WEBVTT - How do we predict the weather? 0:00:07.760 --> 0:00:10.640 When I moved to southern California, I felt this immediate, 0:00:10.760 --> 0:00:13.680 immense relief, not just because I was free of the 0:00:13.720 --> 0:00:17.400 tyranny of outside clothing, but because I was released from 0:00:17.400 --> 0:00:20.119 the anxiety of not knowing if the weather was going 0:00:20.200 --> 0:00:23.400 to ruin my plans. Were you planning an outdoor birthday 0:00:23.400 --> 0:00:26.520 party for your toddler, No need to make backup plans 0:00:26.600 --> 0:00:28.920 just in case it rains. Do you need to drive 0:00:28.920 --> 0:00:31.800 a few hours away, No problem. You don't have to 0:00:31.800 --> 0:00:35.800 worry that a snowstorm might make the roads impassable because 0:00:35.960 --> 0:00:38.680 I could predict the weather myself since it was the 0:00:38.680 --> 0:00:41.519 same every single day. But not all of us are 0:00:41.640 --> 0:00:44.120 lucky enough to live in such calm climbs, so it's 0:00:44.159 --> 0:00:47.519 still very important that we try to anticipate storms so 0:00:47.560 --> 0:00:50.639 that they're less fortunate among us can be prepared. It's 0:00:50.680 --> 0:00:54.600 not often described as important physics, but predicting the weather 0:00:54.720 --> 0:00:58.280 is one of physics' great success stories. John Martin, professor 0:00:58.320 --> 0:01:01.720 of atmospheric oceanic sciences, told me that weather predictions are 0:01:01.800 --> 0:01:05.520 quote the most unheralded scientific advance of the second half 0:01:05.560 --> 0:01:07.960 of the twentieth century. If you keep score every day, 0:01:08.000 --> 0:01:10.520 I can't believe how well we predict the weather three 0:01:10.520 --> 0:01:13.720 to five days in advance. In thirty years, we've gone 0:01:13.760 --> 0:01:16.320 from predictions from one to two days to now five 0:01:16.360 --> 0:01:21.399 to seven days. We have made unbelievable progress. So how 0:01:21.440 --> 0:01:24.720 does that all work? What is the physics underlying the weather, 0:01:24.760 --> 0:01:27.080 why has it gotten better? And what can we expect 0:01:27.120 --> 0:01:29.800 into the future. I talked to Professor Martin and my 0:01:29.840 --> 0:01:33.120 good friend Professor Jane Baldwin here at UC Irvine about 0:01:33.160 --> 0:01:35.840 how the weather all works. So we'll dig into all 0:01:35.880 --> 0:01:38.720 of that in today's episode, dedicated to all of y'all 0:01:38.760 --> 0:01:42.960 who still experience regular weather. Welcome to Daniel and Kelly's 0:01:43.040 --> 0:01:44.960 extraordinarily sunny universe. 0:01:58.160 --> 0:02:02.320 Hello Kelly Leadersmith. I studied fites and space and I 0:02:02.400 --> 0:02:03.840 love rainy days. 0:02:05.720 --> 0:02:05.920 Hi. 0:02:06.000 --> 0:02:08.840 I'm Daniel. I'm a particle physicist, and I can predict 0:02:08.840 --> 0:02:11.400 the weather in California for the next one hundred years 0:02:11.400 --> 0:02:12.600 with my eyes closed. 0:02:13.080 --> 0:02:16.160 How boring, how massively dull. 0:02:16.280 --> 0:02:20.120 How wonderfully, delightfully, predictably, reliably boring. 0:02:20.480 --> 0:02:23.560 Oh, you know, one of my favorite weather moments, I 0:02:23.600 --> 0:02:26.640 have to admit, was a southern California morning. So I 0:02:26.760 --> 0:02:29.160 was a visiting scholar at the University of California, Santa 0:02:29.160 --> 0:02:31.520 Barbara for a little while, and I had an office 0:02:31.600 --> 0:02:34.200 that was like right out on the ocean. Was amazing. 0:02:34.200 --> 0:02:36.560 And when I was driving in one day, there was 0:02:36.760 --> 0:02:39.240 just a little bit of water on the ground and 0:02:39.440 --> 0:02:42.000 the car tires were kicking up a little bit of 0:02:42.080 --> 0:02:45.880 a spray, and there were literally rainbows following all of 0:02:45.960 --> 0:02:48.840 the cars into school. And then I got out of 0:02:48.880 --> 0:02:50.840 the car and the rain had stopped, and there was 0:02:50.880 --> 0:02:53.600 a rainbow over the ocean and there were hummingbirds and 0:02:53.639 --> 0:02:56.400 it was like a Disney movie scene. I expected like 0:02:56.639 --> 0:02:58.360 a bunny to hop out and be like, can I 0:02:58.360 --> 0:03:02.120 help you with anything? Anyway, it was. It was kind 0:03:02.120 --> 0:03:03.480 of magical. I'll give you that. 0:03:03.600 --> 0:03:06.040 California is heaven. Yes, what happens when you die in 0:03:06.080 --> 0:03:08.560 Virginia is you end up in California. 0:03:08.639 --> 0:03:12.800 Do you know that not all California as southern California. 0:03:12.080 --> 0:03:13.559 I mean all of real California. 0:03:13.720 --> 0:03:16.360 Oh, I see, because northern California's got some weather. 0:03:18.880 --> 0:03:21.120 You're absolutely right. In fact, I heard Katrina say something 0:03:21.120 --> 0:03:24.600 really insightful the other day. You know, she's from northern California. 0:03:24.600 --> 0:03:27.239 But now we've lived in southern California for quite a while, 0:03:27.600 --> 0:03:29.880 and she said to somebody that she's now a complete 0:03:29.919 --> 0:03:33.520 Californian because she's lived in both northern and southern California. 0:03:33.600 --> 0:03:35.920 And I was like, Oh, that's cool. She's like accepted 0:03:35.960 --> 0:03:40.960 southern California, which is hard for Northern California's I'm aware, yes, 0:03:41.480 --> 0:03:44.240 not everything is Southern California unfortunately. 0:03:45.120 --> 0:03:48.760 Oh I really like the variability Virginia weather is amazing 0:03:48.760 --> 0:03:50.360 for me. But so my question for you is, what 0:03:50.440 --> 0:03:53.520 is the worst weather situation that you've experienced? 0:03:53.800 --> 0:03:57.320 Great question. I was on the East coast, of course, 0:03:57.440 --> 0:04:00.440 you're doing a college tour with my son, and we 0:04:00.440 --> 0:04:03.560 were in Massachusetts. I think we were visiting Amherst or 0:04:03.560 --> 0:04:06.160 maybe it was Williams, I don't remember. And there was 0:04:06.200 --> 0:04:09.200 some freak tornado which tore up a bunch of trees 0:04:09.280 --> 0:04:12.040 and knocked down a bunch of power lines and there 0:04:12.160 --> 0:04:16.440 was no power in the whole town for like almost 0:04:16.440 --> 0:04:19.360 half a day. It was crazy and the winds were 0:04:19.360 --> 0:04:22.560 insane and it felt a little scary, like we saw 0:04:22.680 --> 0:04:25.120 like huge branches flying by the window. 0:04:26.120 --> 0:04:27.440 Yeah, yep. 0:04:28.040 --> 0:04:29.840 And he didn't end up going to school there. 0:04:31.520 --> 0:04:34.080 Yeah, I get that. I get that. So we lived 0:04:34.080 --> 0:04:38.919 in Alabama, Tuscaloosa, and we moved there pretty soon after 0:04:38.960 --> 0:04:42.640 that giant tornado that like made the news, and you 0:04:42.680 --> 0:04:45.039 could see the path of the tornado because like, you know, 0:04:45.040 --> 0:04:47.000 you'd be driving through an area with lots of like 0:04:47.000 --> 0:04:49.680 you know, Starbucks, Panera, lots of stores or whatever, and 0:04:49.720 --> 0:04:52.599 then suddenly there would be a like air an opening 0:04:52.600 --> 0:04:55.000 in between all of the stores with nothing, and like 0:04:55.040 --> 0:04:58.200 the tornado had just gone through there and just absolutely 0:04:58.240 --> 0:05:00.680 picked up and thrown everything that was in there, and 0:05:00.720 --> 0:05:03.040 even after they cleaned it out, there were still, you know, 0:05:03.120 --> 0:05:05.400 you could tell where the tornado had gone. And we 0:05:05.400 --> 0:05:08.680 were also in Houston during some pretty bad storms and 0:05:08.720 --> 0:05:11.360 we had the kids and our dog and our cats 0:05:11.400 --> 0:05:13.919 in a little hallway in the interior of the house 0:05:14.200 --> 0:05:16.800 and my in laws were visiting, and my mother in 0:05:16.880 --> 0:05:19.400 law was so sweet. She like looked around and she 0:05:19.400 --> 0:05:21.000 she was trying to see, you know, who could get 0:05:21.080 --> 0:05:24.000 hurt and how, And she gave her glasses to Zach 0:05:24.080 --> 0:05:26.159 in case there was any like flying glass and she 0:05:26.279 --> 0:05:29.600 just insisted that he have her glasses. And I was 0:05:29.760 --> 0:05:32.040 like in that moment, I was like, gosh, you are 0:05:32.080 --> 0:05:35.120 the sweetest person in the whole world, Like you are 0:05:35.160 --> 0:05:37.240 thinking about the tiny little things you could do to 0:05:37.279 --> 0:05:39.960 help the people around you, and anyway, she's she's the best. 0:05:40.080 --> 0:05:43.480 Yeah, but we've all been caught in surprise weather, right. 0:05:43.640 --> 0:05:46.920 I remember going backpacking in Arkansas one time and being 0:05:46.920 --> 0:05:49.279 caught in a snowstorm and the temperatures dropped into the 0:05:49.320 --> 0:05:51.560 teens and we weren't one hundred percent sure we were 0:05:51.560 --> 0:05:54.360 going to make it. Oh, and everybody's been, like, you know, 0:05:54.440 --> 0:05:56.760 caught in a snowstorm or a rainstorm or in a 0:05:56.839 --> 0:06:00.320 heat wave. Right, And these things are exciting, they can 0:06:00.360 --> 0:06:02.800 be dramatic, but they can also be very dangerous, right. 0:06:02.880 --> 0:06:03.080 Yeah. 0:06:03.080 --> 0:06:06.280 People die in these crazy weather storms, and so it's 0:06:06.360 --> 0:06:08.680 valuable to be able to know in advance what's going 0:06:08.760 --> 0:06:11.000 to happen, not just so you can plain your picnics, 0:06:11.000 --> 0:06:15.839 but also you can survive the increasingly dramatic weather that 0:06:15.880 --> 0:06:17.760 we're all facing as the planet warms. 0:06:17.960 --> 0:06:20.640 Yeah, that's right, more severe weather is becoming more common. 0:06:20.680 --> 0:06:22.920 And so today we're going to talk about how good 0:06:22.960 --> 0:06:25.200 we are at making predictions and how we go about 0:06:25.200 --> 0:06:27.000 making those predictions exactly. 0:06:27.120 --> 0:06:29.760 And I wanted to pull back the curtain on like 0:06:29.800 --> 0:06:32.120 the science of this, how does this actually happen, What 0:06:32.160 --> 0:06:35.120 are we doing, why is it hard? What are the challenges? 0:06:35.440 --> 0:06:38.120 What improvements might we be seeing in the next five 0:06:38.240 --> 0:06:41.279 or ten years. What problems are just fundamentally impossible and 0:06:41.360 --> 0:06:44.279 might never be solved. And so today we're going to 0:06:44.279 --> 0:06:46.520 dig into science of all that. But before we explain 0:06:46.560 --> 0:06:49.040 to you how the experts do it, I was wondering 0:06:49.080 --> 0:06:52.000 what everybody knew about how weather predictions happen. How do 0:06:52.080 --> 0:06:55.080 those numbers end up on your phone? So I went 0:06:55.120 --> 0:06:57.640 out there to ask our listeners what they knew about 0:06:57.680 --> 0:07:00.640 how we predict the weather. If you would like to 0:07:00.640 --> 0:07:02.800 answer these kind of questions for a future episode, don't 0:07:02.800 --> 0:07:06.200 be shy, right to us two questions at Danielankelly dot org. 0:07:06.320 --> 0:07:09.880 We will send you fun questions every week in your inbox. 0:07:10.360 --> 0:07:12.240 In the meantime, think about it for a minute. What 0:07:12.280 --> 0:07:15.400 do you know about how we predict the weather? Here's 0:07:15.440 --> 0:07:20.920 what our listeners had to say. Sophisticated computer models, which 0:07:21.200 --> 0:07:25.320 with an understanding if case theory, allows us to understand 0:07:25.360 --> 0:07:26.320 the limitations. 0:07:27.000 --> 0:07:30.000 Predicting the weather is like quantum particles. 0:07:30.640 --> 0:07:34.440 There are many probabilities, but it is not known until 0:07:34.560 --> 0:07:39.280 it is observed. Meteorologists they look at the current weather, 0:07:39.400 --> 0:07:41.760 and they try to predict it by looking at the 0:07:42.280 --> 0:07:43.520 moving clouds and all of. 0:07:43.480 --> 0:07:52.360 That, by measuring with velocity and atmospheric pressure and maybe 0:07:52.560 --> 0:07:57.120 modeling these that in supercomputers. 0:07:57.320 --> 0:07:59.720 When a cow lies down in the field, it's going 0:07:59.800 --> 0:08:03.000 to and when my knee aches, it's gonna snow. 0:08:03.360 --> 0:08:06.080 Running multiple models. 0:08:05.880 --> 0:08:11.320 Big computers, really really big computers. 0:08:10.840 --> 0:08:14.040 Feed dad data, two complicated models that run on very 0:08:14.040 --> 0:08:15.200 parfa spoken. 0:08:14.880 --> 0:08:21.280 Pere i'd say, with surface measurements, satellite information and sophisticated 0:08:21.840 --> 0:08:25.280 models and perhaps even artificial. 0:08:24.760 --> 0:08:30.920 Intelligence observations taken by ships, planes, ground stations, satellites combined 0:08:30.960 --> 0:08:34.840 with models built by really really smart people that run 0:08:34.880 --> 0:08:38.360 on some of the fastest computers that humans have ever built. 0:08:38.440 --> 0:08:41.360 There are sophisticated bottles that use a wide range of 0:08:41.400 --> 0:08:44.200 observational and predictive inputs. 0:08:43.840 --> 0:08:46.920 By observing weather patterns and the types of whether those 0:08:46.960 --> 0:08:48.040 patterns tend to bring. 0:08:48.320 --> 0:08:51.360 So I don't know if there's actually like scientific evidence 0:08:51.480 --> 0:08:54.160 that sometimes knees will ache if like a stormfront is 0:08:54.200 --> 0:08:56.160 coming through. But I have to admit that there's a 0:08:56.200 --> 0:08:57.719 part of me that really hopes that if I get 0:08:57.800 --> 0:09:00.720 arthright is when I'm older, I do have the ability 0:09:00.760 --> 0:09:02.880 to tell when the weather's come in because I'll feel 0:09:02.880 --> 0:09:06.160 like I'm really intimately connected to my environment. Oh, the 0:09:06.200 --> 0:09:09.120 knees acted up again. Storms come and get the goats 0:09:09.120 --> 0:09:09.640 in the barn. 0:09:10.240 --> 0:09:13.400 I think that really shows your fundamental optimistic nature, Kelly, 0:09:13.440 --> 0:09:16.040 because you're like, oh, I forget arthritis. There'll be a 0:09:16.080 --> 0:09:18.079 silver lining. I can predict the weather. 0:09:18.600 --> 0:09:21.120 You know, life is easier when you try to see 0:09:21.120 --> 0:09:21.880 the silver lining. 0:09:22.640 --> 0:09:23.480 That's wonderful. 0:09:23.600 --> 0:09:26.800 But our audience had great answers, and they were, you know, 0:09:26.840 --> 0:09:28.839 a lot of them said, you know exactly the right thing, 0:09:28.840 --> 0:09:31.720 which is you've got to have data. Those are the 0:09:31.760 --> 0:09:33.960 observations and you feed them into computers. 0:09:34.200 --> 0:09:37.440 Yeah, essentially, and that's the big picture, not just of 0:09:37.480 --> 0:09:40.920 weather prediction but any kind of prediction. There are two 0:09:41.360 --> 0:09:44.920 fundamental ingredients to how you make a prediction. There's the 0:09:44.960 --> 0:09:48.240 models and then there's the data. So let's take those 0:09:48.280 --> 0:09:51.080 each in turn. When we say the models, we mean 0:09:51.280 --> 0:09:54.280 like we're running a computer simulation or you're calculating things 0:09:54.320 --> 0:09:57.840 on paper. Fundamentally, this is encoding the rules of the 0:09:57.880 --> 0:10:01.560 system what the future can be given what the past was. 0:10:02.040 --> 0:10:04.079 And this doesn't have to be some really complicated thing 0:10:04.280 --> 0:10:06.800 like the weather over ist endbull. Think about a much 0:10:06.840 --> 0:10:09.959 simpler situation, like you're tossing a ball in your backyard. 0:10:10.400 --> 0:10:12.560 You want to know where does it go? Well, the 0:10:12.600 --> 0:10:15.200 laws of physics predict the future, right, this is the model. 0:10:15.240 --> 0:10:17.800 These are the rules that tell you how the past 0:10:18.120 --> 0:10:21.120 becomes the future. Right. And in this case it's simple. 0:10:21.160 --> 0:10:24.120 It's a parabola. It flies through the air. Things to 0:10:24.200 --> 0:10:26.680 keep in mind here, though, is that a model like 0:10:26.760 --> 0:10:30.480 this is always approximate. If I use f eicals MA 0:10:30.760 --> 0:10:33.240 and I just account for gravity, ignore air resistance. When 0:10:33.280 --> 0:10:35.680 I'm describing the ball, I'm going to get a quick answer, 0:10:35.720 --> 0:10:37.719 and it's gonna be pretty good. It's not going to 0:10:37.760 --> 0:10:41.840 be exactly bang on correct. It can't account for everything, 0:10:41.880 --> 0:10:44.160 all the little wind gusts and the air resistance and 0:10:44.200 --> 0:10:46.400 the slight change in humidity and maybe the spin on 0:10:46.440 --> 0:10:50.400 the ball. My model ignores some details, and that's crucial. Right. 0:10:50.440 --> 0:10:53.360 If I included every single particle in the backyard, I 0:10:53.400 --> 0:10:55.839 would never get a calculation. So in order to make 0:10:55.880 --> 0:10:58.200 this tractable, I got to simplify the problem. I got 0:10:58.200 --> 0:11:00.680 to pull out the things that are important and ignore 0:11:00.679 --> 0:11:03.679 the things I think are unimportant. Because I don't think 0:11:03.720 --> 0:11:05.680 they're going to make a big enough difference in the answer. 0:11:06.040 --> 0:11:08.240 And this is where the juice is. This is what 0:11:08.320 --> 0:11:12.120 physics is, right. Physics is taking the universe and simplifying 0:11:12.160 --> 0:11:15.320 it into a model that represents the bits you're excited about, 0:11:15.360 --> 0:11:17.480 the bits you think are interesting and irrelevant, and then 0:11:17.840 --> 0:11:20.320 you use those rules and manipulate it. That's your model 0:11:20.440 --> 0:11:22.920 of the universe, and the model gives you an answer, 0:11:23.160 --> 0:11:25.400 and hopefully, if the model is close enough to your 0:11:25.440 --> 0:11:27.880 description of the universe, the answer you get from the 0:11:27.920 --> 0:11:30.840 model is similar to the answer in the actual universe. 0:11:31.200 --> 0:11:33.360 So one thing I think that's amazing is that something 0:11:33.360 --> 0:11:35.240 as simple as throwing a ball up in the air 0:11:35.480 --> 0:11:37.800 and then seeing where it lands is something we can't 0:11:37.840 --> 0:11:40.600 completely model because there's so many complicating things. And now 0:11:40.640 --> 0:11:44.119 you're talking about weather, which is so much more complicated 0:11:44.120 --> 0:11:46.480 and requires so many more inputs. And of course you 0:11:46.520 --> 0:11:48.760 can update your model. So you know, if you threw 0:11:48.800 --> 0:11:50.160 the ball in the air and you were like, you 0:11:50.160 --> 0:11:52.679 know what, it's a windy day, I absolutely need to 0:11:52.679 --> 0:11:55.920 add wind. Now you've learned something, you add wind, and 0:11:55.960 --> 0:11:58.160 so you know, it's an iterative process where you keep 0:11:58.200 --> 0:12:01.040 trying to say what is in important and do I 0:12:01.120 --> 0:12:03.480 need to include it? And does it make my predictions better? 0:12:03.640 --> 0:12:07.360 But I also will note that you put predicting weather 0:12:08.000 --> 0:12:11.800 under the physics umbrella. You think you guys get to 0:12:11.800 --> 0:12:13.000 claim weather predictions. 0:12:13.600 --> 0:12:15.880 I mean, we're not using economics to predict the weather. 0:12:16.520 --> 0:12:19.280 What else is in the running for taking credit for 0:12:19.320 --> 0:12:21.880 predicting the weather? Is it chemistry? 0:12:22.320 --> 0:12:24.880 I feel like that also is some ecology, you know, 0:12:25.080 --> 0:12:26.760 like because you're tracking. 0:12:26.520 --> 0:12:27.960 Like cowfarts or something. 0:12:28.200 --> 0:12:29.679 No, no, you like, you know, a. 0:12:29.679 --> 0:12:33.080 Fart player role. Actually, so do you think. 0:12:33.000 --> 0:12:36.720 That Noah has cow farts in their weather prediction models? 0:12:38.200 --> 0:12:42.319 I think the climate models do include bovine methane emissions. Yes, 0:12:42.640 --> 0:12:45.880 so not the daily predictions, but the bigger trends. Yes, 0:12:45.960 --> 0:12:48.760 cow farts do help determine the future of our planet. 0:12:48.960 --> 0:12:49.360 Amazing. 0:12:49.360 --> 0:12:50.920 I want to go back to the point you made earlier. 0:12:51.000 --> 0:12:54.360 You're exactly right that we're always approximating, and not just 0:12:54.440 --> 0:12:58.199 when we're doing the weather, not just when we're tossing balls, always, 0:12:58.720 --> 0:13:03.160 every single time, every model is approximation. There's this famous phrase. 0:13:03.160 --> 0:13:05.480 I a memember who said it, like all models are wrong, 0:13:05.640 --> 0:13:08.360 some of them are useful, even our description of like 0:13:08.559 --> 0:13:11.240 the fundamental particles in the universe. As far as we know, 0:13:11.480 --> 0:13:15.920 these are approximations. Every bit of science we have has 0:13:16.040 --> 0:13:19.720 boundaries of where it's relevant because there are approximations made 0:13:20.080 --> 0:13:23.920 when we construct those models everything, literally everything. We have 0:13:24.000 --> 0:13:27.680 no piece of science that isn't an approximation of the universe. 0:13:28.280 --> 0:13:31.120 Maybe one day we have a theory of everything, and 0:13:31.160 --> 0:13:34.600 it's beautiful and we can do exact calculations on very 0:13:34.720 --> 0:13:38.440 very simple situations. But we're not there. We may never 0:13:38.520 --> 0:13:40.920 be there, and even if we are there, it will 0:13:40.960 --> 0:13:43.920 be totally impractical for anything useful. Like you couldn't use 0:13:44.400 --> 0:13:47.360 string theory to predict the path of a hurricane because 0:13:47.640 --> 0:13:51.040 the complexity would be insane, Right, how many strings are 0:13:51.040 --> 0:13:53.640 you modeling? The amount of computation required to do it 0:13:53.720 --> 0:13:57.480 exactly would be impossible. So it's always an approximation. It's 0:13:57.520 --> 0:14:00.920 just a question of which approximations. That's where the science 0:14:00.960 --> 0:14:03.240 comes in, like which ones are important. Having a nose 0:14:03.840 --> 0:14:06.720 for what to approximate and what not to approximate, that's 0:14:06.720 --> 0:14:09.320 what helps some scientists make more progress than others. 0:14:09.480 --> 0:14:11.440 Yeah, And I think another thing to just sort of 0:14:11.480 --> 0:14:14.800 note is that because this is a human endeavor. Sometimes 0:14:14.920 --> 0:14:17.959 you're limited by what you can afford to get data 0:14:18.000 --> 0:14:20.200 on you know, like maybe you do want to know 0:14:20.640 --> 0:14:23.360 how much cows are farting, but in order to get 0:14:23.360 --> 0:14:26.440 that data, you would need seventy billion dollars so that 0:14:27.040 --> 0:14:30.320 farmers could attach sensors to the rear end of every cow. 0:14:30.400 --> 0:14:33.400 And so, like, you know, sometimes you know there's data 0:14:33.440 --> 0:14:34.920 you want, but you can't get it because there's not 0:14:35.040 --> 0:14:37.080 enough money or it's not possible. Maybe one day you 0:14:37.080 --> 0:14:39.000 can get it, Maybe those sensors will become cheap. 0:14:39.160 --> 0:14:42.080 Is seventy billion dollars your like a fantastical number for 0:14:42.120 --> 0:14:44.360 some like absurd amount of money for a science experiment? 0:14:44.920 --> 0:14:47.640 Yeah, I guess that's wow. Yeah what is yours? I 0:14:47.640 --> 0:14:49.280 guess you're a physicist, so it's going to be like. 0:14:49.360 --> 0:14:52.680 Well, that's embarrassing because our next project is one hundred 0:14:52.720 --> 0:14:56.600 billion dollars, So we're like already above the Kelly threshold 0:14:56.640 --> 0:14:58.440 for like absurd amounts of money. 0:14:58.600 --> 0:15:01.400 But wait, like, okay, but that's not like your personal project. 0:15:01.440 --> 0:15:04.560 That's like LHC or like a new particle collider or something. 0:15:04.640 --> 0:15:07.240 Right, Yeah, the new next particle collider budget is about 0:15:07.280 --> 0:15:10.440 one hundred billion, Yes exactly, so more than a planet 0:15:10.480 --> 0:15:12.200 wide cow farts sensor network. 0:15:12.360 --> 0:15:15.280 Well, you guys better make some really important discoveries with 0:15:15.320 --> 0:15:17.880 that money. Otherwise I'm disappointed because I want to know 0:15:17.920 --> 0:15:19.120 what's happening with the cow farts. 0:15:20.280 --> 0:15:22.400 But you bring up another point, which is the data. 0:15:22.560 --> 0:15:25.160 So models are useful. There are a system that tell 0:15:25.240 --> 0:15:28.640 us how the past becomes the future, but you also 0:15:28.680 --> 0:15:31.640 need some data so you know which past you had. Right, 0:15:32.080 --> 0:15:36.680 models describe essentially any possible universe. The rules determine which 0:15:36.840 --> 0:15:39.760 set of universes we might live in, but the data 0:15:39.800 --> 0:15:42.760 constrain it. It tells us which past we had. So 0:15:42.800 --> 0:15:45.080 the rules tell you how the past becomes the future, 0:15:45.280 --> 0:15:47.680 but you need to know which past we were in 0:15:47.720 --> 0:15:50.640 so we know which future will have. So in our 0:15:50.680 --> 0:15:53.320 ball tossing analogy, there's lots of different ways that could 0:15:53.320 --> 0:15:55.440 toss a ball. I Coatalla said high or low, or 0:15:55.440 --> 0:15:58.520 fast or slow or east or west. The rules connect 0:15:58.600 --> 0:16:02.360 the initial conditions that data the past to the future. 0:16:02.560 --> 0:16:05.040 But you need to know where did I throw the ball. 0:16:05.160 --> 0:16:07.720 So if I'm writing a simulation of that ball toss, 0:16:07.800 --> 0:16:09.360 I got to encode in the laws of physics. But 0:16:09.400 --> 0:16:11.320 then I need a data point. I need to say 0:16:11.480 --> 0:16:13.560 the ball was here and it was moving in this 0:16:13.640 --> 0:16:16.920 direction at this velocity. Then I can predict the future. 0:16:17.280 --> 0:16:21.160 Without that, it's useless. Right, So you need these two components. 0:16:21.160 --> 0:16:24.000 You need the models, plus you need the data, and 0:16:24.040 --> 0:16:27.520 then you need more data. Say I'm predicting the ball toss, 0:16:27.680 --> 0:16:29.560 I want to check in halfway and say, hey, it's 0:16:29.600 --> 0:16:32.960 my model correct, doesn't need an adjustment. A way to 0:16:33.000 --> 0:16:35.920 improve your modeling is to shorten the prediction time, to 0:16:35.920 --> 0:16:38.000 say I'm not going to predict the whole path. I'm 0:16:38.000 --> 0:16:39.440 going to predict the second and then I'm going to 0:16:39.480 --> 0:16:41.400 take a measurement and if it's off, I'm going to 0:16:41.440 --> 0:16:43.920 correct it so that if my model has veered off 0:16:43.920 --> 0:16:47.600 from reality, it doesn't get further off. And so the 0:16:47.600 --> 0:16:49.640 more data you have, the better your model is going 0:16:49.680 --> 0:16:52.280 to be. So you need these two elements dancing together, 0:16:52.560 --> 0:16:54.000 the models and the data. 0:16:54.160 --> 0:16:56.600 Yeah, and I checked my weather app today and the 0:16:56.640 --> 0:16:59.600 prediction for tomorrow was changed, And so I'm guessing we 0:16:59.640 --> 0:17:03.280 do this same thing with weather Way update. So I 0:17:03.280 --> 0:17:05.200 think we should talk in a second about what kinds 0:17:05.200 --> 0:17:08.720 of data we collect to help us inform models. But 0:17:08.840 --> 0:17:11.080 I guess my first question is we've talked about models 0:17:11.119 --> 0:17:13.879 in general. How long have we been trying to model weather? 0:17:14.760 --> 0:17:15.840 Aristotle problems, So. 0:17:18.400 --> 0:17:20.800 People have had some crazy ideas about the weather for 0:17:20.880 --> 0:17:24.560 thousands of years. The first real weather models were conceived 0:17:24.600 --> 0:17:28.159 of in the nineteen twenties. And remember we didn't have 0:17:28.200 --> 0:17:31.080 computers really until the fifties or so, so this was 0:17:31.119 --> 0:17:34.480 like a conception and somebody did a proof of principal prediction. 0:17:34.800 --> 0:17:37.760 They tried to predict the weather six hours later. They 0:17:37.800 --> 0:17:39.920 took a bunch of measurements and said, let's try to 0:17:39.960 --> 0:17:43.320 do some calculations. We have an early model. That calculation 0:17:43.480 --> 0:17:44.760 took six weeks. 0:17:45.200 --> 0:17:46.240 So not helpful. 0:17:46.600 --> 0:17:50.000 Not helpful exactly, but they did it and it wasn't terrible, 0:17:50.080 --> 0:17:51.919 and they sort of proved like, hey, you know, if 0:17:51.960 --> 0:17:54.679 you could do this calculation more quickly, then maybe you 0:17:54.680 --> 0:17:57.479 could even know the weather in advance. Oh my gosh, 0:17:57.600 --> 0:18:00.840 what an idea. Right, Yeah, it was until the nineteen 0:18:00.920 --> 0:18:03.399 fifties that we had the first computing models to do 0:18:03.440 --> 0:18:07.359 these calculations. So we can make predictions in time shorter 0:18:07.440 --> 0:18:10.080 than the prediction period. You could have enough data and 0:18:10.200 --> 0:18:13.120 run your model and get an answer before the universe 0:18:13.200 --> 0:18:16.520 revealed it, right, that's that's a prediction instead of a 0:18:16.560 --> 0:18:17.280 post addiction. 0:18:18.320 --> 0:18:18.800 That's better. 0:18:18.840 --> 0:18:21.439 So we've been doing this for decades and the last 0:18:21.560 --> 0:18:24.119 you know, seventy years or so have been improving the 0:18:24.160 --> 0:18:26.160 models and improving the data. 0:18:26.200 --> 0:18:28.680 Man, it's exciting to think that we, you know, we're 0:18:28.720 --> 0:18:33.280 going from slide rules to make these predictions to massive supercomputers. 0:18:33.880 --> 0:18:36.680 I'm appreciating my weather apps a bit more. 0:18:37.960 --> 0:18:40.679 And also, like, six weeks sounds ridiculous. I don't know 0:18:40.680 --> 0:18:42.320 that I could do that in six weeks. Oh, it's 0:18:42.320 --> 0:18:45.800 an amazing calculation. And think about like not just the ideas, 0:18:45.800 --> 0:18:49.040 but all the grunt work doing those calculations and the 0:18:49.119 --> 0:18:51.679 human error that's possible. Like, it's amazing they did it 0:18:51.680 --> 0:18:54.399 in six weeks, you know, So don't laugh at that. 0:18:54.600 --> 0:18:57.840 Absolutely, So we've been doing this since the nineteen fifties. 0:18:57.920 --> 0:19:00.439 Let's talk about what kind of data we're collect to 0:19:00.520 --> 0:19:02.960 inform these models when we get back from the break. 0:19:22.760 --> 0:19:24.840 All right, and we are back, So now we're going 0:19:24.880 --> 0:19:27.280 to talk about the kinds of data that we use 0:19:27.400 --> 0:19:31.200 to make weather predictions. And I'm gonna bet it involves satellites. 0:19:33.160 --> 0:19:36.640 Always going with space first, right, yep, yep. It does 0:19:36.680 --> 0:19:41.359 involve satellites, but there's an amazing, incredible variety of data 0:19:41.400 --> 0:19:44.160 sources we have to understand the weather. And yet still 0:19:44.160 --> 0:19:47.520 it's not nearly enough. Right as you'll hear, our weather 0:19:47.560 --> 0:19:50.399 prediction would be so much better if we had more data. 0:19:50.480 --> 0:19:53.280 We're really limited by the data. But we have lots 0:19:53.280 --> 0:19:56.040 of different kinds. We have weather stations on the surface 0:19:56.160 --> 0:19:58.520 and so a lot of these are called like automatic 0:19:58.520 --> 0:20:01.560 weather stations that are scattered across the country. They're just 0:20:01.560 --> 0:20:04.800 basically a bunch of sensors with a battery and like 0:20:04.880 --> 0:20:08.040 either a wind turbine or a solar panel to get power, 0:20:08.520 --> 0:20:11.960 and they measure things like temperature and pressure and wind 0:20:12.040 --> 0:20:16.359