speaker 1: to think of weather control, even if you are a 461 00:27:39,640 --> 00:27:43,800 Speaker 1: Cobra commander going you know, going back to the beginning 462 00:27:43,800 --> 00:27:46,000 Speaker 1: of this episode with that listener request for this and 463 00:27:46,119 --> 00:27:49,600 Speaker 1: that their father said that in the sixties that they 464 00:27:49,640 --> 00:27:52,159 Speaker 1: felt like the weather report was just a joke. And 465 00:27:52,200 --> 00:27:55,879 Speaker 1: you fast forward to now and how you know, maybe 466 00:27:56,040 --> 00:27:58,320 Speaker 1: some days you grab an umbrella and you don't need it, 467 00:27:58,400 --> 00:28:01,680 Speaker 1: but that it's you know, you can track major weather 468 00:28:02,320 --> 00:28:05,720 Speaker 1: patterns and incoming storms that we have a pretty good 469 00:28:06,480 --> 00:28:10,760 Speaker 1: ability to track hurricanes and uh, and in like flash 470 00:28:10,760 --> 00:28:13,800 Speaker 1: floods and things like that. I can't imagine where I'll 471 00:28:13,840 --> 00:28:16,520 Speaker 1: be in forty or fifty years. Yeah, the fact that 472 00:28:16,560 --> 00:28:21,080 Speaker 1: we can get a at least heads up on stuff 473 00:28:21,119 --> 00:28:25,320 Speaker 1: before it becomes critical to us is really important. Uh. 474 00:28:25,359 --> 00:28:28,800 Speaker 1: I mean, Dylan, you probably remember it wasn't that long 475 00:28:28,800 --> 00:28:32,560 Speaker 1: ago here in Atlanta when we had the snow apocalypse, 476 00:28:33,520 --> 00:28:36,200 Speaker 1: and because it was one of those things where the 477 00:28:36,240 --> 00:28:39,160 Speaker 1: initial weather reports suggested that the weather was going to 478 00:28:39,200 --> 00:28:43,360 Speaker 1: miss the city and it didn't. That's an indication that, yeah, 479 00:28:43,400 --> 00:28:48,000 Speaker 1: our predictions are not accurate, they're not infallible. Uh. And 480 00:28:48,160 --> 00:28:50,920 Speaker 1: it also taught us a valuable lesson, which is that 481 00:28:51,600 --> 00:28:55,440 Speaker 1: even when you feel like there's a pretty good chance 482 00:28:55,480 --> 00:28:57,800 Speaker 1: that you're gonna miss out on that bad weather, it 483 00:28:57,880 --> 00:29:03,080 Speaker 1: doesn't hurt to prepare because the the alternative is to 484 00:29:03,120 --> 00:29:06,080 Speaker 1: spend eight hours on two eight five, even if it 485 00:29:06,200 --> 00:29:09,760 Speaker 1: is an inch of snow. Because it's Atlanta, and because 486 00:29:09,800 --> 00:29:11,760 Speaker 1: we don't have a system in place to deal with 487 00:29:11,800 --> 00:29:13,680 Speaker 1: an inch of snow, and we got a lot of hills. 488 00:29:14,160 --> 00:29:16,920 Speaker 1: None of us have snow tires, why would you, And 489 00:29:17,040 --> 00:29:19,160 Speaker 1: you let out the private in the public sector at 490 00:29:19,160 --> 00:29:23,000 Speaker 1: the same time. Yeah, that was particularly bad. I remember 491 00:29:23,080 --> 00:29:25,880 Speaker 1: I actually stayed here, not here, but in our our 492 00:29:26,040 --> 00:29:30,160 Speaker 1: old office location. I stayed there pretty much through the 493 00:29:30,160 --> 00:29:32,240 Speaker 1: full day because it was like, I'm gonna take Martha, 494 00:29:32,360 --> 00:29:33,960 Speaker 1: I'm gonna take the train. It's not gonna be a 495 00:29:34,000 --> 00:29:36,480 Speaker 1: big deal. It took me three hours to get home, 496 00:29:37,560 --> 00:29:39,960 Speaker 1: usually would take me forty five minutes, and I was 497 00:29:40,000 --> 00:29:42,320 Speaker 1: getting I got online, got ready to complain, and then 498 00:29:42,360 --> 00:29:44,480 Speaker 1: I started reading messages for my friends who were stuck 499 00:29:44,480 --> 00:29:47,160 Speaker 1: in their cars and had been for six hours. I thought, Okay, 500 00:29:47,200 --> 00:29:50,680 Speaker 1: we're gonna back away from the community slowly. Kids stuck 501 00:29:50,720 --> 00:29:54,840 Speaker 1: in school buses overnight. Yeah, pretty rough stuff. So yeah, 502 00:29:55,000 --> 00:29:57,800 Speaker 1: we're we're not We're not perfect, but it is getting 503 00:29:57,840 --> 00:30:00,320 Speaker 1: better and it is pretty impressive to see the amount 504 00:30:00,360 --> 00:30:03,480 Speaker 1: of information you can get. I love, I mean I love. 505 00:30:03,520 --> 00:30:07,120 Speaker 1: I find watching Doppler radar readouts to be fascinating, Like 506 00:30:07,160 --> 00:30:09,680 Speaker 1: I could have that open on my desk all day 507 00:30:09,720 --> 00:30:11,800 Speaker 1: if I if I didn't have other stuff I need 508 00:30:11,840 --> 00:30:16,040 Speaker 1: to do. So Dree, thank you so much for sending 509 00:30:16,040 --> 00:30:18,200 Speaker 1: that request in. There's a lot of fun to kind 510 00:30:18,200 --> 00:30:20,120 Speaker 1: of read up on it and to go over at Dylan, 511 00:30:20,160 --> 00:30:22,000 Speaker 1: thank you for joining me in the in the studio. 512 00:30:22,120 --> 00:30:24,960 Speaker 1: Thanks for having greatly appreciate it. If you guys have 513 00:30:25,040 --> 00:30:27,920 Speaker 1: suggestions for future episodes of tech Stuff, write me let 514 00:30:27,960 --> 00:30:30,800 Speaker 1: me know what you think. I am actually reading those 515 00:30:30,800 --> 00:30:34,840 Speaker 1: emails for a while, Dylan. Are you familiar with outlooks 516 00:30:34,880 --> 00:30:38,480 Speaker 1: clutter folder? I am. I deleted mine. You know what, 517 00:30:38,800 --> 00:30:41,720 Speaker 1: that was a wise decision, Dylan, because it turned out 518 00:30:41,760 --> 00:30:43,960 Speaker 1: that all of my listener mail for the last five 519 00:30:43,960 --> 00:30:46,960 Speaker 1: months has been going to my text stuff in box 520 00:30:47,040 --> 00:30:51,320 Speaker 1: clutter folder. So I discovered that last week and I 521 00:30:51,360 --> 00:30:53,719 Speaker 1: have since been going back and trying to answer as 522 00:30:53,760 --> 00:30:56,880 Speaker 1: many of those as possible. I started foolishly with the 523 00:30:56,920 --> 00:30:59,040 Speaker 1: most recent and started working my way back. So if 524 00:30:59,080 --> 00:31:00,680 Speaker 1: you wrote me a few months ago and you're wondering 525 00:31:00,720 --> 00:31:03,200 Speaker 1: why I never bothered to respond, it's because I haven't 526 00:31:03,200 --> 00:31:05,160 Speaker 1: gotten to your email yet. But I'm still working my 527 00:31:05,240 --> 00:31:07,400 Speaker 1: way through. But if you have a suggestion for a 528 00:31:07,400 --> 00:31:10,000 Speaker 1: future episode, right me, I've been keeping track of those, 529 00:31:10,040 --> 00:31:12,920 Speaker 1: will be covering them shortly. And also you can get 530 00:31:12,960 --> 00:31:15,680 Speaker 1: in touch with me on Facebook or Twitter to handle 531 00:31:15,680 --> 00:31:18,600 Speaker 1: at both of those. Is Tech Stuff hs W and 532 00:31:18,640 --> 00:31:26,400 Speaker 1: I will talk to you again really soon for more 533 00:31:26,440 --> 00:31:28,719 Speaker 1: on this and bousands of other topics. Is it how 534 00:31:28,760 --> 00:31:39,560 Speaker 1: stuff works dot com