1 00:00:04,440 --> 00:00:12,440 Speaker 1: Technology with tech Stuff from stuff works dot com. Welcome 2 00:00:12,520 --> 00:00:16,160 Speaker 1: to tech Stuff. I am your host, Jonathan Strickland. I'm 3 00:00:16,160 --> 00:00:19,560 Speaker 1: a senior writer with how stuff works dot com and 4 00:00:19,600 --> 00:00:22,239 Speaker 1: I'm so glad you could join me today here on 5 00:00:22,320 --> 00:00:25,360 Speaker 1: tech Stuff, we like to cover all things technological and 6 00:00:25,680 --> 00:00:28,680 Speaker 1: explain how they work and why they're important. And for 7 00:00:28,720 --> 00:00:31,480 Speaker 1: the last couple of episodes, we've really been focusing on 8 00:00:31,600 --> 00:00:36,400 Speaker 1: meteorology and weather forecasting and the types of technology that 9 00:00:36,440 --> 00:00:39,280 Speaker 1: we used to try and predict the weather. UH. In 10 00:00:39,400 --> 00:00:42,440 Speaker 1: the first episode in this series, we concentrated mainly on 11 00:00:42,479 --> 00:00:45,760 Speaker 1: the science of weather itself, and that's important to understand 12 00:00:45,800 --> 00:00:49,479 Speaker 1: because you begin to pick up on how complicated a 13 00:00:49,840 --> 00:00:54,200 Speaker 1: system whether actually is, especially when you start expanding out 14 00:00:54,320 --> 00:00:57,240 Speaker 1: from a small region to a larger region to a 15 00:00:57,280 --> 00:01:01,640 Speaker 1: global region and you see how interdependent all of these 16 00:01:01,640 --> 00:01:05,800 Speaker 1: different regions are on each other, the brain starts to 17 00:01:05,880 --> 00:01:08,560 Speaker 1: swim a bit. In Part two, we looked at the 18 00:01:08,680 --> 00:01:14,560 Speaker 1: various sensors and tools used to capture information about what 19 00:01:14,800 --> 00:01:18,360 Speaker 1: is going on with the weather. These are the tools 20 00:01:18,440 --> 00:01:22,559 Speaker 1: that meteorologists use in order to feed that information into 21 00:01:22,640 --> 00:01:27,200 Speaker 1: their various weather models. So UH, Today's episode is going 22 00:01:27,240 --> 00:01:30,120 Speaker 1: to focus on those weather models. These are based on 23 00:01:30,160 --> 00:01:33,720 Speaker 1: our understanding of the behavior of weather under different conditions, 24 00:01:33,760 --> 00:01:36,640 Speaker 1: and it's what gives us the confidence to make predictions 25 00:01:36,640 --> 00:01:41,920 Speaker 1: of what will happen next. Now that being said, predictions, 26 00:01:42,080 --> 00:01:45,080 Speaker 1: as we all know, are not guarantees. I'm sure there 27 00:01:45,120 --> 00:01:47,920 Speaker 1: are many of you who have walked out of your 28 00:01:47,920 --> 00:01:51,640 Speaker 1: homes with confidence, dressed in your best clothing, only to 29 00:01:51,720 --> 00:01:55,720 Speaker 1: be plagued by an unexpected downpour at an inopportune time, 30 00:01:55,880 --> 00:01:58,840 Speaker 1: turning into Charlie Brown with that one rain cloud just 31 00:01:59,000 --> 00:02:02,559 Speaker 1: directly overhead. Or you might be one of those people 32 00:02:02,880 --> 00:02:05,800 Speaker 1: that have convinced him or herself that if you have 33 00:02:05,920 --> 00:02:09,000 Speaker 1: an umbrella in your hand, it virtually guarantees that not 34 00:02:09,120 --> 00:02:12,840 Speaker 1: a single drop of rain will fall, that you, by 35 00:02:12,960 --> 00:02:17,000 Speaker 1: virtue of holding the umbrella, have prevented rain from happening. 36 00:02:17,320 --> 00:02:20,200 Speaker 1: As it turns out, predicting the weather is really hard 37 00:02:20,520 --> 00:02:22,440 Speaker 1: for a lot of reasons, though the biggest one is 38 00:02:22,440 --> 00:02:26,280 Speaker 1: that weather is just an incredibly complicated system affected by 39 00:02:26,360 --> 00:02:29,640 Speaker 1: hundreds of variables, and those variables may have a lesser 40 00:02:29,760 --> 00:02:33,760 Speaker 1: or greater effect in different situations. That means there's a 41 00:02:33,760 --> 00:02:36,959 Speaker 1: lot of potential outcomes for any given scenario, and until 42 00:02:37,000 --> 00:02:40,079 Speaker 1: we have a really comprehensive understanding of what's going on 43 00:02:40,240 --> 00:02:43,440 Speaker 1: at all times in our atmosphere. Weather predictions will be 44 00:02:43,440 --> 00:02:49,200 Speaker 1: based primarily on statistical probabilities, not certainties. Before we really 45 00:02:49,440 --> 00:02:51,960 Speaker 1: had a handle on all of those variables, and to 46 00:02:52,040 --> 00:02:54,880 Speaker 1: be honest, we don't completely have a full handle on 47 00:02:54,919 --> 00:02:58,600 Speaker 1: them right now. We based weather predictions off of empirical 48 00:02:58,720 --> 00:03:03,560 Speaker 1: rules that we formed observation. So, in other words, generally speaking, 49 00:03:03,639 --> 00:03:05,840 Speaker 1: if you woke up and looked outside the window and 50 00:03:05,840 --> 00:03:08,000 Speaker 1: you thought, hey, it looks like it might rain today, 51 00:03:08,080 --> 00:03:09,840 Speaker 1: because a couple of weeks ago I looked out the 52 00:03:09,840 --> 00:03:12,320 Speaker 1: window and it looked just like this and it rained 53 00:03:12,440 --> 00:03:15,519 Speaker 1: that day, well that's about as complex as it got. 54 00:03:15,639 --> 00:03:18,240 Speaker 1: I mean, you might have a weather map, like a 55 00:03:18,320 --> 00:03:21,560 Speaker 1: literal map, and you have some things you've written down 56 00:03:21,560 --> 00:03:23,640 Speaker 1: on it. You know that to the west of you 57 00:03:24,560 --> 00:03:27,919 Speaker 1: there's a low pressure system, so you might start using 58 00:03:27,960 --> 00:03:31,440 Speaker 1: that as a guide for what could end up happening. 59 00:03:31,880 --> 00:03:34,320 Speaker 1: But it wasn't a very precise science. It wasn't what 60 00:03:34,360 --> 00:03:38,200 Speaker 1: people were calling a rational approach. It was an empirical approach, 61 00:03:39,240 --> 00:03:41,480 Speaker 1: and over time we began to understand that weather is 62 00:03:41,480 --> 00:03:46,520 Speaker 1: dictated by a host of very complex variables. So meteorologists 63 00:03:46,520 --> 00:03:51,000 Speaker 1: are gathering all of this data about air pressure, temperature, windspeed, 64 00:03:51,080 --> 00:03:53,920 Speaker 1: weather patterns that are nearby, and tons of other factors. 65 00:03:54,920 --> 00:03:59,440 Speaker 1: These things are changing quickly and constantly, meaning it's important 66 00:03:59,440 --> 00:04:03,640 Speaker 1: to look over those observations regularly and adjust projections. And 67 00:04:03,680 --> 00:04:06,800 Speaker 1: if you're not lucky, you may only have a few 68 00:04:06,840 --> 00:04:11,680 Speaker 1: observation stations placed in strategic locations within your particular region, 69 00:04:12,040 --> 00:04:18,120 Speaker 1: which gives you a limited on resolution. So weather forecasting 70 00:04:18,160 --> 00:04:23,000 Speaker 1: is a lot like your displays or your televisions resolutions. 71 00:04:23,080 --> 00:04:27,040 Speaker 1: Very important resolution is all about how many points of 72 00:04:27,160 --> 00:04:31,040 Speaker 1: data do you have within that given area, how representative 73 00:04:31,720 --> 00:04:35,000 Speaker 1: are those observation stations. If you have a single observation 74 00:04:35,080 --> 00:04:39,000 Speaker 1: station for several square miles, well that's not going to 75 00:04:39,040 --> 00:04:41,440 Speaker 1: give you very good resolution, right You're going to have 76 00:04:41,800 --> 00:04:44,360 Speaker 1: a very specific idea of what's going on at one 77 00:04:44,440 --> 00:04:48,360 Speaker 1: point within that area, but everything further out from there 78 00:04:48,640 --> 00:04:51,719 Speaker 1: it's going to be some variation of the information you've 79 00:04:51,760 --> 00:04:54,520 Speaker 1: pulled down. So if you want high resolution, you have 80 00:04:54,560 --> 00:04:57,760 Speaker 1: to have lots of observation stations throughout that same area, 81 00:04:57,920 --> 00:05:01,640 Speaker 1: and you collectively are able to determine what's happening by 82 00:05:01,680 --> 00:05:05,160 Speaker 1: looking at all of them, but obviously that adds a 83 00:05:05,160 --> 00:05:09,520 Speaker 1: lot more information to your calculations. In an ideal world, 84 00:05:09,920 --> 00:05:13,480 Speaker 1: you have all areas densely packed with observation stations, giving 85 00:05:13,480 --> 00:05:18,440 Speaker 1: you amazing consistent resolution and the processing power necessary to 86 00:05:18,480 --> 00:05:21,200 Speaker 1: take all that raw data and crunch it to produce 87 00:05:21,279 --> 00:05:25,320 Speaker 1: reliable weather forecasts at any given moment. But we just 88 00:05:25,520 --> 00:05:31,600 Speaker 1: aren't there yet. So weather models, what's the story on those? Well, 89 00:05:31,640 --> 00:05:33,920 Speaker 1: it helps to look back on the birth of meteorology 90 00:05:33,960 --> 00:05:37,159 Speaker 1: and weather forecasting in the form of numerical weather prediction. 91 00:05:37,480 --> 00:05:39,559 Speaker 1: That's really what we get down to when we start 92 00:05:39,600 --> 00:05:43,200 Speaker 1: talking about weather models. And to do this we actually 93 00:05:43,200 --> 00:05:45,760 Speaker 1: have to backtrack a little bit. I know, we talked 94 00:05:45,760 --> 00:05:48,719 Speaker 1: a lot about the various tools in the last episode 95 00:05:48,720 --> 00:05:50,920 Speaker 1: and we got pretty up to date, but we're gonna 96 00:05:50,920 --> 00:05:54,440 Speaker 1: have to go back to talk about weather models specifically. Now. 97 00:05:54,440 --> 00:05:57,480 Speaker 1: In the nineteenth century, so this is the eighteen hundreds, 98 00:05:57,600 --> 00:06:01,320 Speaker 1: physicists were beginning to suss out the law of thermodynamics. 99 00:06:02,279 --> 00:06:06,680 Speaker 1: These are the basic laws of energy that we UH 100 00:06:06,680 --> 00:06:11,320 Speaker 1: that everything is is UH has to obey, at least 101 00:06:11,320 --> 00:06:14,200 Speaker 1: everything on the macro scale has to obey. And it 102 00:06:14,240 --> 00:06:18,760 Speaker 1: was relatively easy to put hypotheses to test with modest experiments, right, Like, 103 00:06:18,800 --> 00:06:23,040 Speaker 1: you could do tests about fluid dynamics with small contained 104 00:06:23,279 --> 00:06:27,560 Speaker 1: systems and you could limit the variables and make lots 105 00:06:27,560 --> 00:06:32,160 Speaker 1: of observations. That was pretty easy, relatively speaking, But it 106 00:06:32,240 --> 00:06:35,039 Speaker 1: was much more challenging to step back, and I mean 107 00:06:35,160 --> 00:06:38,120 Speaker 1: way way back and see how those same laws applied 108 00:06:38,120 --> 00:06:41,760 Speaker 1: to something as massive as our atmosphere. If you want 109 00:06:41,760 --> 00:06:45,320 Speaker 1: to think about another way, it's one thing to look 110 00:06:45,400 --> 00:06:49,479 Speaker 1: at a maze that has rats running in it. It's 111 00:06:49,480 --> 00:06:52,640 Speaker 1: another thing to try and figure out what the maze 112 00:06:52,760 --> 00:06:55,680 Speaker 1: is like. When you are inside the maze and you 113 00:06:55,680 --> 00:06:58,159 Speaker 1: can only see a small part of it, How do 114 00:06:58,200 --> 00:07:01,240 Speaker 1: you know what the entire layout of the mazes from 115 00:07:01,279 --> 00:07:03,599 Speaker 1: that perspective? So, in other words, the perspective of the 116 00:07:03,680 --> 00:07:08,120 Speaker 1: rats we are inside the actual environment that we want 117 00:07:08,160 --> 00:07:11,120 Speaker 1: to describe. That makes it way more difficult for us 118 00:07:11,160 --> 00:07:17,400 Speaker 1: to uh isolate and weigh every single variable in the system. Well, 119 00:07:17,440 --> 00:07:21,680 Speaker 1: in nineteen o one, there was a professor named Cleveland Abbey. 120 00:07:21,840 --> 00:07:25,239 Speaker 1: He published a piece in a journal called Monthly Weather Review. 121 00:07:25,400 --> 00:07:28,160 Speaker 1: Now I was really sad to discover that this wasn't 122 00:07:28,160 --> 00:07:32,120 Speaker 1: actually a list of reviews for actual weather like Weather 123 00:07:32,160 --> 00:07:35,760 Speaker 1: Today was pretty good. I give it three stars. No 124 00:07:36,000 --> 00:07:38,600 Speaker 1: it It actually was a scholarly journal on the subject 125 00:07:38,640 --> 00:07:42,760 Speaker 1: of meteorology, and Abbey's piece had the title The Physical 126 00:07:42,840 --> 00:07:47,559 Speaker 1: Basis of Long Range Weather Forecasts. And in that piece, 127 00:07:47,560 --> 00:07:50,119 Speaker 1: Abbey pointed out that forecasts of the time were based 128 00:07:50,160 --> 00:07:54,400 Speaker 1: on experience rather than any real knowledge of how weather works, 129 00:07:54,600 --> 00:07:57,520 Speaker 1: and that the physical theories explained the development of whether 130 00:07:57,560 --> 00:08:01,400 Speaker 1: we're either superficial or non existent. So it goes back 131 00:08:01,400 --> 00:08:05,400 Speaker 1: to that example I gave earlier. Weather forecasting was based 132 00:08:05,440 --> 00:08:09,720 Speaker 1: on people's experience with weather, but not knowing how the 133 00:08:09,760 --> 00:08:13,240 Speaker 1: weather was actually working. So you might say, well, I 134 00:08:13,280 --> 00:08:17,000 Speaker 1: think that it may snow tomorrow based upon what the 135 00:08:17,040 --> 00:08:19,840 Speaker 1: conditions are right now, and the fact that I remember 136 00:08:19,880 --> 00:08:22,080 Speaker 1: a day that was like this where it snowed the 137 00:08:22,120 --> 00:08:27,080 Speaker 1: next day. But that's not a very scientific approach ultimately speaking, 138 00:08:27,080 --> 00:08:30,800 Speaker 1: and it doesn't have It is not based upon understanding 139 00:08:30,840 --> 00:08:35,160 Speaker 1: the factors that lead to things like a snowstorm, and 140 00:08:35,200 --> 00:08:38,280 Speaker 1: so Abby was arguing that in order to have real 141 00:08:38,440 --> 00:08:42,240 Speaker 1: weather forecasting prowess, we would have to gain that understanding 142 00:08:42,360 --> 00:08:47,040 Speaker 1: of the underlying factors of weather. Abby asserted that we 143 00:08:47,120 --> 00:08:50,959 Speaker 1: just had to understand the laws of mechanics and heat 144 00:08:51,320 --> 00:08:54,679 Speaker 1: of the atmosphere. Only then, he posited, could we use 145 00:08:54,720 --> 00:08:57,920 Speaker 1: the information to start making more accurate forecasts. And his 146 00:08:58,040 --> 00:09:00,200 Speaker 1: peace would go on to outline what he saw as 147 00:09:00,280 --> 00:09:04,280 Speaker 1: the necessary steps to get there, including a thorough investigation 148 00:09:04,360 --> 00:09:06,959 Speaker 1: of the behaviors of the atmosphere, and he said that 149 00:09:07,000 --> 00:09:11,199 Speaker 1: the science of meteorology is quote essentially the application of 150 00:09:11,280 --> 00:09:17,280 Speaker 1: hydrodynamics and thermodynamics in the atmosphere end quote, So he 151 00:09:17,400 --> 00:09:22,320 Speaker 1: was calling for the establishment of a new area of science, 152 00:09:22,760 --> 00:09:26,480 Speaker 1: specifically within meteorology, something that that would require people to 153 00:09:26,520 --> 00:09:30,120 Speaker 1: dedicate a lot of time to try and understand this 154 00:09:30,320 --> 00:09:35,400 Speaker 1: complex system that is our atmosphere. Then you have a 155 00:09:35,440 --> 00:09:42,000 Speaker 1: Norwegian scientist, Vilhelm Biekness. He was born in Christiania, Norway, 156 00:09:42,120 --> 00:09:44,560 Speaker 1: in eighteen sixty two, and as a young man he 157 00:09:44,640 --> 00:09:49,840 Speaker 1: worked with a notable physicist, Heinrich Hurts Hurts I mentioned 158 00:09:50,040 --> 00:09:53,200 Speaker 1: in our episodes on the history of electricity, you know 159 00:09:53,320 --> 00:09:58,000 Speaker 1: the Hurts. He went on to he being Bakness went 160 00:09:58,080 --> 00:10:01,800 Speaker 1: on to teach applied mechanic and mathematical physics at the 161 00:10:01,920 --> 00:10:04,839 Speaker 1: University of Stockholm, where he sussed out some theorems that 162 00:10:04,880 --> 00:10:08,760 Speaker 1: helped him create a synthesis of hydrodynamics and thermodynamics for 163 00:10:09,000 --> 00:10:13,560 Speaker 1: large scale atmospheric motions, the very thing that Abby was 164 00:10:13,640 --> 00:10:17,880 Speaker 1: calling for. Bierk Nous was the one to to develop 165 00:10:17,920 --> 00:10:23,600 Speaker 1: a very comprehensive model, the first really comprehensive model for that, 166 00:10:24,040 --> 00:10:26,720 Speaker 1: and this led to the development of air mass theory, 167 00:10:26,880 --> 00:10:30,439 Speaker 1: one of the principal ideas upon which we base weather forecasting. 168 00:10:31,480 --> 00:10:35,200 Speaker 1: Now in n Berk Now published a work titled on 169 00:10:35,280 --> 00:10:39,160 Speaker 1: the Dynamics of the Circular Vortex with Applications to the 170 00:10:39,200 --> 00:10:43,640 Speaker 1: atmosphere and to atmospheric vortex and wave motion. And it's 171 00:10:43,679 --> 00:10:47,400 Speaker 1: a real page turner, guys. This is a pretty dense 172 00:10:48,679 --> 00:10:51,920 Speaker 1: piece of of scholarly work. It's considered one of the 173 00:10:51,920 --> 00:10:55,800 Speaker 1: most important scholarly works in the field of meteorology, and 174 00:10:55,800 --> 00:10:58,160 Speaker 1: it was the basis of our understanding of general weather 175 00:10:58,200 --> 00:11:01,280 Speaker 1: pattern behaviors and why they take on the forms the 176 00:11:01,320 --> 00:11:03,760 Speaker 1: way they do. In other words, it was pretty much 177 00:11:03,800 --> 00:11:07,240 Speaker 1: what Professor Abbey was saying was necessary before we took 178 00:11:07,240 --> 00:11:11,200 Speaker 1: a rationally scientific approach to forecasting the weather. Now, what 179 00:11:11,400 --> 00:11:16,720 Speaker 1: Wilhelm illustrated was that the atmosphere and thus weather does 180 00:11:17,160 --> 00:11:22,280 Speaker 1: can be described in math through fluid dynamics factors like 181 00:11:22,360 --> 00:11:25,760 Speaker 1: temperature impact those behaviors, so you have to take that 182 00:11:25,800 --> 00:11:29,720 Speaker 1: into account. And as things change, there's a sort of 183 00:11:29,800 --> 00:11:34,760 Speaker 1: ripple effect. If you change one part of the the system, 184 00:11:34,800 --> 00:11:37,319 Speaker 1: that ripples out and affects the rest of the system 185 00:11:37,360 --> 00:11:41,120 Speaker 1: in different ways depending upon other factors. And nothing in 186 00:11:41,160 --> 00:11:44,840 Speaker 1: the atmosphere is remaining completely unchanged, and as each element 187 00:11:44,920 --> 00:11:48,360 Speaker 1: shifts or cools down, or heats up, or the density 188 00:11:48,520 --> 00:11:52,360 Speaker 1: changes or whatever it may be, it affects other parts. 189 00:11:52,400 --> 00:11:56,520 Speaker 1: So the math gets pretty challenging pretty fast. He further 190 00:11:56,559 --> 00:11:59,480 Speaker 1: went on to create a two step process for rational 191 00:11:59,559 --> 00:12:03,440 Speaker 1: forecast nesting of whether now. The first step was diagnostic, 192 00:12:03,880 --> 00:12:07,240 Speaker 1: which is, in other words, using observations to determine what 193 00:12:07,440 --> 00:12:10,120 Speaker 1: is the present state of the atmosphere. This is where 194 00:12:10,480 --> 00:12:12,760 Speaker 1: you take all those readings and you say what is 195 00:12:12,800 --> 00:12:16,240 Speaker 1: going on right now? That's the diagnostic step, and it's 196 00:12:16,320 --> 00:12:19,400 Speaker 1: absolutely necessary before you can do anything else. You can't 197 00:12:19,440 --> 00:12:22,160 Speaker 1: say what's going to happen next until you have an 198 00:12:22,200 --> 00:12:26,640 Speaker 1: understanding of what is happening right now. The second step 199 00:12:26,720 --> 00:12:30,680 Speaker 1: was prognostic, which meant that you would use that information 200 00:12:30,720 --> 00:12:34,920 Speaker 1: from the diagnostic step and project outwards and say, all right, well, 201 00:12:35,120 --> 00:12:38,120 Speaker 1: based upon what we know is happening right now, what 202 00:12:38,320 --> 00:12:41,400 Speaker 1: is going to happen twelve hours from now, or a 203 00:12:41,520 --> 00:12:44,679 Speaker 1: day from now or two days from now. And you 204 00:12:44,720 --> 00:12:47,280 Speaker 1: would have to use the information you had gathered in 205 00:12:47,320 --> 00:12:50,600 Speaker 1: the diagnostic step, combined with our knowledge of the laws 206 00:12:50,640 --> 00:12:56,520 Speaker 1: of motion for atmospheric masses, to predict what would happen next. Now, 207 00:12:56,600 --> 00:12:59,840 Speaker 1: if you could strip everything away and just look at 208 00:12:59,840 --> 00:13:02,720 Speaker 1: the math, you'd be looking at a collection of what 209 00:13:02,840 --> 00:13:06,600 Speaker 1: are called partial differential equations. The mathematicians out there know 210 00:13:06,679 --> 00:13:10,160 Speaker 1: exactly what I'm talking about. These are equations that deal 211 00:13:10,200 --> 00:13:14,640 Speaker 1: with rates of change with respect to continuous variables. So 212 00:13:14,679 --> 00:13:19,040 Speaker 1: you're not just talking about variables like temperature, pressure, and velocity. 213 00:13:19,360 --> 00:13:21,840 Speaker 1: You are talking about those, but you're also talking about 214 00:13:21,840 --> 00:13:26,640 Speaker 1: the rate of change of those variables. How quickly is 215 00:13:26,679 --> 00:13:31,320 Speaker 1: the temperature changing, how quickly is the pressure changing, etcetera. Now, 216 00:13:31,320 --> 00:13:35,200 Speaker 1: the way you frame and solve these equations defines your 217 00:13:35,600 --> 00:13:41,439 Speaker 1: weather model. Different weather models place different emphasis on these 218 00:13:41,520 --> 00:13:44,320 Speaker 1: variables and the rates of change. All of this boils 219 00:13:44,320 --> 00:13:48,760 Speaker 1: down to a computer program ultimately that solves these equations 220 00:13:48,840 --> 00:13:52,560 Speaker 1: as you have directed. So one model might approximate different 221 00:13:52,559 --> 00:13:55,960 Speaker 1: equations one way and another model does so in a 222 00:13:56,080 --> 00:13:59,240 Speaker 1: different way, and thus you're going to get two different forecasts. 223 00:13:59,280 --> 00:14:03,320 Speaker 1: Consulting these two different models, they might resemble one another, 224 00:14:03,480 --> 00:14:06,040 Speaker 1: but they're taking different pathways to get to their destination, 225 00:14:06,280 --> 00:14:10,400 Speaker 1: so sometimes they might be very different from one another. 226 00:14:10,800 --> 00:14:13,480 Speaker 1: And it's all because of the way you have told 227 00:14:13,520 --> 00:14:18,400 Speaker 1: the program to prioritize the various processes, which ones you know, 228 00:14:18,400 --> 00:14:23,320 Speaker 1: which factors have the most weight and under what circumstances. Now, 229 00:14:23,360 --> 00:14:25,880 Speaker 1: this doesn't necessarily mean one model is by its nature 230 00:14:25,960 --> 00:14:29,600 Speaker 1: superior to the other. Some models are for specific regions 231 00:14:29,600 --> 00:14:32,080 Speaker 1: in the world, and those regions, due to geography and 232 00:14:32,160 --> 00:14:35,760 Speaker 1: general atmospheric motions, may require more importance to be placed 233 00:14:35,840 --> 00:14:40,280 Speaker 1: on certain sets of variables rather than others. Now Berkness 234 00:14:40,400 --> 00:14:45,760 Speaker 1: identified seven variables he saw as critical for accurate weather forecasting. 235 00:14:46,120 --> 00:14:51,160 Speaker 1: That includes pressure, temperature, density, humidity, and then three different 236 00:14:51,200 --> 00:14:56,160 Speaker 1: components of velocity. He also identified seven equations, three therma 237 00:14:56,800 --> 00:15:00,560 Speaker 1: hydro dynamic equations I should say three hydro dynamic equation, emotion, 238 00:15:01,120 --> 00:15:05,040 Speaker 1: the continuity equation, the equation of state, and equations expressing 239 00:15:05,080 --> 00:15:08,160 Speaker 1: the first two laws of thermo dynamics. Now, keep in 240 00:15:08,200 --> 00:15:11,400 Speaker 1: mind that the atmosphere is three dimensional. You have to 241 00:15:11,480 --> 00:15:16,160 Speaker 1: essentially consider any region within that area a three dimensional grid. 242 00:15:16,800 --> 00:15:19,400 Speaker 1: So your grid has an x, y, and z axis, 243 00:15:20,080 --> 00:15:24,080 Speaker 1: and the events within one part of that grid can 244 00:15:24,120 --> 00:15:28,200 Speaker 1: affect other parts of it, particularly atmospheric motion. And you 245 00:15:28,240 --> 00:15:31,560 Speaker 1: need to figure out how to take partial derivatives which 246 00:15:32,200 --> 00:15:36,680 Speaker 1: computers can't really handle, and then turn them into approximate 247 00:15:36,760 --> 00:15:42,560 Speaker 1: partial derivatives that computers can handle instead. This approximation adds 248 00:15:42,560 --> 00:15:46,520 Speaker 1: in a bit of imprecision by its very nature. But 249 00:15:46,600 --> 00:15:51,240 Speaker 1: then's the brakes. And speaking of brakes, let's take a 250 00:15:51,320 --> 00:16:00,880 Speaker 1: quick one right now to thank our sponsor. So getting 251 00:16:00,920 --> 00:16:06,080 Speaker 1: back into forming weather models. There's a meteorologist named Lewis 252 00:16:06,200 --> 00:16:10,080 Speaker 1: Fry Richardson who was influenced by Berkness, and he did 253 00:16:10,160 --> 00:16:13,520 Speaker 1: his best to tackle the problem of numerical weather forecasting, 254 00:16:13,600 --> 00:16:16,840 Speaker 1: but he stated that the sheer amount of computation was 255 00:16:16,920 --> 00:16:19,960 Speaker 1: impractical for the time. This would be in the early 256 00:16:20,040 --> 00:16:23,200 Speaker 1: twentieth century, first couple of decades of the nineteen hundreds. 257 00:16:23,720 --> 00:16:27,800 Speaker 1: He did say, quote, perhaps someday in the dim future, 258 00:16:28,080 --> 00:16:31,360 Speaker 1: it will be possible to advance the computations faster than 259 00:16:31,400 --> 00:16:34,920 Speaker 1: the weather advances. But that is a dream end quote. 260 00:16:35,240 --> 00:16:37,960 Speaker 1: So he's saying, by the time I'm able to work 261 00:16:37,960 --> 00:16:40,720 Speaker 1: out the math, whatever the weather was gonna be has 262 00:16:40,760 --> 00:16:45,560 Speaker 1: already happened. I'm predicting what happened hours ago. Uh. And 263 00:16:45,560 --> 00:16:47,800 Speaker 1: that was a real problem. Was just that again, you 264 00:16:47,880 --> 00:16:50,720 Speaker 1: had these very complex equations with lots of points of 265 00:16:50,800 --> 00:16:53,440 Speaker 1: data and lots of variables that you had to solve for, 266 00:16:54,080 --> 00:16:56,080 Speaker 1: and by the time you would be done with all 267 00:16:56,080 --> 00:17:01,520 Speaker 1: the calculations, the time had passed. So he was saying, 268 00:17:01,520 --> 00:17:06,480 Speaker 1: there is a need for some sort of engine that 269 00:17:06,560 --> 00:17:11,720 Speaker 1: can do computations faster than what humans can do. So 270 00:17:11,760 --> 00:17:13,960 Speaker 1: the math was just too complex to complete without the 271 00:17:14,080 --> 00:17:17,720 Speaker 1: use of that computational engine. One person determined to help 272 00:17:17,720 --> 00:17:20,680 Speaker 1: design such an engine was John von Neumann, who was 273 00:17:20,720 --> 00:17:24,439 Speaker 1: a mathematician who made numerous contributions to the sciences, and 274 00:17:24,440 --> 00:17:26,639 Speaker 1: he realized that some of the more advanced problems in 275 00:17:26,720 --> 00:17:30,520 Speaker 1: hydrodynamics and weather forecasting would benefit from a powerful automatic 276 00:17:30,560 --> 00:17:34,199 Speaker 1: computational machine. He worked on a project at Princeton at 277 00:17:34,240 --> 00:17:36,960 Speaker 1: the Institute for Advanced Studies and it would become known 278 00:17:37,000 --> 00:17:42,120 Speaker 1: as the Electronic Computer Project. Meanwhile, over at the University 279 00:17:42,119 --> 00:17:46,600 Speaker 1: of Pennsylvania's More School of Electrical Engineering, you had J. G. 280 00:17:46,880 --> 00:17:50,200 Speaker 1: Brainerd who was heading up a project, and J. Presspur 281 00:17:50,280 --> 00:17:53,600 Speaker 1: Eckert and John W. Malchley who were working on what 282 00:17:53,640 --> 00:17:59,399 Speaker 1: was called the Electronic Numerical Integrator and Computer or NIAC 283 00:17:59,760 --> 00:18:03,080 Speaker 1: for short, and computer nerds out there, I consider myself 284 00:18:03,119 --> 00:18:06,040 Speaker 1: one of them will kind of bristle at the sound 285 00:18:06,040 --> 00:18:09,040 Speaker 1: of ENNIAC. They might prick up their ears and say, oh, 286 00:18:09,280 --> 00:18:11,879 Speaker 1: I know, I've heard of ENNIAC. ENIAC being one of 287 00:18:11,880 --> 00:18:16,360 Speaker 1: those early early computers in the dawn of the computing age. 288 00:18:16,720 --> 00:18:20,320 Speaker 1: Well brain Nerd, the man who was heading this project, 289 00:18:20,400 --> 00:18:23,560 Speaker 1: invited von Neumann over to the University of Pennsylvania to 290 00:18:23,640 --> 00:18:25,880 Speaker 1: check out ENIAC, and von Neuman would end up having 291 00:18:25,920 --> 00:18:29,119 Speaker 1: these very deep discussions with the team, and those discussions 292 00:18:29,160 --> 00:18:33,240 Speaker 1: would help inform the design of the successor to ENIAC, 293 00:18:33,680 --> 00:18:36,680 Speaker 1: and these early computers would become some of the first 294 00:18:36,720 --> 00:18:40,639 Speaker 1: capable of tackling those difficult computational problems. I was mentioning 295 00:18:40,640 --> 00:18:44,119 Speaker 1: a second ago, and that brings us to the concept 296 00:18:44,600 --> 00:18:48,000 Speaker 1: of the weather model. Now, your weather model is an 297 00:18:48,040 --> 00:18:51,280 Speaker 1: advanced computer program that runs all of these sorts of 298 00:18:51,320 --> 00:18:54,399 Speaker 1: equations and then calculates outcomes. So, in other words, it 299 00:18:54,520 --> 00:18:57,920 Speaker 1: generates your weather forecast based upon those points of data. 300 00:18:58,680 --> 00:19:01,120 Speaker 1: Many of these weather models have been written in four 301 00:19:01,240 --> 00:19:05,000 Speaker 1: chan for Tran, I should say, largely because that's how 302 00:19:05,040 --> 00:19:08,560 Speaker 1: it's been done for decades. So if you've ever chatted 303 00:19:08,600 --> 00:19:11,000 Speaker 1: with somebody and you're saying, why are we doing it 304 00:19:11,040 --> 00:19:13,080 Speaker 1: this way, and they say, it's because it's how we've 305 00:19:13,119 --> 00:19:15,600 Speaker 1: always done it, that's sort of the case with Fortran 306 00:19:15,840 --> 00:19:19,919 Speaker 1: in weather models. Uh, that's one of the reasons. But 307 00:19:20,440 --> 00:19:26,680 Speaker 1: it's also a very useful language that has evolved over time. 308 00:19:26,720 --> 00:19:29,680 Speaker 1: It's not like it was developed and then forgotten about. 309 00:19:29,800 --> 00:19:33,879 Speaker 1: It has received a lot of I hesitate to use 310 00:19:33,880 --> 00:19:37,679 Speaker 1: the word love, but development over the years. Now, this 311 00:19:37,760 --> 00:19:40,960 Speaker 1: four trend program is compiled into machine language. That's the 312 00:19:41,040 --> 00:19:43,920 Speaker 1: language that computers understand, and we'll talk more about that 313 00:19:44,000 --> 00:19:47,000 Speaker 1: in the Programming Languages episode that will be coming up soon. 314 00:19:47,440 --> 00:19:49,600 Speaker 1: So keeping the year out for those episodes, they should 315 00:19:49,600 --> 00:19:54,159 Speaker 1: be following this one shortly. The program, the weather model 316 00:19:54,240 --> 00:19:56,879 Speaker 1: takes all this information, the data fed to it from 317 00:19:56,960 --> 00:20:01,520 Speaker 1: multiple sources, all those observation stations, and all those equations 318 00:20:01,560 --> 00:20:04,920 Speaker 1: based off of fluid dynamics and thermodynamics, and steps through 319 00:20:05,040 --> 00:20:08,959 Speaker 1: in time to simulate what will happen next. So the 320 00:20:08,960 --> 00:20:13,399 Speaker 1: computer is actively trying to simulate the behavior of weather 321 00:20:13,440 --> 00:20:19,680 Speaker 1: patterns based upon our understanding of hydrodynamics, thermodynamics, and all 322 00:20:19,680 --> 00:20:25,120 Speaker 1: of these variables. So it's it's actively simulating the outcomes. 323 00:20:25,359 --> 00:20:29,679 Speaker 1: Now you're getting these simulations in the form of numeric answers. 324 00:20:29,720 --> 00:20:35,159 Speaker 1: It's not like you're looking at a graphic representation of weather. 325 00:20:35,240 --> 00:20:37,280 Speaker 1: You're not, you know, looking at your computer and you 326 00:20:37,320 --> 00:20:40,080 Speaker 1: see a massive storm is roiling across the screen. I'm 327 00:20:40,119 --> 00:20:42,880 Speaker 1: pretty sure that's how Hollywood would do it. But we're 328 00:20:42,880 --> 00:20:46,560 Speaker 1: talking more about lots of numbers, so not as sexy 329 00:20:46,960 --> 00:20:51,680 Speaker 1: as say, watching Twister on Netflix and you're saying, that's 330 00:20:51,720 --> 00:20:53,879 Speaker 1: what the way, what's the what the weather is going 331 00:20:53,920 --> 00:20:59,119 Speaker 1: to be? Uh, that's not exactly the case. Sadly, Maybe 332 00:20:59,119 --> 00:21:02,520 Speaker 1: one day we'll get there, but not not right now now. 333 00:21:02,560 --> 00:21:06,800 Speaker 1: Some simulations can project out for multiple days, and some 334 00:21:06,880 --> 00:21:10,560 Speaker 1: are more immediate. Some look at short range forecast, some 335 00:21:10,640 --> 00:21:13,439 Speaker 1: do mid range and long range as well, and the 336 00:21:13,480 --> 00:21:16,960 Speaker 1: highest amount of accuracy typically is within the next several hours. 337 00:21:17,400 --> 00:21:19,960 Speaker 1: And then, of course the further out you go from 338 00:21:20,040 --> 00:21:23,160 Speaker 1: the moment you gathered all that data and did your 339 00:21:23,200 --> 00:21:28,040 Speaker 1: diagnostic stuff, the more you are likely to diverge from 340 00:21:28,160 --> 00:21:32,639 Speaker 1: reality for your forecast, more uncertainty enters into the picture 341 00:21:32,760 --> 00:21:36,919 Speaker 1: because it's hard to predict how all of those different 342 00:21:37,000 --> 00:21:41,040 Speaker 1: variables are going to uh what what their state will 343 00:21:41,080 --> 00:21:43,000 Speaker 1: be at any given point in the future. And the 344 00:21:43,000 --> 00:21:46,000 Speaker 1: further out you go, the more uncertain you're going to be. Typically, 345 00:21:46,760 --> 00:21:49,280 Speaker 1: So let's make up a hypothetical situation to kind of 346 00:21:49,320 --> 00:21:53,560 Speaker 1: explain what I'm talking about here. Let's say I'm in 347 00:21:53,600 --> 00:21:57,000 Speaker 1: the north of wester Ross and I know winter is coming. 348 00:21:57,640 --> 00:22:00,760 Speaker 1: My weather forecast model is very much focused on atmospheric 349 00:22:00,800 --> 00:22:04,320 Speaker 1: movements from beyond the wall and less concerned with other 350 00:22:04,440 --> 00:22:09,320 Speaker 1: variables like maybe humidity or I don't know, dragons, because 351 00:22:09,400 --> 00:22:11,919 Speaker 1: humidity and dragons don't play such a large role in 352 00:22:11,920 --> 00:22:15,440 Speaker 1: the weather patterns of my region, right, I mean, I'm 353 00:22:15,480 --> 00:22:18,879 Speaker 1: one of the Starks in this hypothesis. The model I 354 00:22:18,920 --> 00:22:22,280 Speaker 1: have created is as accurate a representation of how patterns 355 00:22:22,320 --> 00:22:25,320 Speaker 1: emerge and behave in my region as I can get 356 00:22:25,320 --> 00:22:28,439 Speaker 1: my hands on. So that's what I rely upon. But 357 00:22:28,520 --> 00:22:32,800 Speaker 1: let's say you in your fancy pants King's landing house 358 00:22:33,240 --> 00:22:36,240 Speaker 1: are concerned with trade winds coming in from out over 359 00:22:36,280 --> 00:22:39,560 Speaker 1: the sea, because that's a large influencer of the weather 360 00:22:39,600 --> 00:22:43,440 Speaker 1: in your area. So your weather model takes that into 361 00:22:43,480 --> 00:22:46,320 Speaker 1: account and gives it greater weight than some of the 362 00:22:46,400 --> 00:22:49,159 Speaker 1: variables that I'm concerned with. And this is so that 363 00:22:49,240 --> 00:22:53,040 Speaker 1: you can create accurate forecasts for your area. Your model 364 00:22:53,080 --> 00:22:55,960 Speaker 1: and my model aren't equivalent. Your model would not work 365 00:22:55,960 --> 00:22:59,160 Speaker 1: as well in my region, and vice versa. And neither 366 00:22:59,240 --> 00:23:02,480 Speaker 1: model is comp inhensive, which means neither model covers all 367 00:23:02,520 --> 00:23:05,800 Speaker 1: of wester Roast. That's very much regional. Now in the 368 00:23:05,840 --> 00:23:11,320 Speaker 1: real world spoiler alert, Game of Thrones isn't real. We 369 00:23:11,480 --> 00:23:15,320 Speaker 1: sometimes find that our computer models are mostly good, but 370 00:23:15,480 --> 00:23:19,960 Speaker 1: not perfect. Some may, under certain conditions under or over 371 00:23:20,480 --> 00:23:23,399 Speaker 1: estimate the temperature for example. Now, this could happen for 372 00:23:23,520 --> 00:23:25,760 Speaker 1: lots of different reasons, such as, you might have a 373 00:23:25,800 --> 00:23:27,880 Speaker 1: region that's close to the ocean, and the ocean could 374 00:23:27,920 --> 00:23:31,040 Speaker 1: affect the temperature in ways that the model is not 375 00:23:31,320 --> 00:23:35,520 Speaker 1: quite capable of accounting for. So you might experience more 376 00:23:35,600 --> 00:23:38,960 Speaker 1: windy conditions than other areas, and the wind may affect 377 00:23:38,960 --> 00:23:42,720 Speaker 1: temperatures in ways that the model can anticipate. The wave 378 00:23:42,800 --> 00:23:46,880 Speaker 1: dynamics could affect whether in ways that the model can't anticipate, 379 00:23:47,960 --> 00:23:50,119 Speaker 1: so you still have meteorologists who are dealing with this, 380 00:23:50,200 --> 00:23:53,520 Speaker 1: and they're fudging the numbers a bit once they've been processed, 381 00:23:53,560 --> 00:23:58,080 Speaker 1: because you learn over time how well your model does 382 00:23:58,320 --> 00:24:02,119 Speaker 1: versus reality. So you can look at the results of 383 00:24:02,160 --> 00:24:05,600 Speaker 1: your model, what does the predictions say, and then you 384 00:24:05,640 --> 00:24:09,720 Speaker 1: can compare that against the actual results that you get 385 00:24:09,760 --> 00:24:12,080 Speaker 1: just by waiting around. Right, you wait around and you 386 00:24:12,119 --> 00:24:15,320 Speaker 1: see what actually happens. You compare that to the forecast 387 00:24:15,359 --> 00:24:17,960 Speaker 1: that your model gave, and you start to look and 388 00:24:18,000 --> 00:24:21,000 Speaker 1: see if there's any any adjustment that needs to be made, 389 00:24:21,080 --> 00:24:23,640 Speaker 1: or in some cases you may just say, well, this 390 00:24:23,680 --> 00:24:28,440 Speaker 1: model is frequently about two degrees warmer than what really happens, 391 00:24:28,520 --> 00:24:32,080 Speaker 1: so we're going to build in an adjustment. We will 392 00:24:32,119 --> 00:24:36,760 Speaker 1: automatically no to decrease the temperature forecast by two degrees 393 00:24:37,359 --> 00:24:40,000 Speaker 1: from this model, and that we are more likely to 394 00:24:40,240 --> 00:24:43,680 Speaker 1: hit on what the actual temperature will be. This happens 395 00:24:43,720 --> 00:24:46,560 Speaker 1: all the time with lots of computer models, not just 396 00:24:46,600 --> 00:24:50,879 Speaker 1: for temperature, but for other variables as well, and really 397 00:24:51,080 --> 00:24:53,320 Speaker 1: we should expect this to continue to happen because we 398 00:24:53,359 --> 00:24:57,320 Speaker 1: cannot have a perfect understanding of how everything is going 399 00:24:57,359 --> 00:25:00,919 Speaker 1: to be and builded into a computer, not yet, possibly 400 00:25:01,000 --> 00:25:05,320 Speaker 1: not ever. It is so complex and so dependent upon 401 00:25:05,359 --> 00:25:08,439 Speaker 1: so many different variables. But what we can do is 402 00:25:08,480 --> 00:25:13,480 Speaker 1: we can correct for those known problems. If we know 403 00:25:13,640 --> 00:25:15,639 Speaker 1: that there is an issue and it's not likely to 404 00:25:15,720 --> 00:25:18,200 Speaker 1: cause a ripple effect, which I'll talk about a little 405 00:25:18,200 --> 00:25:21,560 Speaker 1: bit later in this episode, then we can just correct 406 00:25:21,560 --> 00:25:23,880 Speaker 1: for it at the end and say, all right, let's 407 00:25:23,880 --> 00:25:26,240 Speaker 1: bump up or bump down the temperature by a couple 408 00:25:26,280 --> 00:25:29,359 Speaker 1: of degrees based upon our knowledge of how this model 409 00:25:29,480 --> 00:25:33,520 Speaker 1: performs against what really happens. It's kind of interesting because 410 00:25:33,520 --> 00:25:37,280 Speaker 1: it really nails home how computers are all about precision 411 00:25:37,320 --> 00:25:42,160 Speaker 1: and replication. They don't tend to give you vague guesses. 412 00:25:42,200 --> 00:25:47,080 Speaker 1: They can create different answers that have different probably probabilities 413 00:25:47,080 --> 00:25:50,760 Speaker 1: of being correct, and then choose whichever one is the 414 00:25:50,800 --> 00:25:57,280 Speaker 1: most likely to be correct. But they are about making 415 00:25:57,320 --> 00:26:03,440 Speaker 1: these precise uh outputs, and we as humans are the 416 00:26:03,480 --> 00:26:06,119 Speaker 1: ones who have to add extra levels of interpretation on 417 00:26:06,240 --> 00:26:09,200 Speaker 1: top of that, which means that there's still a human 418 00:26:09,240 --> 00:26:13,639 Speaker 1: being associated with this process. It's kind of what you 419 00:26:13,840 --> 00:26:16,440 Speaker 1: have to do with an old scale. If you had 420 00:26:16,440 --> 00:26:18,679 Speaker 1: an old like weight scale and it was off of 421 00:26:18,720 --> 00:26:22,159 Speaker 1: its calibration, you couldn't quite get it to reset at zero. 422 00:26:22,359 --> 00:26:24,240 Speaker 1: So let's say you notice that your scales giving a 423 00:26:24,320 --> 00:26:27,080 Speaker 1: reading that's always two pounds less than what it should be. 424 00:26:27,560 --> 00:26:29,560 Speaker 1: You know that when you weigh something, you need to 425 00:26:29,600 --> 00:26:32,800 Speaker 1: add two pounds to your scales reading whenever you weigh something. 426 00:26:33,119 --> 00:26:36,000 Speaker 1: Meteorologists will often do the same thing, and sometimes they'll 427 00:26:36,040 --> 00:26:39,080 Speaker 1: do it to just a very specific region within a 428 00:26:39,119 --> 00:26:42,480 Speaker 1: computer models area of coverage. They know that the model 429 00:26:42,520 --> 00:26:45,199 Speaker 1: has a history of under or overestimating things, and so 430 00:26:45,280 --> 00:26:49,199 Speaker 1: they just correct for it. Now, for that reason, we 431 00:26:49,280 --> 00:26:52,120 Speaker 1: still have human beings involved in meteorology and all these 432 00:26:52,119 --> 00:26:56,080 Speaker 1: different phases. Meteorologists use their training and expertise to interpret 433 00:26:56,119 --> 00:26:58,960 Speaker 1: the output from weather models, and they learned the quirks 434 00:26:58,960 --> 00:27:01,879 Speaker 1: of the models, even as new versions of those models 435 00:27:01,920 --> 00:27:05,120 Speaker 1: come out to correct for inaccuracies or to increase resolution 436 00:27:05,240 --> 00:27:09,120 Speaker 1: or frequency. So remember whether forecasting depends upon the quality 437 00:27:09,240 --> 00:27:12,440 Speaker 1: of the weather model, the accuracy of the information being 438 00:27:12,480 --> 00:27:15,840 Speaker 1: fed into the model, and the frequency with which that 439 00:27:15,880 --> 00:27:19,560 Speaker 1: information comes in, and the density of the observations within 440 00:27:19,600 --> 00:27:23,280 Speaker 1: that region. All of these things will affect the accuracy 441 00:27:23,400 --> 00:27:26,120 Speaker 1: of the ultimate weather forecast to come out of that 442 00:27:26,160 --> 00:27:29,320 Speaker 1: computer model. If you have the best model in the world, 443 00:27:29,720 --> 00:27:32,760 Speaker 1: it's still not going to give you a very accurate prediction. 444 00:27:33,000 --> 00:27:36,280 Speaker 1: If either you don't have enough observation stations so your 445 00:27:36,280 --> 00:27:41,400 Speaker 1: resolution is low, you aren't consulting your observation stations frequently enough, 446 00:27:41,880 --> 00:27:47,040 Speaker 1: so you are relying on older information, or the information 447 00:27:47,160 --> 00:27:50,480 Speaker 1: you're feeding into your model is somehow inaccurate. Let's say 448 00:27:50,480 --> 00:27:53,480 Speaker 1: that you have some sensors that aren't working properly and 449 00:27:53,520 --> 00:27:58,280 Speaker 1: are giving you, uh the wrong reading for some element here, 450 00:27:58,640 --> 00:28:02,439 Speaker 1: whether it's air press or temperature, whatever it might be. Well, 451 00:28:02,520 --> 00:28:05,240 Speaker 1: if that information gets fed into your computer model, then 452 00:28:05,520 --> 00:28:07,960 Speaker 1: you would expect that the outcome is not going to 453 00:28:07,960 --> 00:28:11,080 Speaker 1: be accurate because it wasn't accurate information going in, or, 454 00:28:11,359 --> 00:28:16,440 Speaker 1: as some people say, garbage in garbage out. Your outcomes 455 00:28:16,440 --> 00:28:18,520 Speaker 1: are only going to be as good as your data, 456 00:28:19,680 --> 00:28:21,240 Speaker 1: as well as the fact that you have to worry 457 00:28:21,280 --> 00:28:24,800 Speaker 1: about the the quality of your model itself. Now, if 458 00:28:24,800 --> 00:28:28,000 Speaker 1: this sounds like it's a ton of processing, it is. 459 00:28:28,400 --> 00:28:31,480 Speaker 1: Traditionally one of the big applications we have for supercomputers 460 00:28:31,680 --> 00:28:35,040 Speaker 1: is for weather models. So whenever you hear about supercomputers 461 00:28:35,080 --> 00:28:39,480 Speaker 1: and the massive amounts of processing power they have. Often 462 00:28:39,960 --> 00:28:45,560 Speaker 1: these computers are being put towards the task of simulating weather, 463 00:28:45,680 --> 00:28:48,720 Speaker 1: taking these weather models and trying to get more and 464 00:28:48,760 --> 00:28:53,440 Speaker 1: more accurate simulations of what is going to happen. ANIAC 465 00:28:53,480 --> 00:28:56,280 Speaker 1: itself was used to generate weather forecasts. But even though 466 00:28:56,400 --> 00:28:58,800 Speaker 1: NIAC was a big jump forward on just working out 467 00:28:58,800 --> 00:29:01,680 Speaker 1: the equations by hand, it was still limited and the 468 00:29:01,720 --> 00:29:05,600 Speaker 1: weather model wasn't much more than a barotropic equation. A 469 00:29:05,680 --> 00:29:09,480 Speaker 1: barotropic equation is a fluid dynamics problem in which density 470 00:29:09,520 --> 00:29:13,800 Speaker 1: is a function of pressure only. Even this limited interpretation 471 00:29:13,840 --> 00:29:16,280 Speaker 1: of the factors that affect weather was still a big 472 00:29:16,400 --> 00:29:20,280 Speaker 1: leap forward. However, any act success led to the development 473 00:29:20,320 --> 00:29:24,040 Speaker 1: of new models, including multi level models, and one such 474 00:29:24,080 --> 00:29:27,240 Speaker 1: model was the product of several scientists work in the 475 00:29:28,040 --> 00:29:31,520 Speaker 1: in the in the wake of a massive storm system 476 00:29:31,560 --> 00:29:35,200 Speaker 1: that took place on Thanksgiving Day in nineteen fifty. So 477 00:29:35,320 --> 00:29:39,280 Speaker 1: this big storm ended up being a great opportunity for 478 00:29:39,360 --> 00:29:42,000 Speaker 1: the scientists who were trying to make a weather model, 479 00:29:42,040 --> 00:29:46,360 Speaker 1: and the model they developed seemed to simulate actual events accurately. 480 00:29:46,400 --> 00:29:48,840 Speaker 1: They were very excited. This multi level model appeared to 481 00:29:48,840 --> 00:29:51,880 Speaker 1: be much more accurate than the barotropic model that had 482 00:29:51,920 --> 00:29:55,120 Speaker 1: been used as it turned out their model was only 483 00:29:55,160 --> 00:29:59,080 Speaker 1: really accurate for that one set of circumstances. They found 484 00:29:59,080 --> 00:30:03,160 Speaker 1: that as they ran more simulations, it was not giving 485 00:30:03,200 --> 00:30:08,880 Speaker 1: accurate forecasts, at least not in every situation. So it 486 00:30:08,920 --> 00:30:12,880 Speaker 1: turned out that that weather model was really great for 487 00:30:13,040 --> 00:30:16,640 Speaker 1: one set of circumstances, but it didn't handle other ones 488 00:30:17,160 --> 00:30:19,920 Speaker 1: nearly as well. It didn't get nearly as accurate a result, 489 00:30:20,440 --> 00:30:23,840 Speaker 1: and the barotropic model actually was superior. The older model 490 00:30:23,880 --> 00:30:25,880 Speaker 1: that had been running on any act was superior to 491 00:30:26,000 --> 00:30:29,640 Speaker 1: the multi level model, at least in some situations. So 492 00:30:29,760 --> 00:30:32,840 Speaker 1: throughout the nineteen fifties, meteorologists were mostly relying on this 493 00:30:32,920 --> 00:30:37,000 Speaker 1: older barotropic model because it was more accurate more often 494 00:30:37,520 --> 00:30:41,440 Speaker 1: than multi level models that had been proposed. Starting in 495 00:30:41,520 --> 00:30:44,719 Speaker 1: ninety eight, multi level models began to gain more acceptance 496 00:30:44,760 --> 00:30:47,280 Speaker 1: as they tuned into the right waitings for the various 497 00:30:47,320 --> 00:30:50,800 Speaker 1: variables and weather forecasting, and from that point forward we 498 00:30:50,840 --> 00:30:55,360 Speaker 1: saw more varieties of weather models arise, each with its 499 00:30:55,400 --> 00:30:59,960 Speaker 1: own pros and cons, And what followed were numerous symposes 500 00:31:00,360 --> 00:31:03,440 Speaker 1: about computational models and the machines that would be needed 501 00:31:03,440 --> 00:31:05,840 Speaker 1: to crunch the numbers in a reasonable amount of time. 502 00:31:05,880 --> 00:31:11,320 Speaker 1: And it gets super duper technical. Now today we have 503 00:31:11,480 --> 00:31:14,880 Speaker 1: many models, most of them covering specific regions. Creating a 504 00:31:14,960 --> 00:31:19,040 Speaker 1: global weather model is an enormous task, and not just 505 00:31:19,120 --> 00:31:22,200 Speaker 1: to combine our understanding of weather behavior from around the globe, 506 00:31:22,480 --> 00:31:25,040 Speaker 1: but also to find a computer capable of processing such 507 00:31:25,080 --> 00:31:28,040 Speaker 1: an enormous amount of data regularly enough to give us 508 00:31:28,040 --> 00:31:30,720 Speaker 1: an accurate weather forecast at any given time for any 509 00:31:30,760 --> 00:31:34,120 Speaker 1: given location. But here's some of the models that we 510 00:31:34,200 --> 00:31:36,800 Speaker 1: use today. One of the big ones is the European 511 00:31:36,880 --> 00:31:41,760 Speaker 1: Center for Medium Range Weather Forecasts or e c MWF. 512 00:31:42,320 --> 00:31:44,760 Speaker 1: They provide one of the more important models in the world. 513 00:31:45,160 --> 00:31:48,600 Speaker 1: The Journal of Computational Physics describes the model as quote 514 00:31:49,120 --> 00:31:54,240 Speaker 1: a spectral primitive equation model with a semi Lagrangian, semi 515 00:31:54,280 --> 00:31:58,400 Speaker 1: implicit time scheme and a comprehensive treatment of physical processes. 516 00:32:00,120 --> 00:32:04,080 Speaker 1: I'm pretty sure that means it can summon cthulhu. In addition, 517 00:32:04,400 --> 00:32:07,440 Speaker 1: this model is coupled with an ocean wave model, and 518 00:32:07,600 --> 00:32:11,640 Speaker 1: the basis is the Integrated Forecast System or i f S. 519 00:32:12,000 --> 00:32:15,680 Speaker 1: The model runs on high performance supercomputers capable of performing 520 00:32:15,720 --> 00:32:19,520 Speaker 1: several terra flops of calculations, and just a reminder, a 521 00:32:19,640 --> 00:32:24,240 Speaker 1: flop is a floating point operations per second. So generally speaking, 522 00:32:24,480 --> 00:32:26,640 Speaker 1: the number of flops the computer can perform gives you 523 00:32:26,640 --> 00:32:30,040 Speaker 1: an idea of its processing power or speed, and terra 524 00:32:30,120 --> 00:32:34,480 Speaker 1: flops means a lot. All Right, we're in the home 525 00:32:34,600 --> 00:32:38,960 Speaker 1: stretch for meteorology. But before we jump into that final section, 526 00:32:39,040 --> 00:32:49,400 Speaker 1: let's take another quick break to thank our sponsor. Alright, 527 00:32:49,440 --> 00:32:52,920 Speaker 1: I just talked about the weather model over the big 528 00:32:52,960 --> 00:32:55,800 Speaker 1: one over in Europe, and keep in mind there are 529 00:32:56,120 --> 00:32:59,040 Speaker 1: dozens of weather models, but over here in the good 530 00:32:59,040 --> 00:33:01,360 Speaker 1: old US of A, you've got a big one with 531 00:33:01,400 --> 00:33:05,360 Speaker 1: the National Centers for Environmental Prediction or in c e 532 00:33:05,480 --> 00:33:09,600 Speaker 1: P as it is known, and it has a globally 533 00:33:09,680 --> 00:33:13,880 Speaker 1: gridded set of data about the state of the Earth's atmosphere. 534 00:33:13,960 --> 00:33:16,520 Speaker 1: And there are tons of other models too. As I 535 00:33:16,560 --> 00:33:19,400 Speaker 1: was just saying, some of them are more localized than others. 536 00:33:19,640 --> 00:33:22,400 Speaker 1: Some of them are capable of much higher resolution because 537 00:33:22,400 --> 00:33:25,840 Speaker 1: they consult more observation systems with respect the area covered 538 00:33:25,920 --> 00:33:29,840 Speaker 1: by the model, and those grids are important. You want 539 00:33:29,960 --> 00:33:33,800 Speaker 1: um smaller grids, You want the the sides of each 540 00:33:34,440 --> 00:33:36,320 Speaker 1: of the grids. To keep in mind, this is three dimensional. 541 00:33:36,480 --> 00:33:40,680 Speaker 1: It's not just um land area, but elevation as well. 542 00:33:41,760 --> 00:33:46,120 Speaker 1: You want smaller grids because that increases that resolution, right, 543 00:33:46,720 --> 00:33:50,600 Speaker 1: because each grid represents an area where you understand what 544 00:33:50,800 --> 00:33:55,360 Speaker 1: is going on inside of that area. The smaller you 545 00:33:55,400 --> 00:33:58,040 Speaker 1: make the grids, the higher the resolution is. This is 546 00:33:58,120 --> 00:34:02,760 Speaker 1: again a lot like your television or computer display. If 547 00:34:02,800 --> 00:34:04,880 Speaker 1: you make a picture out of just a few pixels, 548 00:34:05,000 --> 00:34:08,000 Speaker 1: it will be blocky. It has very low resolution. Like 549 00:34:08,080 --> 00:34:10,960 Speaker 1: think about eight bit graphics back in the day. Every 550 00:34:11,040 --> 00:34:13,239 Speaker 1: all the characters on video games were made up of 551 00:34:13,280 --> 00:34:16,600 Speaker 1: these blocks. They all had very jagged edges. It was 552 00:34:16,640 --> 00:34:19,880 Speaker 1: not a high resolution. If you use more pixels to 553 00:34:19,960 --> 00:34:24,240 Speaker 1: make your photo, that improves the resolution to a point. Anyway, 554 00:34:24,280 --> 00:34:26,600 Speaker 1: there gets to a point where we can't really perceive 555 00:34:26,640 --> 00:34:28,919 Speaker 1: it anymore, but you certainly can perceive it at those 556 00:34:28,920 --> 00:34:32,920 Speaker 1: early stages. So the smaller, the smaller, and more numerous 557 00:34:32,960 --> 00:34:37,480 Speaker 1: the pixels, the higher the resolution is and the higher 558 00:34:37,560 --> 00:34:41,080 Speaker 1: quality you get of an image up to a certain point. 559 00:34:41,800 --> 00:34:44,600 Speaker 1: The same is true for weather models. So if you 560 00:34:44,640 --> 00:34:48,120 Speaker 1: have a grid with small squares, such as on the 561 00:34:48,239 --> 00:34:51,759 Speaker 1: order of a few kilometers per side, you would have 562 00:34:51,800 --> 00:34:54,560 Speaker 1: a high resolution, and those grid points can have a 563 00:34:54,600 --> 00:35:00,480 Speaker 1: single value per atmospheric variable per observation. So another words, 564 00:35:00,520 --> 00:35:04,960 Speaker 1: you get a value for temperature, a value for wind direction, 565 00:35:05,000 --> 00:35:09,120 Speaker 1: a value for wind speed, a value for atmospheric density, 566 00:35:09,160 --> 00:35:13,520 Speaker 1: et cetera. Uh, all of that tends to be consulted 567 00:35:13,560 --> 00:35:18,040 Speaker 1: about once an hour with most of these models. So 568 00:35:18,080 --> 00:35:21,640 Speaker 1: once an hour you pull all that observational data for 569 00:35:21,760 --> 00:35:27,480 Speaker 1: every square or cube if you prefer, within that grid. 570 00:35:28,480 --> 00:35:30,600 Speaker 1: So you pull all of the information for all of 571 00:35:30,640 --> 00:35:34,759 Speaker 1: the grids within that area or all the cubes within 572 00:35:34,800 --> 00:35:37,319 Speaker 1: that grid, and you crunch the numbers from all of 573 00:35:37,320 --> 00:35:42,960 Speaker 1: that to see how weather will progress from that moment forward. Now, 574 00:35:43,000 --> 00:35:47,040 Speaker 1: if one variable from one grid is way off, it 575 00:35:47,080 --> 00:35:50,760 Speaker 1: can cause bigger errors and forecasts further down the line. 576 00:35:50,880 --> 00:35:53,080 Speaker 1: That garbage in, garbage out thing I was talking about, 577 00:35:53,120 --> 00:35:57,320 Speaker 1: and this is the infamous butterfly effect. The butterfly effect 578 00:35:57,400 --> 00:36:00,000 Speaker 1: refers to a small effect that can have much larger 579 00:36:00,160 --> 00:36:04,080 Speaker 1: consequences further on in time, and you've probably heard about 580 00:36:04,120 --> 00:36:07,440 Speaker 1: the effect before. The classic example is that you have 581 00:36:07,480 --> 00:36:10,600 Speaker 1: a butterfly flapping its wings in South America and the 582 00:36:10,640 --> 00:36:13,400 Speaker 1: force from the breeze generated from the flapping ends up 583 00:36:13,440 --> 00:36:16,239 Speaker 1: contributing to a system that eventually grows in power and 584 00:36:16,320 --> 00:36:20,400 Speaker 1: ultimately culminates in a massive typhoon in Asia, for example. 585 00:36:21,239 --> 00:36:24,359 Speaker 1: Now that's just a thought experiment, obviously, but with weather 586 00:36:24,440 --> 00:36:27,480 Speaker 1: models something similar can happen. If you have a large 587 00:36:27,520 --> 00:36:30,440 Speaker 1: grid and each square in the grid is representing a 588 00:36:30,480 --> 00:36:34,440 Speaker 1: relatively small area, and one of those areas within that 589 00:36:34,520 --> 00:36:39,359 Speaker 1: grid produces data that doesn't reflect real conditions, your forecasts 590 00:36:39,440 --> 00:36:42,360 Speaker 1: will be affected by this. Now, depending upon the weight 591 00:36:42,480 --> 00:36:45,240 Speaker 1: of the variable in question, it could make the entire 592 00:36:45,320 --> 00:36:49,800 Speaker 1: forecast inaccurate after a certain amount of time. Generally speaking, 593 00:36:49,880 --> 00:36:52,120 Speaker 1: the further out you go in time, the more you 594 00:36:52,160 --> 00:36:56,200 Speaker 1: have to depend upon numerical forecast models. A short term 595 00:36:56,280 --> 00:36:59,680 Speaker 1: forecast might not require a full numerical analysis. It could 596 00:36:59,760 --> 00:37:02,480 Speaker 1: depend and more on what's going on right now and 597 00:37:02,520 --> 00:37:05,160 Speaker 1: the likelihood of how weather will change over the next 598 00:37:05,239 --> 00:37:08,759 Speaker 1: few hours, so you can refer more on experience in 599 00:37:08,760 --> 00:37:12,520 Speaker 1: those cases. Beyond that, however, you'll need some more numerical 600 00:37:12,560 --> 00:37:15,319 Speaker 1: analysis to get a better than UH to do better 601 00:37:15,360 --> 00:37:18,800 Speaker 1: than just giving a wild guess. So even so, you 602 00:37:18,880 --> 00:37:21,120 Speaker 1: might run the data through a couple of different models 603 00:37:21,200 --> 00:37:24,160 Speaker 1: to look at potential forecasts, and from that point and experience, 604 00:37:24,239 --> 00:37:27,920 Speaker 1: meteorologists might look over the data to see which predictions 605 00:37:27,960 --> 00:37:31,600 Speaker 1: appeared to be the most realistic. Sometimes computer models get 606 00:37:31,640 --> 00:37:35,439 Speaker 1: stuff wrong. They might predict an extremely unlikely outcome. Other 607 00:37:35,480 --> 00:37:39,680 Speaker 1: models might have a very different forecast. The meteorologist has 608 00:37:39,719 --> 00:37:42,719 Speaker 1: to determine which of these outcomes best represents what is 609 00:37:42,760 --> 00:37:46,160 Speaker 1: likely to actually happen, based upon how well they seem 610 00:37:46,200 --> 00:37:49,960 Speaker 1: to handle the current weather situations. So you might look 611 00:37:49,960 --> 00:37:51,920 Speaker 1: at a computer model and say, well, how is it 612 00:37:51,960 --> 00:37:54,839 Speaker 1: handling what's going on right now. If it's doing that well, 613 00:37:55,640 --> 00:37:58,960 Speaker 1: then we can at least lay some assumptions that the 614 00:37:59,120 --> 00:38:02,560 Speaker 1: any predictions coming from this computer model are going to 615 00:38:02,719 --> 00:38:05,960 Speaker 1: at least be semi accurate. If it's not handling it well, 616 00:38:06,000 --> 00:38:08,120 Speaker 1: then we may need to consult a different weather model 617 00:38:08,160 --> 00:38:12,600 Speaker 1: for this particular forecast. Now, if observation data is affected, 618 00:38:13,000 --> 00:38:15,360 Speaker 1: as in, if there are problems with sensors, then the 619 00:38:15,400 --> 00:38:17,520 Speaker 1: information you will get out of the models will not 620 00:38:17,719 --> 00:38:20,600 Speaker 1: be dependable. The high resolution can help smooth this out. 621 00:38:21,160 --> 00:38:25,200 Speaker 1: So if you're getting one sensor with erroneous data, but 622 00:38:25,280 --> 00:38:28,080 Speaker 1: you've got lots of other sensors in the area, you 623 00:38:28,120 --> 00:38:31,160 Speaker 1: could possibly smooth that out. You might say, well, this 624 00:38:31,239 --> 00:38:36,000 Speaker 1: is clearly an anomaly. If most of your observation stations 625 00:38:36,000 --> 00:38:40,480 Speaker 1: are reporting that the temperature is about seventy degrees fahrenheit, 626 00:38:41,120 --> 00:38:44,520 Speaker 1: and one of them is saying it's degrees fahrenheit. You 627 00:38:44,520 --> 00:38:47,680 Speaker 1: could say, well, this one clearly there's an anomaly. Maybe 628 00:38:47,719 --> 00:38:50,440 Speaker 1: something is going on in that area. Maybe it's close 629 00:38:50,520 --> 00:38:54,200 Speaker 1: to a fire or something not too close but fairly close. 630 00:38:54,239 --> 00:38:59,040 Speaker 1: Because pretty off the track for everything else, you can 631 00:38:59,160 --> 00:39:03,960 Speaker 1: perhaps week your model to ignore that particular sensor so 632 00:39:04,000 --> 00:39:07,160 Speaker 1: that way it doesn't affect the rest of your forecast 633 00:39:07,560 --> 00:39:12,080 Speaker 1: and throw things into disarray. But if you have just 634 00:39:12,239 --> 00:39:16,160 Speaker 1: very few observation stations, then the loss of even one 635 00:39:16,560 --> 00:39:20,040 Speaker 1: could be enough to throw your forecast off anyway, So 636 00:39:20,280 --> 00:39:22,880 Speaker 1: you may end up having your forecast thrown off either 637 00:39:23,000 --> 00:39:26,920 Speaker 1: because a sensor is giving you incorrect information or because 638 00:39:26,960 --> 00:39:30,440 Speaker 1: without that sensor you don't have enough information to build 639 00:39:30,600 --> 00:39:36,439 Speaker 1: a reliable prediction. So it's a delicate thing. Meteorologists also 640 00:39:36,480 --> 00:39:38,960 Speaker 1: have to keep up with what is actually happening at 641 00:39:38,960 --> 00:39:41,319 Speaker 1: any given time, and this seems pretty evident, but it's 642 00:39:41,320 --> 00:39:44,200 Speaker 1: important to either verify that a computer model is in 643 00:39:44,239 --> 00:39:48,080 Speaker 1: fact forecasting, whether accurately or if it's off track. And 644 00:39:48,120 --> 00:39:51,000 Speaker 1: we learn more from our mistakes than we do from successes. 645 00:39:51,719 --> 00:39:55,600 Speaker 1: Just like in that example of that first multi level model, 646 00:39:55,960 --> 00:39:58,360 Speaker 1: the scientist thought that they had really hit on something 647 00:39:58,480 --> 00:40:01,520 Speaker 1: because their models seemed to handle the conditions of that 648 00:40:01,560 --> 00:40:06,520 Speaker 1: Thanksgiving Day storm and give very realistic outcomes, But as 649 00:40:06,560 --> 00:40:09,720 Speaker 1: it turned out, it wasn't good at handling other situations. 650 00:40:10,960 --> 00:40:14,480 Speaker 1: If we succeed, we might think that what we've done 651 00:40:14,600 --> 00:40:16,919 Speaker 1: is perfect, that it's worked, but it may turn out 652 00:40:16,960 --> 00:40:19,720 Speaker 1: that that's not the case. When we fail, we realize, oh, 653 00:40:19,840 --> 00:40:22,400 Speaker 1: something is not quite right here. We need to figure 654 00:40:22,400 --> 00:40:24,839 Speaker 1: out what that is and how to correct for it. 655 00:40:25,200 --> 00:40:27,760 Speaker 1: This is true, by the way, in all areas of science, 656 00:40:28,040 --> 00:40:30,560 Speaker 1: we learn more from our failures than we do from 657 00:40:30,560 --> 00:40:35,319 Speaker 1: our successes. As computers get more powerful, the simulations can 658 00:40:35,360 --> 00:40:38,040 Speaker 1: take in more data and in theory, will generate more 659 00:40:38,080 --> 00:40:41,719 Speaker 1: accurate forecasts. The addition of some other elements, such as 660 00:40:41,760 --> 00:40:45,279 Speaker 1: deep learning algorithms that can assign probabilities to outcomes, might 661 00:40:45,360 --> 00:40:50,640 Speaker 1: also help. These probabilistic models assigned statistical probabilities to various outcomes, 662 00:40:50,760 --> 00:40:54,839 Speaker 1: letting meteorologists or even an artificially intelligent program determine which 663 00:40:54,880 --> 00:40:58,840 Speaker 1: one most likely represents what is really going to happen. 664 00:40:59,640 --> 00:41:02,879 Speaker 1: But some challenges will remain. I'd like to end this 665 00:41:02,920 --> 00:41:07,120 Speaker 1: episode by quoting from the second edition of Introduction to 666 00:41:07,280 --> 00:41:11,880 Speaker 1: Tropical Meteorology to illustrate just how dann complex this is, 667 00:41:12,480 --> 00:41:17,280 Speaker 1: and I quote from chapter nine of said textbook, Tropical 668 00:41:17,320 --> 00:41:21,160 Speaker 1: weather is difficult to forecast. Mid latitude weather is dominated 669 00:41:21,200 --> 00:41:24,719 Speaker 1: by synoptic systems moving in the westerly's, which formed the 670 00:41:24,760 --> 00:41:27,840 Speaker 1: basis for the weather analysis methods developed in the nineteenth 671 00:41:27,840 --> 00:41:32,680 Speaker 1: and twentieth centuries. In the mid latitudes, baro clinic instability 672 00:41:32,760 --> 00:41:37,200 Speaker 1: results from air masses with contrasting temperature and density. Their 673 00:41:37,320 --> 00:41:41,160 Speaker 1: energy is concentrated in extra tropical cyclones that can be 674 00:41:41,239 --> 00:41:45,440 Speaker 1: tracked fairly easily. By comparison, the tropics have a relatively 675 00:41:45,520 --> 00:41:50,000 Speaker 1: homogeneous air mass and fairly uniform distribution of surface temperature 676 00:41:50,000 --> 00:41:54,640 Speaker 1: and pressure. Therefore, local and mesoscale effects are more dominant 677 00:41:54,640 --> 00:41:59,920 Speaker 1: than synoptic influences, except for tropical cyclones. For example, service 678 00:42:00,000 --> 00:42:04,000 Speaker 1: temperature and pressure can change quickly with convection and sea breezes. 679 00:42:04,760 --> 00:42:09,360 Speaker 1: So you see, we cannot We cannot apply what works 680 00:42:09,400 --> 00:42:13,759 Speaker 1: for one area across all areas of Earth because it 681 00:42:13,880 --> 00:42:17,719 Speaker 1: just doesn't work that way. There are some areas where 682 00:42:17,719 --> 00:42:21,960 Speaker 1: things that are of a major impact on weather patterns 683 00:42:22,120 --> 00:42:30,200 Speaker 1: are almost non factors, and so until we develop very 684 00:42:30,280 --> 00:42:36,800 Speaker 1: specific particular ways of observing, measuring, and predicting weather across 685 00:42:36,840 --> 00:42:40,120 Speaker 1: all of Earth, and then synthesizing that so that we 686 00:42:40,160 --> 00:42:46,520 Speaker 1: can give global weather forecasts that can then narrow down 687 00:42:46,560 --> 00:42:50,040 Speaker 1: to the hyperlocal area, we're going to continue to have. 688 00:42:50,080 --> 00:42:55,160 Speaker 1: This uncertainty is a phenomenal area of science. It is 689 00:42:55,800 --> 00:43:00,240 Speaker 1: remarkable to see technology applied to that area of sience 690 00:43:00,280 --> 00:43:03,719 Speaker 1: in such a way that is easy to illustrate the 691 00:43:03,760 --> 00:43:10,359 Speaker 1: importance of pushing back the barriers of computational power. It's 692 00:43:10,440 --> 00:43:13,520 Speaker 1: why a lot of people look at stuff like Moore's 693 00:43:13,600 --> 00:43:17,280 Speaker 1: lawns said say, it's really important that we keep that going, 694 00:43:17,400 --> 00:43:19,640 Speaker 1: even though it's getting harder and harder to keep Moore's 695 00:43:19,719 --> 00:43:26,400 Speaker 1: law relevant, because we do have these needs for heavy 696 00:43:26,440 --> 00:43:34,040 Speaker 1: computational loads that have real effects and and and powerful 697 00:43:34,080 --> 00:43:37,480 Speaker 1: outcomes for billions of people on this planet. I mean, 698 00:43:38,080 --> 00:43:45,160 Speaker 1: accurate weather forecasts have the the potential to affect or 699 00:43:45,200 --> 00:43:51,920 Speaker 1: to save people from calamity, or to help businesses determine 700 00:43:52,040 --> 00:43:56,120 Speaker 1: when and where they're going to transport goods to get 701 00:43:56,160 --> 00:44:00,359 Speaker 1: to people more effectively, thus reducing lots of things like 702 00:44:00,520 --> 00:44:05,040 Speaker 1: environmental impact and the economic impact. You start to see 703 00:44:05,760 --> 00:44:10,200 Speaker 1: how intrinsic our weather is to everything else we do. 704 00:44:11,080 --> 00:44:13,920 Speaker 1: It's more than just small talk, and it's more than 705 00:44:13,960 --> 00:44:16,720 Speaker 1: just whether or not you need to grab your umbrella 706 00:44:16,800 --> 00:44:19,560 Speaker 1: before you leave the house. And I hope that these 707 00:44:19,600 --> 00:44:23,120 Speaker 1: episodes were interesting to you. I find meteorology to be 708 00:44:23,280 --> 00:44:26,680 Speaker 1: absolutely fascinating and I would love to learn more about it. 709 00:44:26,719 --> 00:44:30,319 Speaker 1: And I love chatting with meteorologists because even though they 710 00:44:30,360 --> 00:44:33,440 Speaker 1: can get super technical and they can really talk about 711 00:44:33,520 --> 00:44:37,719 Speaker 1: some heavy duty math that I can sometimes kind of 712 00:44:37,760 --> 00:44:43,799 Speaker 1: sort of follow. There dedication to understanding something so complex 713 00:44:43,960 --> 00:44:48,960 Speaker 1: I find inspiring. Now, our next episode will be about 714 00:44:49,040 --> 00:44:52,040 Speaker 1: the history of programming languages. It's likely going to be 715 00:44:52,080 --> 00:44:55,440 Speaker 1: a two part episode, and I'm going to study the 716 00:44:55,520 --> 00:44:59,360 Speaker 1: origin and evolution of computer languages. But if you have 717 00:44:59,440 --> 00:45:04,080 Speaker 1: suggestions for future episodes of tech Stuff, please share them 718 00:45:04,120 --> 00:45:06,680 Speaker 1: with me. You can send me an email. The address 719 00:45:06,840 --> 00:45:10,160 Speaker 1: is tech Stuff at how stuff works dot com, or 720 00:45:10,200 --> 00:45:12,520 Speaker 1: you can drop me a line on Facebook or Twitter. 721 00:45:12,640 --> 00:45:15,840 Speaker 1: The handle of both of those is tech Stuff hs W. 722 00:45:16,640 --> 00:45:19,279 Speaker 1: You can always drop in and watch me record these 723 00:45:19,280 --> 00:45:23,360 Speaker 1: episodes live that you can find at Twitch dot tv, 724 00:45:23,600 --> 00:45:27,920 Speaker 1: slash tech Stuff. I record on Wednesdays and Friday's today. 725 00:45:27,960 --> 00:45:30,080 Speaker 1: If you had joined us in the studio, you would 726 00:45:30,080 --> 00:45:32,360 Speaker 1: see that I'm in a brand new studio, or at 727 00:45:32,440 --> 00:45:34,279 Speaker 1: least a different one from the one I'm usually in, 728 00:45:34,920 --> 00:45:37,880 Speaker 1: and that Dylan has been separated from me by a 729 00:45:38,000 --> 00:45:42,799 Speaker 1: pain of soundproof glass, as nature intended. And I hope 730 00:45:42,840 --> 00:45:44,799 Speaker 1: that you guys can join me for future episodes. Just 731 00:45:44,840 --> 00:45:47,239 Speaker 1: go to twitch dot tv slash tech Stuff and you'll 732 00:45:47,280 --> 00:45:50,600 Speaker 1: find a schedule right then and there, and I'll talk 733 00:45:50,640 --> 00:45:59,520 Speaker 1: to you guys again really soon for more on this 734 00:45:59,680 --> 00:46:01,920 Speaker 1: and of sense of other topics. Is it how stuff 735 00:46:01,920 --> 00:46:12,320 Speaker 1: works dot com, wh