1 00:00:04,360 --> 00:00:12,360 Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there, 2 00:00:12,440 --> 00:00:16,160 Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland. 3 00:00:16,200 --> 00:00:19,599 Speaker 1: I'm an executive producer with iHeartRadio and how the tech 4 00:00:19,640 --> 00:00:22,880 Speaker 1: are you. It's Friday, so it's time for another classic episode. 5 00:00:23,120 --> 00:00:26,440 Speaker 1: This is the second part of a two parter called 6 00:00:26,720 --> 00:00:30,360 Speaker 1: weather Tech Part two. So if you didn't listen to 7 00:00:30,480 --> 00:00:33,280 Speaker 1: last week's classic episode of weather Tech Part one, I 8 00:00:33,320 --> 00:00:35,400 Speaker 1: recommend you do that. That would have been last Friday. 9 00:00:36,080 --> 00:00:41,920 Speaker 1: This episode originally published on April twenty first, two sixteen. Enjoy. 10 00:00:42,960 --> 00:00:46,560 Speaker 1: We are now going to join the podcast already in progress. 11 00:00:48,440 --> 00:00:51,360 Speaker 1: We were able to gather a lot of information once 12 00:00:51,440 --> 00:00:55,000 Speaker 1: we had those basic tools available to us. But what 13 00:00:55,280 --> 00:01:00,560 Speaker 1: really pushed meteorology forward is when we could stop relying 14 00:01:00,640 --> 00:01:03,240 Speaker 1: upon the data that we can gather here on the 15 00:01:03,280 --> 00:01:08,320 Speaker 1: ground and supplement that with information from the atmosphere itself. 16 00:01:08,880 --> 00:01:12,679 Speaker 1: And that brings us to weather balloons. Weather balloons more 17 00:01:12,720 --> 00:01:17,640 Speaker 1: than just fodder for your roswell conspiracy right right, Swamp 18 00:01:17,680 --> 00:01:21,319 Speaker 1: gas and weather balloons and weather balloons do more than 19 00:01:21,400 --> 00:01:25,039 Speaker 1: just act as a subplot in an X Files episode. Right, Yes, 20 00:01:25,080 --> 00:01:29,560 Speaker 1: They're very important. Yeah, so they carry instrumentation that collects 21 00:01:29,640 --> 00:01:35,120 Speaker 1: data about atmospheric conditions and weather balloons have been around 22 00:01:35,120 --> 00:01:39,280 Speaker 1: for a long time, but more recently they typically carry 23 00:01:39,319 --> 00:01:43,160 Speaker 1: instruments called radiosond, which is a battery powered device that 24 00:01:43,160 --> 00:01:47,840 Speaker 1: can measure altitude, atmospheric pressure, temperature, humidity, wind speed. Sometimes 25 00:01:47,840 --> 00:01:51,280 Speaker 1: there's a GPS element to it, so it can so 26 00:01:51,400 --> 00:01:53,440 Speaker 1: people on the ground can track where the weather balloon is. 27 00:01:53,920 --> 00:01:57,560 Speaker 1: Normally the you tether these devices. You don't just release 28 00:01:57,560 --> 00:02:01,160 Speaker 1: a weather balloon and say sea, but sometimes you know 29 00:02:01,200 --> 00:02:04,200 Speaker 1: you need to have that GPS element there too. And 30 00:02:04,760 --> 00:02:07,520 Speaker 1: getting this information from the atmosphere is really important because 31 00:02:07,520 --> 00:02:10,280 Speaker 1: it can tell you about how conditions may soon change 32 00:02:10,440 --> 00:02:16,000 Speaker 1: on the ground. It's pretty interesting actually to ever if 33 00:02:16,040 --> 00:02:18,480 Speaker 1: you've ever had a chance to look at some of 34 00:02:18,520 --> 00:02:23,000 Speaker 1: the data pulled from these, because you see how different 35 00:02:23,040 --> 00:02:26,600 Speaker 1: conditions in the atmosphere are compared to what we experience here, 36 00:02:27,600 --> 00:02:34,200 Speaker 1: including some pretty intense winds at higher altitudes. So we've 37 00:02:34,240 --> 00:02:38,359 Speaker 1: got all this information being collected, and before we get 38 00:02:38,400 --> 00:02:42,040 Speaker 1: into space, because that'll be the next step outward, I 39 00:02:42,080 --> 00:02:43,920 Speaker 1: wanted to talk a little bit about what we do 40 00:02:44,040 --> 00:02:46,480 Speaker 1: with all that data. One of the things we do 41 00:02:46,600 --> 00:02:50,640 Speaker 1: is we create databases that have all this information. So 42 00:02:50,680 --> 00:02:54,000 Speaker 1: that let's say that we have a day with pretty 43 00:02:54,080 --> 00:02:58,000 Speaker 1: nice weather, we collect all the information about that. What 44 00:02:58,120 --> 00:03:01,000 Speaker 1: was the atmospheric pressure, what is the temperature, how much 45 00:03:01,080 --> 00:03:03,640 Speaker 1: humidity was in the air, what was the wind speed, 46 00:03:04,040 --> 00:03:07,640 Speaker 1: were there any higher low pressure systems nearby? Were the 47 00:03:07,760 --> 00:03:09,639 Speaker 1: what kind of front had just moved through? All this 48 00:03:09,720 --> 00:03:12,160 Speaker 1: sort of information, we feed it all into a database. 49 00:03:13,200 --> 00:03:15,640 Speaker 1: Collecting that over and over and over again allows us 50 00:03:15,680 --> 00:03:20,919 Speaker 1: to build a better virtual understanding of how weather works, right, 51 00:03:22,680 --> 00:03:25,360 Speaker 1: and we can supplement that with more information as we 52 00:03:25,440 --> 00:03:29,360 Speaker 1: learn more about the weather. Then we would end up 53 00:03:30,440 --> 00:03:32,880 Speaker 1: using that to help us make some predictions about how 54 00:03:32,919 --> 00:03:36,480 Speaker 1: weather might be in the future. And to do that, 55 00:03:36,520 --> 00:03:41,560 Speaker 1: really we use computer models. Typically we would build what 56 00:03:41,800 --> 00:03:48,720 Speaker 1: was called a numerical weather prediction model, the NWP. So 57 00:03:49,240 --> 00:03:52,160 Speaker 1: this is really a model that's made up of a 58 00:03:52,200 --> 00:03:55,320 Speaker 1: bunch of different calculations that take all of the different 59 00:03:55,400 --> 00:04:00,040 Speaker 1: variables into account and tell you, based upon all the 60 00:04:00,080 --> 00:04:03,440 Speaker 1: variables available to us, here's what it looks like the 61 00:04:03,480 --> 00:04:08,040 Speaker 1: weather is going to be like in X amount of time. Right, So, 62 00:04:08,120 --> 00:04:10,560 Speaker 1: whenever we're talking about forecasts, obviously we have to worry 63 00:04:10,600 --> 00:04:13,360 Speaker 1: about what are the current conditions and how far out 64 00:04:13,400 --> 00:04:17,599 Speaker 1: are we trying to predict the weather? And on TV 65 00:04:17,760 --> 00:04:20,640 Speaker 1: you might see five or seven or even these days 66 00:04:20,680 --> 00:04:23,960 Speaker 1: sometimes ten. Yeah. Like if you go to weather dot com, 67 00:04:24,000 --> 00:04:26,120 Speaker 1: they have a ten day forecast, which I always think 68 00:04:26,240 --> 00:04:29,840 Speaker 1: is hilarious. And the reason I think it's hilarious is 69 00:04:30,480 --> 00:04:34,120 Speaker 1: here's how those predictions work. You take all the information 70 00:04:34,160 --> 00:04:37,120 Speaker 1: available to you, you run it through your computer model, 71 00:04:37,320 --> 00:04:41,600 Speaker 1: which factors in these different variables and gives you sort 72 00:04:41,640 --> 00:04:45,520 Speaker 1: of a percentage of probability of what your weather is 73 00:04:45,520 --> 00:04:48,320 Speaker 1: going to be like in the next let's say hour. 74 00:04:50,040 --> 00:04:52,200 Speaker 1: What if you want to look two hours ahead, Well, 75 00:04:52,240 --> 00:04:54,400 Speaker 1: then what they do is they take the prediction that 76 00:04:54,440 --> 00:05:01,240 Speaker 1: they made for an hour from now and extrapolate from there, saying, well, 77 00:05:01,839 --> 00:05:04,240 Speaker 1: if in fact the weather is what we think it's 78 00:05:04,279 --> 00:05:07,000 Speaker 1: going to be like in an hour, this is what 79 00:05:07,120 --> 00:05:10,599 Speaker 1: should look like two hours from now. Well, if you 80 00:05:10,600 --> 00:05:12,560 Speaker 1: want to look at three hours from now, well let's 81 00:05:12,600 --> 00:05:14,800 Speaker 1: take what the results were for two hours from now 82 00:05:14,800 --> 00:05:17,000 Speaker 1: and extrapolate again, and that's what we think it's going 83 00:05:17,040 --> 00:05:19,880 Speaker 1: to be three hours from now. Extend that out to 84 00:05:19,960 --> 00:05:23,039 Speaker 1: ten days. Yeah, and it's going to become less and 85 00:05:23,520 --> 00:05:29,400 Speaker 1: less reliable, Yes, because you're basing your predictions upon the 86 00:05:29,440 --> 00:05:33,600 Speaker 1: results of a previous set of predictions, not upon a 87 00:05:33,640 --> 00:05:39,799 Speaker 1: previous set of actual conditions. Right, So when you're tracing 88 00:05:39,800 --> 00:05:41,960 Speaker 1: it all the way back and your starting point is 89 00:05:42,160 --> 00:05:46,800 Speaker 1: right now, well, clearly, the further out we look, the 90 00:05:46,839 --> 00:05:49,159 Speaker 1: more unreliable the information is going to be, the more 91 00:05:49,279 --> 00:05:54,120 Speaker 1: likely some other variable that we have not anticipated will 92 00:05:54,160 --> 00:05:58,120 Speaker 1: play a larger role or a smaller role, and that 93 00:05:58,279 --> 00:06:02,240 Speaker 1: is going to affect the overall outcome of the of 94 00:06:02,279 --> 00:06:05,000 Speaker 1: what will actually happen. The forecast is the same, but 95 00:06:05,040 --> 00:06:08,600 Speaker 1: the actual thing we experience might be very different, which 96 00:06:08,640 --> 00:06:11,760 Speaker 1: is why if you're planning a picnic and you've got 97 00:06:11,760 --> 00:06:14,280 Speaker 1: ten days out from it and you're looking at the 98 00:06:14,279 --> 00:06:16,800 Speaker 1: weather and it says it's going to be absolutely perfect, 99 00:06:18,120 --> 00:06:21,679 Speaker 1: don't bet the house on it. Yes, that's not necessarily true. 100 00:06:21,800 --> 00:06:25,080 Speaker 1: Not to discredit numerical weather predictions, because a lot of 101 00:06:25,120 --> 00:06:29,880 Speaker 1: science and time goes into it. Yeah, but it's still 102 00:06:30,240 --> 00:06:33,520 Speaker 1: you know, It's the way that I see modern meteorology 103 00:06:33,640 --> 00:06:37,919 Speaker 1: is that over time we have continually built upon basically 104 00:06:37,960 --> 00:06:41,440 Speaker 1: what our ancestors did and just gotten it's gotten more scientific, 105 00:06:41,520 --> 00:06:46,040 Speaker 1: we've gotten better instruments, but it's still looking for patterns. Yeah, 106 00:06:46,080 --> 00:06:50,120 Speaker 1: exactly right. So you might look at the patterns of 107 00:06:50,839 --> 00:06:54,520 Speaker 1: when all of these conditions are in play. Out of 108 00:06:54,520 --> 00:06:58,400 Speaker 1: the last one hundred times that that happened, this is 109 00:06:58,560 --> 00:07:01,480 Speaker 1: how the weather turned out, and we're going to break 110 00:07:01,480 --> 00:07:04,960 Speaker 1: it down. So maybe eighty days out of those one 111 00:07:05,040 --> 00:07:08,120 Speaker 1: hundred days where the conditions were similar to today's, it 112 00:07:08,160 --> 00:07:11,760 Speaker 1: didn't rain at all. It was perfectly sunny, so eighty 113 00:07:11,760 --> 00:07:14,840 Speaker 1: out of one hundred it was lovely. The other twenty 114 00:07:14,920 --> 00:07:19,160 Speaker 1: days it rained and it was just steady rain, and 115 00:07:19,640 --> 00:07:21,840 Speaker 1: that's all there is to it. This is a super 116 00:07:21,960 --> 00:07:26,000 Speaker 1: oversimplified version of what could happen. This is what would 117 00:07:26,080 --> 00:07:28,280 Speaker 1: lead you to say there's a twenty percent chance of rain, 118 00:07:28,760 --> 00:07:31,280 Speaker 1: because he would say, all right, now, the last hundred 119 00:07:31,280 --> 00:07:35,520 Speaker 1: times the weather was exactly like it is today, twenty 120 00:07:35,560 --> 00:07:38,320 Speaker 1: of those times it rained, eighty of those times it 121 00:07:38,360 --> 00:07:41,040 Speaker 1: did not. Therefore, there is a twenty percent chance that 122 00:07:41,160 --> 00:07:44,400 Speaker 1: it will rain. That again is oversimplifying the way it works, 123 00:07:44,960 --> 00:07:47,920 Speaker 1: but generally speaking, that's kind of how they come to 124 00:07:47,960 --> 00:07:53,040 Speaker 1: those determinations, And in fact, there are ways of bolstering 125 00:07:53,120 --> 00:07:57,080 Speaker 1: the NWP by using something called model output statistics, which 126 00:07:57,160 --> 00:07:59,520 Speaker 1: is I kind of just talked about it a little bit. 127 00:07:59,640 --> 00:08:01,280 Speaker 1: I'll just go ahead and touch on it right now. 128 00:08:01,640 --> 00:08:04,320 Speaker 1: It's essentially doing what we were talking about, looking at 129 00:08:04,360 --> 00:08:09,280 Speaker 1: a specific region and the specific outcomes of days that 130 00:08:09,360 --> 00:08:12,240 Speaker 1: had similar conditions to the one you're looking at right now, 131 00:08:13,160 --> 00:08:16,280 Speaker 1: and then you're kind of making an educated guess based 132 00:08:16,320 --> 00:08:20,920 Speaker 1: upon a computer model and actual localized history. I got 133 00:08:20,960 --> 00:08:22,960 Speaker 1: that clear to something up for a lot of people. 134 00:08:23,080 --> 00:08:25,840 Speaker 1: I sure hope so because I've just kind of gone 135 00:08:25,880 --> 00:08:27,960 Speaker 1: with it. You know, I've just seen, oh, twenty percent 136 00:08:28,080 --> 00:08:30,240 Speaker 1: chance of rain, okay, and never really thought about what 137 00:08:30,320 --> 00:08:33,560 Speaker 1: goes into determining that. Well. And I know that there's 138 00:08:33,559 --> 00:08:36,480 Speaker 1: some people who had, you know, when they saw twenty 139 00:08:36,520 --> 00:08:38,439 Speaker 1: percent chance of rain, they thought it meant, oh, it's 140 00:08:38,480 --> 00:08:41,520 Speaker 1: going to rain over twenty percent of the forecast area, 141 00:08:41,679 --> 00:08:43,480 Speaker 1: which means that you know, that would be more like 142 00:08:43,520 --> 00:08:46,319 Speaker 1: scattered showers. That's really what scattered showers means. When you're 143 00:08:46,360 --> 00:08:50,520 Speaker 1: scattered showers, it means that parts of the forecast area 144 00:08:50,600 --> 00:08:53,119 Speaker 1: are expected to get rain, but it will not necessarily 145 00:08:53,200 --> 00:08:57,640 Speaker 1: rain over the entire forecast area. But if you hear 146 00:08:57,679 --> 00:08:59,440 Speaker 1: twenty percent chance of rain, it does not mean that 147 00:08:59,640 --> 00:09:01,760 Speaker 1: eighty percent of the forecast area is going to be 148 00:09:01,840 --> 00:09:03,400 Speaker 1: dry and the other twenty percent it's going to be wet. 149 00:09:03,440 --> 00:09:05,720 Speaker 1: Nor does it mean it will rain for twenty percent 150 00:09:05,720 --> 00:09:08,760 Speaker 1: of the day. In fact, that's part of the problem. 151 00:09:10,000 --> 00:09:13,880 Speaker 1: A prediction of precipitation, the good old pop, the pop 152 00:09:15,200 --> 00:09:19,920 Speaker 1: so pop. That requires a time element to it as well. 153 00:09:20,080 --> 00:09:22,600 Speaker 1: It doesn't mean anything without a time element. So if 154 00:09:22,640 --> 00:09:25,120 Speaker 1: you say there's a twenty percent chance to rain, you 155 00:09:25,200 --> 00:09:27,480 Speaker 1: also need to have an element of time attached to 156 00:09:27,520 --> 00:09:29,880 Speaker 1: that to make it meaningful. So twenty percent chance to 157 00:09:30,000 --> 00:09:33,439 Speaker 1: rain over the next six hours, then you know, all right, 158 00:09:33,480 --> 00:09:35,280 Speaker 1: So it's not saying that's going to be twenty percent 159 00:09:35,400 --> 00:09:37,679 Speaker 1: chance to rain or it's not gonna rain twenty percent 160 00:09:37,720 --> 00:09:40,280 Speaker 1: of the day, just that for the next six hours 161 00:09:40,320 --> 00:09:42,400 Speaker 1: there's a twenty percent chance it will be raining in 162 00:09:42,440 --> 00:09:46,640 Speaker 1: the forecast area. We will be back with more weather 163 00:09:46,720 --> 00:10:01,160 Speaker 1: technology after this quick break, so I hope that demystifies 164 00:10:01,200 --> 00:10:04,000 Speaker 1: some of it. Also, we can talk about radar. One 165 00:10:04,000 --> 00:10:07,240 Speaker 1: of my favorite things to talk about. Radar is awesome, 166 00:10:07,679 --> 00:10:11,719 Speaker 1: favorite character on mash me too. Yeah, well, I think 167 00:10:12,160 --> 00:10:14,880 Speaker 1: it's so adorable, right, Yeah, it's hard not to feel 168 00:10:14,920 --> 00:10:18,480 Speaker 1: for him, and the fact that he can anticipate everything 169 00:10:18,559 --> 00:10:23,000 Speaker 1: his commanding officer wants, and he can even say what 170 00:10:23,040 --> 00:10:25,600 Speaker 1: the commanding officer is saying before the commanding officer has 171 00:10:25,640 --> 00:10:31,640 Speaker 1: finished a sentence is obviously a key part of that operation. 172 00:10:33,080 --> 00:10:37,040 Speaker 1: But we're talking about actual radar, using radar to detect weather. 173 00:10:37,440 --> 00:10:41,200 Speaker 1: You've probably heard Doppler radar when looking at a weather report, like, well, 174 00:10:41,240 --> 00:10:44,120 Speaker 1: let's look at the Doppler radar and see where this 175 00:10:44,720 --> 00:10:49,720 Speaker 1: precipitation is moving in. Doppler radar for weather is different 176 00:10:49,760 --> 00:10:52,880 Speaker 1: from Doppler radar used by say, police officers, who are 177 00:10:53,360 --> 00:10:56,960 Speaker 1: trying to detect if you are speeding. The Doppler radar 178 00:10:57,120 --> 00:11:01,840 Speaker 1: that meteorologists use actually shoots out radio waves in very 179 00:11:01,960 --> 00:11:06,920 Speaker 1: short bursts called pulses, and then the radar listens for 180 00:11:07,000 --> 00:11:10,280 Speaker 1: any echoing pulses coming back to the antenna, and the 181 00:11:10,320 --> 00:11:14,360 Speaker 1: short pulses indicate not just the presence of something out there, 182 00:11:14,800 --> 00:11:18,480 Speaker 1: but whether it's moving and which direction is it moving in. 183 00:11:20,280 --> 00:11:23,199 Speaker 1: Is it moving toward the radar station or away from it. 184 00:11:23,280 --> 00:11:26,800 Speaker 1: If a Doppler radar receiver detect waves of a higher frequency, 185 00:11:27,400 --> 00:11:31,720 Speaker 1: the precipitation particles are moving toward the radar exactly, and 186 00:11:32,000 --> 00:11:35,679 Speaker 1: lower frequencies they're moving away. Yes, Because what's happening is 187 00:11:35,920 --> 00:11:39,800 Speaker 1: it's similar to a Doppler shift. And anyone who's ever 188 00:11:39,840 --> 00:11:43,720 Speaker 1: heard a vehicle with a siren go past yeah, is 189 00:11:43,760 --> 00:11:46,480 Speaker 1: familiar with this. It's a higher pitch as the vehicles 190 00:11:46,520 --> 00:11:49,400 Speaker 1: coming toward you, and a lower pitch as it's moving away. 191 00:11:49,440 --> 00:11:52,920 Speaker 1: What's actually happening is as a vehicle is moving toward you, 192 00:11:53,160 --> 00:11:56,640 Speaker 1: the sound waves it's emitting are being compressed. Now that 193 00:11:56,760 --> 00:12:00,679 Speaker 1: compression creates a higher frequency, which means we detect higher pitch. 194 00:12:01,720 --> 00:12:06,400 Speaker 1: As the vehicle passes, those frequencies are elongated, which means 195 00:12:06,400 --> 00:12:08,760 Speaker 1: a lower pitch. Same thing is true with the radar 196 00:12:08,800 --> 00:12:11,559 Speaker 1: accept Instead of it being a pitch, it's a radio frequency. 197 00:12:11,640 --> 00:12:14,319 Speaker 1: If so, a higher frequency will tell you, yeah, something's 198 00:12:14,360 --> 00:12:16,840 Speaker 1: coming towards you, and a lower frequency will tell you 199 00:12:17,160 --> 00:12:21,360 Speaker 1: something's moving away from you. And also the time between 200 00:12:21,480 --> 00:12:24,120 Speaker 1: when the pulse goes out and when you detect it 201 00:12:24,160 --> 00:12:29,720 Speaker 1: tells you the distance from the radar detection system and 202 00:12:29,840 --> 00:12:33,400 Speaker 1: the precipitation. So you could even say there's a storm 203 00:12:33,520 --> 00:12:37,960 Speaker 1: system that's five miles to the west, it's moving easterly 204 00:12:38,120 --> 00:12:41,280 Speaker 1: at this speed because you've detected it through a series 205 00:12:41,280 --> 00:12:45,600 Speaker 1: of pulses. If you have enough radar detection stations, you 206 00:12:45,640 --> 00:12:49,880 Speaker 1: can even describe the shape of the weather system and 207 00:12:49,960 --> 00:12:53,880 Speaker 1: talk about how some areas are more intense than others. 208 00:12:53,960 --> 00:12:56,280 Speaker 1: You can get all of that information from this approach, 209 00:12:56,720 --> 00:13:01,440 Speaker 1: and it's amazing how this thing works. First of all, 210 00:13:02,400 --> 00:13:07,760 Speaker 1: it's super high power. These radar stations are they're they're 211 00:13:07,800 --> 00:13:10,480 Speaker 1: generating or they're transmitting I should say, at four hundred 212 00:13:10,480 --> 00:13:14,680 Speaker 1: and fifty thousand watts. So your typical microwave oven is 213 00:13:14,679 --> 00:13:17,400 Speaker 1: a thousand watts. Wow, so you need a four hundred 214 00:13:17,400 --> 00:13:20,520 Speaker 1: and fifty of those to equal one of these radar systems. 215 00:13:21,400 --> 00:13:25,439 Speaker 1: So four and fifty thousand watts, and the pulse lasts 216 00:13:25,520 --> 00:13:29,800 Speaker 1: so short as to be unimaginable. It is point zero 217 00:13:30,040 --> 00:13:35,679 Speaker 1: zero zero zero zero one five seven seconds long, or 218 00:13:35,760 --> 00:13:38,520 Speaker 1: one point five seven times ten to the minus six seconds. 219 00:13:38,679 --> 00:13:42,160 Speaker 1: So if you hear the weatherman on TV bragging about 220 00:13:42,200 --> 00:13:45,640 Speaker 1: Doppler radar, there's a reason. It's very impressive. Yeah. I mean, 221 00:13:45,640 --> 00:13:51,520 Speaker 1: you're sitting at a burst of radio signals at such 222 00:13:51,520 --> 00:13:55,480 Speaker 1: a fraction of a second that it is again impossible 223 00:13:55,480 --> 00:13:59,520 Speaker 1: to even imagine. Meanwhile, then it listens for a longer period, 224 00:13:59,559 --> 00:14:02,000 Speaker 1: and by longer I mean relatively longer. It's still a 225 00:14:02,040 --> 00:14:05,360 Speaker 1: fraction of a second. It's point zero zero zero nine 226 00:14:05,520 --> 00:14:10,040 Speaker 1: nine eight four three seconds. So it shoots out a pulse, 227 00:14:10,800 --> 00:14:13,480 Speaker 1: listens for a little while, so it can detect when 228 00:14:13,520 --> 00:14:16,320 Speaker 1: the pulse comes back and what frequency it's at, so 229 00:14:16,360 --> 00:14:18,240 Speaker 1: it knows whether or not a body is moving toward 230 00:14:18,280 --> 00:14:20,880 Speaker 1: it or away from it, and then it does it again. 231 00:14:21,880 --> 00:14:24,280 Speaker 1: But that means, with that amount of time and the 232 00:14:25,360 --> 00:14:29,400 Speaker 1: comparatively large amount of time of listening, for every hour 233 00:14:29,680 --> 00:14:34,600 Speaker 1: of operation, the radio or the radar antenna is only 234 00:14:34,640 --> 00:14:39,360 Speaker 1: shooting out signals for seven seconds out of an entire hour. Wow, 235 00:14:39,680 --> 00:14:42,120 Speaker 1: that means for the fifty nine minutes fifty three seconds, 236 00:14:42,120 --> 00:14:44,160 Speaker 1: it is not sending out a signal. It is listening. 237 00:14:45,520 --> 00:14:50,160 Speaker 1: So for almost a full hour it's listening and only 238 00:14:50,200 --> 00:14:52,840 Speaker 1: for seven seconds is it actively shooting out a signal. 239 00:14:52,960 --> 00:14:55,200 Speaker 1: Can you mentioned it a conversation for someone who talks 240 00:14:55,200 --> 00:14:58,160 Speaker 1: seven seconds out of an hour, it would I any 241 00:14:58,200 --> 00:15:01,360 Speaker 1: conversation with me would last like a decade and before 242 00:15:01,400 --> 00:15:05,200 Speaker 1: you could get a word in edgewise. Yeah, it's it's 243 00:15:05,320 --> 00:15:08,680 Speaker 1: pretty amazing, and you usually would have one of these 244 00:15:08,880 --> 00:15:14,320 Speaker 1: stations shooting out these radio bursts at different angles of elevation. 245 00:15:14,480 --> 00:15:18,520 Speaker 1: These are called elevation slices, and when you go through 246 00:15:18,760 --> 00:15:22,360 Speaker 1: the entire range, you get what was called volume coverage 247 00:15:22,400 --> 00:15:25,640 Speaker 1: pattern or VCP, and that's what tells you what the 248 00:15:25,680 --> 00:15:29,600 Speaker 1: activity is, not just at ground level, but up in 249 00:15:29,640 --> 00:15:35,480 Speaker 1: the atmosphere as well. Toper radar can also detect tornadoes. Yeah. Yeah, 250 00:15:34,280 --> 00:15:38,400 Speaker 1: if if the particles switch from moving toward and then 251 00:15:38,480 --> 00:15:42,000 Speaker 1: away over a small distance, there's a good chance it 252 00:15:42,040 --> 00:15:45,000 Speaker 1: could be a tornado. Yeah, we know a lot about 253 00:15:45,040 --> 00:15:47,080 Speaker 1: those here in the southeast too. We get a lot 254 00:15:47,080 --> 00:15:50,800 Speaker 1: of tornadoes, not as many as places in the you know, 255 00:15:50,880 --> 00:15:54,000 Speaker 1: like in the Midwestern states, things like you know, Oklahoma 256 00:15:54,000 --> 00:15:56,640 Speaker 1: and stuff. I mean, you guys get tornadoes even more 257 00:15:56,680 --> 00:16:00,160 Speaker 1: frequently than we do, but we get them pretty serious. 258 00:16:00,760 --> 00:16:03,240 Speaker 1: Actually this year hasn't been too bad. No, but there 259 00:16:03,280 --> 00:16:06,160 Speaker 1: was one in November, yeah, which is weird because typically 260 00:16:06,160 --> 00:16:09,680 Speaker 1: we get them in the spring. Yes, usually between March 261 00:16:09,720 --> 00:16:14,120 Speaker 1: and June. That's kind of like our let's play it easy. Yeah, 262 00:16:14,200 --> 00:16:17,240 Speaker 1: but you don't wash that on anybody. So no, I 263 00:16:18,120 --> 00:16:22,680 Speaker 1: have been through a close call with a tornado while 264 00:16:22,720 --> 00:16:26,880 Speaker 1: wearing Renaissance Festival gear. That sounds surreal. That was my 265 00:16:27,120 --> 00:16:29,680 Speaker 1: final day when I did my first run at the 266 00:16:29,680 --> 00:16:33,480 Speaker 1: festival in two thousand and one. Wow. Yeah, we had 267 00:16:33,520 --> 00:16:36,240 Speaker 1: a really massive thunderstorm and at one point someone said 268 00:16:36,280 --> 00:16:39,760 Speaker 1: that there was a tornado a tornado watch, but not 269 00:16:39,840 --> 00:16:43,640 Speaker 1: a tornado warning watch, being that the conditions for a 270 00:16:43,640 --> 00:16:48,000 Speaker 1: tornado forming are present, warning being that a tornado has 271 00:16:48,040 --> 00:16:51,960 Speaker 1: actually been spotted in the region, in case you were wondering. 272 00:16:52,400 --> 00:16:55,480 Speaker 1: So now let's talk about satellites and meteorology. So the 273 00:16:55,520 --> 00:16:58,160 Speaker 1: computers are really good for building out those models and 274 00:16:58,200 --> 00:17:02,040 Speaker 1: giving us predictions. The double radars really good at tracking precipitation. 275 00:17:02,480 --> 00:17:07,159 Speaker 1: What do weather satellites do well, they're keeping an eye 276 00:17:07,600 --> 00:17:11,600 Speaker 1: on global weather patterns. But there are two different types 277 00:17:11,640 --> 00:17:15,159 Speaker 1: of weather satellites and they do this in different ways. 278 00:17:15,200 --> 00:17:20,200 Speaker 1: So one is the geostationary weather satellite. Now, geostationary weather 279 00:17:20,240 --> 00:17:24,919 Speaker 1: satellites maintain their relative position over a specific point on 280 00:17:24,920 --> 00:17:28,160 Speaker 1: the Earth. They are at a very high orbit over 281 00:17:28,240 --> 00:17:32,800 Speaker 1: the equator and they're always looking at the same thing 282 00:17:32,840 --> 00:17:36,480 Speaker 1: because their orbit is at the same speed as Earth's rotation, 283 00:17:37,640 --> 00:17:39,960 Speaker 1: not really the same speed, but relative speed. Because it's 284 00:17:40,000 --> 00:17:43,200 Speaker 1: able to stay in that same point over that part 285 00:17:43,280 --> 00:17:45,840 Speaker 1: of the Earth, and so they have to be on 286 00:17:45,920 --> 00:17:47,879 Speaker 1: an equatorial orbit and they have to be at a 287 00:17:47,880 --> 00:17:51,040 Speaker 1: particular altitude for this to work. It's great because it 288 00:17:51,080 --> 00:17:53,119 Speaker 1: means they can keep an eye on a specific region. 289 00:17:53,240 --> 00:17:56,800 Speaker 1: It's lousy because one, they're really far away, so the 290 00:17:56,840 --> 00:17:59,240 Speaker 1: instrumentation you have to put on the satellites has to 291 00:17:59,240 --> 00:18:03,080 Speaker 1: be incredibly sophisticated in order to get good readings from 292 00:18:03,119 --> 00:18:06,359 Speaker 1: that altitude. Plus they have a limited view, right, They're 293 00:18:06,359 --> 00:18:08,240 Speaker 1: always looking at one part of the Earth. They can't 294 00:18:08,280 --> 00:18:11,720 Speaker 1: see anything else outside of that view. Always geostationary yep. 295 00:18:12,200 --> 00:18:14,840 Speaker 1: So the other type you have are satellites that are 296 00:18:14,840 --> 00:18:18,480 Speaker 1: in a polar orbit around the Earth. Polar orbits are interesting. 297 00:18:18,560 --> 00:18:21,720 Speaker 1: So if you think of the Earth on its axis, 298 00:18:21,760 --> 00:18:25,840 Speaker 1: the polar orbit is going parallel to the axis of 299 00:18:25,880 --> 00:18:29,840 Speaker 1: the Earth. It's going perpendicular to the equator. So you 300 00:18:29,840 --> 00:18:32,200 Speaker 1: would think of it as going from north to south 301 00:18:32,240 --> 00:18:35,439 Speaker 1: and then south to north because once it crosses the 302 00:18:35,440 --> 00:18:38,080 Speaker 1: south pole, you can only go north at that point. 303 00:18:38,119 --> 00:18:41,280 Speaker 1: That's the only direction left to you, and it goes 304 00:18:41,280 --> 00:18:44,960 Speaker 1: in that circle, which means these satellites get a full 305 00:18:45,040 --> 00:18:48,440 Speaker 1: view of the entire Earth, because the Earth is rotating 306 00:18:48,520 --> 00:18:52,520 Speaker 1: while it's going in this orbit north south orbit. But 307 00:18:52,880 --> 00:18:55,760 Speaker 1: it also means that you only get a look at 308 00:18:55,840 --> 00:18:58,480 Speaker 1: the same part of the Earth twice in a twenty 309 00:18:58,480 --> 00:19:01,960 Speaker 1: four hour period, since once every twelve hours, you could 310 00:19:01,960 --> 00:19:04,359 Speaker 1: always put another satellite up there, and that way you 311 00:19:04,359 --> 00:19:08,399 Speaker 1: could get you put it on the opposite side of 312 00:19:08,440 --> 00:19:10,560 Speaker 1: the Earth where it's in the same orbit, and then 313 00:19:10,560 --> 00:19:13,120 Speaker 1: you get a look every six hours, just one from 314 00:19:13,160 --> 00:19:16,600 Speaker 1: one satellite and then six hours later one from another satellite. 315 00:19:17,240 --> 00:19:20,280 Speaker 1: But you also get to see everything on the planet, 316 00:19:20,359 --> 00:19:23,399 Speaker 1: So there's your trade off is that you get a 317 00:19:24,080 --> 00:19:27,600 Speaker 1: more comprehensive view, but you don't get a consistent view 318 00:19:27,880 --> 00:19:29,800 Speaker 1: of any one part of the Earth with these kind 319 00:19:29,840 --> 00:19:33,280 Speaker 1: of weather satellites. So a lot of weather surfaces depend 320 00:19:33,359 --> 00:19:36,000 Speaker 1: upon both, yeah, so it's good to have both, yeah. 321 00:19:36,280 --> 00:19:41,119 Speaker 1: And typically they carry devices called radiometers, which usually have 322 00:19:41,359 --> 00:19:44,840 Speaker 1: a small telescope or some sort of antenna, a scanning 323 00:19:44,840 --> 00:19:47,600 Speaker 1: device of some sort, and one or more detectors that 324 00:19:47,640 --> 00:19:51,080 Speaker 1: can pick up visible, infrared or microwave radiation, and they 325 00:19:51,200 --> 00:19:53,680 Speaker 1: use that to take measurements of the Earth and send 326 00:19:53,720 --> 00:19:57,439 Speaker 1: that down to the planet's surface, so that weather stations 327 00:19:57,480 --> 00:20:00,560 Speaker 1: around the world can take that data and crunch it 328 00:20:00,640 --> 00:20:03,000 Speaker 1: and figure out what the heck is going out on 329 00:20:03,080 --> 00:20:05,200 Speaker 1: out there when the frogs are raining from the sky 330 00:20:05,960 --> 00:20:11,080 Speaker 1: apart from amphibious assault, and all of those measurements are 331 00:20:11,080 --> 00:20:14,640 Speaker 1: actually done through little electrical voltages which then get digitized, 332 00:20:14,800 --> 00:20:18,320 Speaker 1: so transformed into digital information before transmitted down to Earth, 333 00:20:18,320 --> 00:20:21,440 Speaker 1: because you know, zapping electricity through space down to the planet, 334 00:20:21,440 --> 00:20:24,199 Speaker 1: it's not the most efficient way of getting information across 335 00:20:25,000 --> 00:20:26,760 Speaker 1: You certainly don't want to have a power chord stretch 336 00:20:26,800 --> 00:20:28,760 Speaker 1: all the way there. That would just be such a pain, 337 00:20:28,960 --> 00:20:32,480 Speaker 1: very inefficient. Yeah. Also with aircraft patterns. Yeah, and if 338 00:20:32,480 --> 00:20:35,200 Speaker 1: you don't have geostationary orbit, it gets wrapped up around 339 00:20:35,240 --> 00:20:40,240 Speaker 1: the planet pretty quickly. Yeah. We will conclude our two 340 00:20:40,320 --> 00:20:44,679 Speaker 1: part episode series about weather attack after this quick break. 341 00:20:54,280 --> 00:20:58,080 Speaker 1: So how do meteorologists do things like predict temperature, like 342 00:20:58,119 --> 00:21:00,800 Speaker 1: predict highs and lows and that kind of stuff? For 343 00:21:00,920 --> 00:21:03,480 Speaker 1: this I went to a website that was written by 344 00:21:03,520 --> 00:21:08,399 Speaker 1: a meteorologist named Jeff Haby, and boy howdy, did it 345 00:21:08,440 --> 00:21:11,439 Speaker 1: suddenly dawn on me how much more complicated this was 346 00:21:11,480 --> 00:21:15,600 Speaker 1: than I had even anticipated. But according to Haby, he 347 00:21:15,680 --> 00:21:22,520 Speaker 1: looks at everything from thermal advection, wind speed, cloud cover, 348 00:21:22,800 --> 00:21:25,960 Speaker 1: dew point, and the number of daylight hours expected for 349 00:21:26,040 --> 00:21:28,680 Speaker 1: that region in order to come up with the prediction 350 00:21:28,760 --> 00:21:30,240 Speaker 1: for the high temperature of the day in the low 351 00:21:30,280 --> 00:21:32,880 Speaker 1: temperature of the day. So what the heck does all 352 00:21:32,920 --> 00:21:36,879 Speaker 1: that mean? So thermal advection, what is that? That refers 353 00:21:36,920 --> 00:21:40,480 Speaker 1: to the transportation of heat by a moving fluid. So 354 00:21:40,520 --> 00:21:44,920 Speaker 1: typically the stuff that affects the advection include the strength 355 00:21:44,960 --> 00:21:48,119 Speaker 1: of wind, So how hard is the wind blowing in 356 00:21:48,160 --> 00:21:52,840 Speaker 1: that region? The temperature gradient between the warmer and colder areas. 357 00:21:53,000 --> 00:21:55,960 Speaker 1: So if one area is warmer than the other, is 358 00:21:55,960 --> 00:21:58,560 Speaker 1: it warmer by like a couple of degrees or is 359 00:21:58,560 --> 00:22:01,440 Speaker 1: it more significant than that is ten degrees fahrenheit, that 360 00:22:01,480 --> 00:22:06,159 Speaker 1: would be a much larger gradient, right, And the angle 361 00:22:06,240 --> 00:22:09,239 Speaker 1: between the wind direction and the temperature gradient, if that 362 00:22:09,480 --> 00:22:12,359 Speaker 1: angle is more narrow, you're going to see a greater 363 00:22:12,480 --> 00:22:17,000 Speaker 1: thermal advection, meaning you'll see more temperature changes moving into 364 00:22:17,040 --> 00:22:21,520 Speaker 1: an area from a different region. So that's just advection. 365 00:22:22,720 --> 00:22:25,399 Speaker 1: That's a lot of play. The other one that you 366 00:22:25,640 --> 00:22:28,320 Speaker 1: other term you might be a little confused by. I mean, 367 00:22:28,400 --> 00:22:31,560 Speaker 1: wind speed makes sense, cloudcover makes sense daylight hours. All 368 00:22:31,600 --> 00:22:34,199 Speaker 1: of that makes sense, but what about dew point? That 369 00:22:34,280 --> 00:22:36,960 Speaker 1: refers to the temperature at which air must be cooled 370 00:22:37,119 --> 00:22:42,000 Speaker 1: at constant barometric pressure for water vapor to condense. So 371 00:22:42,240 --> 00:22:45,040 Speaker 1: that temperature, again is dependent upon things like the actual 372 00:22:45,040 --> 00:22:49,680 Speaker 1: air pressure, right, and so the dew point changes based 373 00:22:49,720 --> 00:22:53,199 Speaker 1: upon those other factors as well. So all those have 374 00:22:53,280 --> 00:22:56,320 Speaker 1: to be taken into account before a meteorologist can forecast 375 00:22:56,359 --> 00:22:58,120 Speaker 1: what the temperature is going to be the next day. 376 00:22:58,160 --> 00:23:01,399 Speaker 1: This is why we're so happy to have those complicated 377 00:23:01,400 --> 00:23:04,600 Speaker 1: computers now, because if you were to keep track of 378 00:23:04,600 --> 00:23:08,320 Speaker 1: this yourself, you probably go bonkers. And then we have 379 00:23:08,440 --> 00:23:11,600 Speaker 1: like the idea of the probability of precipitation, which we 380 00:23:11,680 --> 00:23:14,320 Speaker 1: kind of talked about already, but generally speaking, there's some 381 00:23:14,359 --> 00:23:20,720 Speaker 1: weather services that will only predict rainfall if it's expected 382 00:23:20,720 --> 00:23:23,840 Speaker 1: to be over a certain amount, like point two five millimeters. 383 00:23:24,080 --> 00:23:26,480 Speaker 1: If it's going to be less than point two five millimeters, 384 00:23:26,560 --> 00:23:30,800 Speaker 1: it doesn't even register as rainfall in predictions. You would 385 00:23:30,800 --> 00:23:35,040 Speaker 1: say there's a zero percent chance or whatever, if that's 386 00:23:35,080 --> 00:23:38,200 Speaker 1: what you think is going to be the accumulation. Some 387 00:23:38,280 --> 00:23:41,800 Speaker 1: other ones are like, no any rain at all counts 388 00:23:42,760 --> 00:23:45,760 Speaker 1: if it's one drop of rain it rained in that region, 389 00:23:46,960 --> 00:23:51,239 Speaker 1: so it really depends upon the service. But that we 390 00:23:51,240 --> 00:23:53,719 Speaker 1: already talked about the percentages and what those means, so 391 00:23:53,760 --> 00:23:56,560 Speaker 1: hopefully that clears things up. And again that kind of 392 00:23:56,560 --> 00:24:00,560 Speaker 1: goes into that concept of model output statistics, where you 393 00:24:01,960 --> 00:24:05,800 Speaker 1: correct for your predictions based upon past conditions for a 394 00:24:05,800 --> 00:24:10,479 Speaker 1: particular region. All of this comes together to create the 395 00:24:10,520 --> 00:24:15,520 Speaker 1: weather report that you see. So I think the real 396 00:24:15,560 --> 00:24:22,760 Speaker 1: takeaway here is it's incredible the amount of schooling and 397 00:24:23,040 --> 00:24:26,320 Speaker 1: expertise a meteorologist has to have in order to do 398 00:24:26,400 --> 00:24:29,439 Speaker 1: his or her job properly. Yes, right, like, because you 399 00:24:29,440 --> 00:24:31,359 Speaker 1: see how complicated this is, and you start to have 400 00:24:31,400 --> 00:24:34,679 Speaker 1: an appreciation of all right, they said it was a 401 00:24:34,800 --> 00:24:37,399 Speaker 1: sixty percent chance of rain. I brought my umbrella and 402 00:24:37,520 --> 00:24:40,880 Speaker 1: never rained. Now you realize, well, when you're talking about 403 00:24:40,880 --> 00:24:45,240 Speaker 1: the system, this complicated and this unpredictable, something that can 404 00:24:45,320 --> 00:24:49,600 Speaker 1: change dramatically just because something you did not anticipate happened, 405 00:24:50,720 --> 00:24:54,119 Speaker 1: you start to appreciate more the challenge that they have 406 00:24:54,520 --> 00:24:58,320 Speaker 1: to do their jobs properly. So give your meteorologist a 407 00:24:58,440 --> 00:25:04,400 Speaker 1: hug and say thank you, because this stuff is hard. Yeah, 408 00:25:04,440 --> 00:25:07,960 Speaker 1: and my you know if weather is the state of 409 00:25:08,000 --> 00:25:10,800 Speaker 1: the atmosphere from day to day, and the atmosphere is 410 00:25:10,920 --> 00:25:15,480 Speaker 1: super complex, you know, it's it's my favorite analogy from 411 00:25:15,520 --> 00:25:21,520 Speaker 1: our article on our website about meteorologies, that the atmosphere 412 00:25:21,640 --> 00:25:25,240 Speaker 1: is like a soup with too many cooks. Yeah. Yeah, 413 00:25:25,240 --> 00:25:28,439 Speaker 1: there's a lot at play and so many different variables 414 00:25:28,520 --> 00:25:33,760 Speaker 1: are working behind the scenes to give you a simple 415 00:25:34,000 --> 00:25:38,119 Speaker 1: a simple weather forecast that is easily digestible. Yeah. This 416 00:25:38,240 --> 00:25:45,200 Speaker 1: is also why when you hear about supercomputers running weather simulations, 417 00:25:45,280 --> 00:25:47,760 Speaker 1: that's why you need a supercomputer because of this too 418 00:25:47,760 --> 00:25:50,280 Speaker 1: many cooks. I mean, it takes a lot to make 419 00:25:50,359 --> 00:25:54,040 Speaker 1: us stew. I hope, I hope at least some of 420 00:25:54,080 --> 00:25:58,240 Speaker 1: you are singing along now, But yeah, it's it really 421 00:25:58,280 --> 00:26:01,280 Speaker 1: does explain why you need that massive amount of computing 422 00:26:01,280 --> 00:26:03,280 Speaker 1: power just to do something that you would think would 423 00:26:03,280 --> 00:26:06,080 Speaker 1: be fairly simple. You know, you're thinking like, oh, it's 424 00:26:06,080 --> 00:26:08,879 Speaker 1: like six or seven factors, and then you realize, oh, wait, no, 425 00:26:08,960 --> 00:26:11,800 Speaker 1: there are these other things that also have an effect, 426 00:26:11,840 --> 00:26:15,760 Speaker 1: and in some cases a measurable and meaningful effect, not 427 00:26:15,880 --> 00:26:21,200 Speaker 1: just a potential effect. And it really does drive home 428 00:26:21,400 --> 00:26:26,640 Speaker 1: that it's amazing we can have relatively accurate weather predictions 429 00:26:26,640 --> 00:26:31,640 Speaker 1: in the first place. And also it makes me kind 430 00:26:31,680 --> 00:26:34,399 Speaker 1: of sad that I no longer I used to be 431 00:26:34,480 --> 00:26:38,159 Speaker 1: on television with a local weather guy. He did a 432 00:26:38,200 --> 00:26:41,440 Speaker 1: show at five thirty in the morning, and I would 433 00:26:41,440 --> 00:26:43,760 Speaker 1: show up on television and do a gadget segment with him, 434 00:26:43,800 --> 00:26:46,520 Speaker 1: and it was great. He was very nice, and his 435 00:26:46,560 --> 00:26:51,840 Speaker 1: ability to break down complicated concepts of weather in a 436 00:26:51,880 --> 00:26:55,040 Speaker 1: way that was helpful to people so that they could 437 00:26:55,080 --> 00:26:59,920 Speaker 1: plan their day was really amazing, especially when you start 438 00:27:00,040 --> 00:27:02,080 Speaker 1: really thinking about all the things that come into play 439 00:27:02,119 --> 00:27:06,680 Speaker 1: to make that, you know possible. So our hats are 440 00:27:06,680 --> 00:27:09,880 Speaker 1: off to you meteorologists out there, keep doing the good work. 441 00:27:10,560 --> 00:27:14,080 Speaker 1: I look forward to learning more about you know, when 442 00:27:14,160 --> 00:27:16,679 Speaker 1: we when we figure out even more details about the 443 00:27:16,680 --> 00:27:19,760 Speaker 1: complexities of the atmosphere and perhaps are able to make 444 00:27:19,800 --> 00:27:24,920 Speaker 1: even more accurate models, maybe we will one day reach 445 00:27:25,000 --> 00:27:29,080 Speaker 1: the back to the future too level where minute by 446 00:27:29,119 --> 00:27:31,720 Speaker 1: minute it tells you what the weather is going to be. 447 00:27:31,960 --> 00:27:34,240 Speaker 1: Of course, then I think they were actually suggesting that 448 00:27:34,280 --> 00:27:38,520 Speaker 1: we would have weather control, which is a whole other 449 00:27:38,560 --> 00:27:42,439 Speaker 1: thing that's opening and cannle worms. Yeah, I talked to 450 00:27:42,520 --> 00:27:45,800 Speaker 1: Dylan that I said I had thought about doing a 451 00:27:45,800 --> 00:27:49,840 Speaker 1: little discussion about weather control. But obviously we've gone pretty 452 00:27:49,880 --> 00:27:52,439 Speaker 1: long already. So what I will say about weather control 453 00:27:52,640 --> 00:27:56,920 Speaker 1: is weather systems represent a huge amount of energy, and 454 00:27:57,080 --> 00:28:02,359 Speaker 1: in order for us to affect or manufacture weather events 455 00:28:03,160 --> 00:28:05,520 Speaker 1: on a large scale, we would have to be able 456 00:28:05,560 --> 00:28:08,760 Speaker 1: to generate that amount of energy and pour it into 457 00:28:08,760 --> 00:28:11,720 Speaker 1: the atmosphere in a way that actually does what we 458 00:28:11,760 --> 00:28:14,600 Speaker 1: wanted to do. And we're so far away from any 459 00:28:14,680 --> 00:28:18,359 Speaker 1: of those things that it's absolutely unrealistic to think of 460 00:28:18,400 --> 00:28:21,080 Speaker 1: weather control, even if you are a Cobra commander. Yeah, 461 00:28:22,480 --> 00:28:24,640 Speaker 1: you know, going back to the beginning of this episode 462 00:28:24,640 --> 00:28:27,080 Speaker 1: with that listener request for this, and yeah, their father 463 00:28:27,240 --> 00:28:30,320 Speaker 1: said that in the sixties that they felt like the 464 00:28:30,400 --> 00:28:33,320 Speaker 1: weather report was just a joke. And you fast forward 465 00:28:33,400 --> 00:28:36,760 Speaker 1: to now and how you know, maybe some days you 466 00:28:36,800 --> 00:28:39,040 Speaker 1: grab an umbrella and you don't need it, but that 467 00:28:39,120 --> 00:28:43,200 Speaker 1: it's you know, you can track major weather patterns and 468 00:28:43,760 --> 00:28:47,440 Speaker 1: incoming storms. That we have a pretty good ability to 469 00:28:47,480 --> 00:28:51,960 Speaker 1: track hurricanes and like flash floods and things like that. 470 00:28:52,960 --> 00:28:56,200 Speaker 1: I can't imagine where I'll be in forty or fifty years. Yeah, 471 00:28:56,240 --> 00:29:00,800 Speaker 1: the fact that we can get at least heads up 472 00:29:00,800 --> 00:29:04,360 Speaker 1: on stuff before it becomes critical to us, is really important. 473 00:29:05,560 --> 00:29:09,000 Speaker 1: I mean, Dylan, you probably remember it wasn't that long 474 00:29:09,040 --> 00:29:13,560 Speaker 1: ago here in Atlanta when we had the snow apocalypse. Yes, 475 00:29:13,680 --> 00:29:16,440 Speaker 1: and because it was one of those things where the 476 00:29:16,480 --> 00:29:19,400 Speaker 1: initial weather report suggested that the weather was going to 477 00:29:19,440 --> 00:29:23,600 Speaker 1: miss the city and it didn't, that's an indication that, yeah, 478 00:29:23,600 --> 00:29:27,040 Speaker 1: our predictions are not one hundred percent accurate, they're not infallible. 479 00:29:27,920 --> 00:29:31,000 Speaker 1: And it also taught us a valuable lesson, which is 480 00:29:31,040 --> 00:29:35,239 Speaker 1: that even when you feel like there's a pretty good 481 00:29:35,360 --> 00:29:37,440 Speaker 1: chance that you're going to miss out on that bad weather, 482 00:29:37,840 --> 00:29:43,320 Speaker 1: it doesn't hurt to prepare because the alternative is to 483 00:29:43,360 --> 00:29:46,280 Speaker 1: spend eight hours on two eighty five, even if it 484 00:29:46,400 --> 00:29:49,640 Speaker 1: is an inch of snow, because it's Atlanta. Yeah, and 485 00:29:49,720 --> 00:29:51,840 Speaker 1: because we don't have a system in place to deal 486 00:29:51,880 --> 00:29:53,400 Speaker 1: with an inch of snow, and we got a lot 487 00:29:53,440 --> 00:29:56,720 Speaker 1: of hills. None of us have snow tires. Why would you? 488 00:29:57,000 --> 00:29:59,240 Speaker 1: And you let out the private in the public sector 489 00:29:59,280 --> 00:30:03,240 Speaker 1: at the same time, that was particularly bad. I remember 490 00:30:03,280 --> 00:30:06,480 Speaker 1: I actually stayed here, not here, but in our old 491 00:30:06,560 --> 00:30:10,560 Speaker 1: office location. I stayed there pretty much through the full 492 00:30:10,640 --> 00:30:12,760 Speaker 1: day because it was like, I'm gonna take Martha, I'm 493 00:30:12,760 --> 00:30:14,640 Speaker 1: gonna take the train. It's not gonna be a big deal. 494 00:30:15,040 --> 00:30:18,280 Speaker 1: It took me three hours to get home, usually would 495 00:30:18,280 --> 00:30:20,600 Speaker 1: take me forty five minutes, and I was getting I 496 00:30:20,640 --> 00:30:23,000 Speaker 1: got online, got ready to complain, and then I started 497 00:30:23,000 --> 00:30:24,960 Speaker 1: reading messages for my friends who were stuck in their 498 00:30:24,960 --> 00:30:27,360 Speaker 1: cars and had been for six hours. I thought, yeah, okay, 499 00:30:27,400 --> 00:30:30,560 Speaker 1: we're going to back away from the community slowly. Kids 500 00:30:30,600 --> 00:30:34,320 Speaker 1: stuck in school buses overnight. Yeah. Yeah, pretty rough stuff. 501 00:30:34,360 --> 00:30:38,360 Speaker 1: So yeah, we're We're not perfect, but it is getting better, 502 00:30:38,400 --> 00:30:40,640 Speaker 1: and it is pretty impressive to see the amount of 503 00:30:40,640 --> 00:30:43,680 Speaker 1: information you can get. I love, I mean I love. 504 00:30:43,720 --> 00:30:47,320 Speaker 1: I find watching Doppler radar readouts to be fascinating, Like 505 00:30:47,360 --> 00:30:50,280 Speaker 1: I could have that open on my desk all day 506 00:30:49,960 --> 00:30:52,040 Speaker 1: if I if I didn't have other stuff I need 507 00:30:52,040 --> 00:30:56,240 Speaker 1: to do. So Drees, thank you so much for sending 508 00:30:56,280 --> 00:30:58,440 Speaker 1: that request in. It's a lot of fun to kind 509 00:30:58,440 --> 00:31:00,360 Speaker 1: of read up on it and to go over Dylan, 510 00:31:00,400 --> 00:31:02,640 Speaker 1: thank you for joining me in the studio. Thanks for 511 00:31:02,680 --> 00:31:06,760 Speaker 1: having me greatly appreciate it. Well, that was it for WeatherTech, 512 00:31:06,840 --> 00:31:10,600 Speaker 1: at least from back in twenty sixteen. Again, I'll probably 513 00:31:10,720 --> 00:31:13,400 Speaker 1: need to do an update to this whenever I do 514 00:31:13,440 --> 00:31:18,040 Speaker 1: these classic episode intros naltros. I'm always reminded, Oh yeah, 515 00:31:18,080 --> 00:31:20,800 Speaker 1: I should really revisit this. This is one of those. 516 00:31:20,840 --> 00:31:23,760 Speaker 1: But if there are topics like new ones, or maybe 517 00:31:23,800 --> 00:31:26,400 Speaker 1: there's a topic you think I should revisit that I 518 00:31:26,440 --> 00:31:29,000 Speaker 1: haven't really talked about in a long time, you should 519 00:31:29,080 --> 00:31:30,880 Speaker 1: let me know. And there are a couple of different 520 00:31:30,880 --> 00:31:33,320 Speaker 1: ways of doing that. You can go to Twitter and 521 00:31:33,400 --> 00:31:36,400 Speaker 1: you can tweet to me. The show handle is tech 522 00:31:36,640 --> 00:31:41,920 Speaker 1: Stuff HSW or if you prefer to actually talk to me, 523 00:31:42,600 --> 00:31:46,320 Speaker 1: you can download the iHeartRadio app. It's free to downloads 524 00:31:46,320 --> 00:31:48,800 Speaker 1: free to use. You can navigate over to tech Stuff 525 00:31:48,800 --> 00:31:50,960 Speaker 1: by putting that into the little search field and you 526 00:31:51,000 --> 00:31:53,200 Speaker 1: will see on the tech Stuff podcast page there's a 527 00:31:53,240 --> 00:31:56,400 Speaker 1: little microphone icon. If you click on that icon, you 528 00:31:56,440 --> 00:31:58,920 Speaker 1: can leave a voice message up to thirty seconds in 529 00:31:59,000 --> 00:32:00,800 Speaker 1: length and let me know what you would like me 530 00:32:00,840 --> 00:32:03,520 Speaker 1: to talk about in the future, and I will talk 531 00:32:03,560 --> 00:32:12,400 Speaker 1: to you again really soon. Y text Stuff is an 532 00:32:12,400 --> 00:32:17,920 Speaker 1: iHeartRadio production. For more podcasts from iHeartRadio, visit the iHeartRadio app, 533 00:32:18,040 --> 00:32:21,240 Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.