Why Is It So Hard to Make a Good Weather App?

It's sort of the realization that all weather forecasts are going to be wrong, right? There's just nothing you can do about it. The key is how do you convey that uncertainty? I'm Charlie Wurzel and this is Galaxy Brain, a show where today we are going to get to the bottom of a question plaguing mankind since time immemorial. Do weather apps suck? People have very strange relationships to weather apps. They check them obsessively, they love them, they talk about them, they pay money for them, and at the same time, they constantly complain about them. Weather apps often leave us high and dry or low and wet, whatever you want to call it. Weather apps are a feature of life and yet the weather is super unpredictable. And so we get this tortured relationships with these devices and they tend to be really, really important. As the climate gets more and more erratic, as there's more instances of extreme weather, and as we become increasingly information junkies, we rely on these apps more and more. And frankly, a lot of times they don't work the way we want to. And so I wanted to demystify these weather apps. I wanted to talk to somebody who could tell me how they work, how they've gotten better, how they've gotten worse. Weather, we need all the information about the weather that we have. Remember, back in the day we just used to look in the newspaper and get one forecast or go to the local news and get a forecast in the morning and a forecast at night. Now we have all this information, what are we doing with it? And so my guest today is Adam Grossman. Adam is a physicist who created the app Dark Sky back in the early 2010s. And that app quickly became an absolute cult favorite. It launched in 2012 and then Apple bought Dark Sky right around the pandemic and integrated it into their massive weather app. Adam then helped build Weatherkit at Apple for a long time and he left to build a new app called Acme Weather based all around this idea of trying to give people more access to more information and also communicate more uncertainty about the weather. And so I thought Adam would be the perfect person to talk about this. He has this inside view of this platform and he can help answer these questions. Why do we need all this information? Can we ever get a perfect definitive forecast? Do weather apps suck? Or do the users just simply expect too much from it? Adam and I got to the bottom of all of this. But first, a quick break. The world moves fast. You work day? Even faster. Pitching products, drafting reports, analyzing data. Microsoft 365 Co-Pilot is your AI assistant for work built into Word, Excel, PowerPoint, and other Microsoft 365 apps you use. Helping you quickly write, analyze, create, and summarize. So you can cut through clutter and clear a path to your best work. Learn more at Microsoft.com slash N365 Co-Pilot. Adam, welcome to Galaxy Brain. Thanks for having me. It's exciting. So I want to start at the beginning here. How did you get into this job? This is an interesting gig building weather apps. So have you always been a weather nerd? What is the background here? Walk me through that. So my background actually isn't in weather. I have a physics degree. I ended up doing a lot of just software development, web development. But I think everyone is kind of a weather nerd to a certain degree. Everyone gets kind of excited about the weather. I don't know if it's just built into humans in general. I started doing weather probably 15 years ago. I guess it was in the summer of 2010. My now wife, my girlfriend at the time, we were driving to Cleveland to go on vacation because people go to Cleveland on vacation. I'm from Cleveland. I get it. Oh, are you? There you go. I'm in Connecticut. And so we were just driving west and we pulled off at a rest area and it was just a torrential downpour. Just cars on the highway were going 10 miles an hour. It was just a mess. And I remember opening up whatever weather app I had in 2010. I don't remember what it was. I looked at it to see, okay, when can we go go back out to our car and continue driving? And the weather app said something like 70% chance of rain. And it just, it was like, this is not useful. Right? It was a torrential downpour. And then I went to the radar and you could see it on the radar. And I remember thinking the whole time is like, how can we do this better? You know, if there's rain right there, your app shouldn't just say 70% chance of rain. It shouldn't just say it's raining, right? It should be rain is going to stop in 12 minutes or whatever. I started just thinking about the weather then, started playing around with just radar data, trying to see if can we do machine learning? Can we do computer vision to try to figure out where these storms are headed? Right? Because if you look at a radar map, you hit the little play button, you see the radar moving at time. You know your brain can parse this as moving through time. Right. A computer should be able to do this, right? So I built a little gizmo for just trying to predict, you know, just the next few minutes, right? Up to an hour of what the rain's going to do minute by minute so that if it looks like it's raining outside or you get stuck on the highway going to Cleveland, you can say, okay, in 15 minutes, you can go out to your car. It ended up working. And then we decided, hey, let's do a Kickstarter to see if we can make an app out of this for an iOS app. And that was an app called Dark Sky. Originally, Dark Sky wasn't a really a general purpose weather app. It literally just told you what the rain was going to do in the next hour. It didn't even have temperature, nothing. And we always promised ourselves, we're not going to make a general purpose weather app. There's so many of those. That did not last long because we realized people don't want two weather apps. And then in 2020, just as the pandemic was hitting, we ended up joining Apple to work on Apple weather. And then four years later, a few of us left. And then shortly thereafter, we started Acme Weather, which is our new app or our new weather service. So I want to get there. But I think it's really important that you had kind of the canonical normal weather app experience, right? Which is you open the thing up, you see that it's not reflecting your reality. Obviously, you had the means and the tools to do that, to change it, to make something different. But I think let's just start very basic here. I want to get into the nuts and bolts of how weather apps and forecasting works for the layman. Because I think it's really important to foreground that for the rest of this discussion, which is going to get into why these apps succeed sometimes, fail other times. But can you just walk me through, explain it like I'm five? How do these weather apps work? Or how does let's start eating there. How does weather forecasting work? Yeah. So I mean, it depends what kind of forecasting you're talking about. So the one I just mentioned, which Dark Sky did originally, was very short term, very hyper local. This is exactly what's happening at your location over the next few minutes. But that technique is not going to work for what's happening this weekend, right? Or multiple days ahead. And so it, and also when you talk about just climate forecasting, long term climate trends, that's a very different kind of forecast. When people think about weather forecasting, they sort of think hour by hour out 10 days, right? And that's sort of the starting point. And then you could tack on other things. But the way that works, it's they're sort of a pipeline. And the beginning of the pipeline is gathering a whole bunch of weather data. And the beginning, by the way, the beginning of this pipeline is mostly done by government agencies, government weather services. And so the first step is, if you want to predict the weather, you got to know what the weather is doing right now, right? You got to know what that sort of initial state of the world is. And that comes from data light data. It comes from, you know, weather balloons that they put up. So the National Weather Service puts up hundreds of weather balloons, I think a couple hundred every day. Weather balloons are nice because it gives you sort of a 3D slice through the atmosphere. So it's temperature, pressure, humidity, things like that, but at different elevations. And that's really useful for, you know, subsequently simulating the weather. There's ground stations, weather stations, right? There's there's buoys out in the ocean that measure things like, you know, water temperature and all that. And so you have all this data that gets collected. And then that all gets fed in to numerical weather prediction models. And again, these are generally models that are are run by by government agencies. And what they're literally doing is just calculating the physics. It's basically running a physics simulation of the atmosphere, given the initial conditions that you have. And, you know, they run these things on enormous supercomputers, and you get an output. You're now starting to do things like using machine learning and AI to do the same thing, but dramatically faster. Let's go to Dark Sky. You created this to solve a very, as you put it, like a very real and kind of isolated problem, right? To me, as an outsider, I feel like when you guys started to blow up, the push notification part of it was really important, right? Like I have this app that is not only going to tell me this thing, but it's going to reach out, make use of then what was sort of a relatively new thing. Push notifications were were somewhat novel at that time. And to say, like, Hey, hi, like this is going to happen. Be aware of this, grab the umbrella or whatever. What did you guys feel like you solved that led to really, you know, blowing up there as an app? I think the big difference is Dark Sky was a weather app and weather service designed for your phone. And at that point, I mean, this is, you know, we started it in 2010. We didn't launch it really early at the end of 2011 beginning of 2012. But these phones that everyone has with them at all times, these smart phones, they were had not been around for that long. Like we have these always connected internet super computers in our pockets. And they were pretty new then. And before that, something like Dark Sky just doesn't make sense because you have no way to actually get the information, right? It's it's very specific on where you are right now. And that's not how people got their weather information, when in their weather forecast before phones, right? They got them from your TV meteorologist, right, or the newspaper, and those necessarily have to be for broad areas, right? For your city, your part of the state. And so the type of forecasts that they would provide were for your region. It wasn't that I think we were doing anything technically magical. It was the fact that we were tailoring it for your smartphone. And I think we sort of got ahead of the other, you know, weather services out there who were still sort of thinking about it the old way, right? They would just put the forecast that you would give on the evening news, they just put that on your phone and it was the same forecast, right? I think it was the realization that you could do some fundamentally different things once you haven't always connected, always on device. I wrote a couple of years ago about weather apps, you know, in general, and we'll get into this a little more later, I think, that the tortured relationship that a lot of people have with them. There was a stat that I pulled from this, this website called Forecast Advisor, that was just talking about Dark Sky in general during, during the time that it was up. It accurately predicted the high temperature in my zip code, only 39% of the time. Do you feel like there were, there were a lot of limitations to what you guys could do there? I mean, it was a lot of fumbling around. So again, we know we just started with one specific kind of forecast when we first started looking into doing longer range forecast. Oh boy, we were very naive. We thought, oh, you just go out and get the data and then plunk whatever the data says and into the app and you're done, right? And that is, yeah, it's not like that, right? And so it takes a lot of work to try to figure out how to take this data and turn it into something that's as accurate as, as you can be. Were there any big fumbles where you guys did something where like, oh no, this, we didn't mean to do that. Or this did not, this does not work like abort or like, how does that work? Oh man, I mean, doing something like this, it's, it's pretty much all little fumbles, right? One important thing is that, you know, you open the app, Forecast are one thing, but also like what's happening right now, what's the temperature outside right now? Models will give you that, not necessarily great at that again, because of like micro climate effects. I was just looking at station data now and we're here and it was in the 30s, but there's some, sometimes there's stations that will say it's 78 degrees or negative 100, right? There's like conversion issues. We had so many times where our forecast would just be off by like 100 degrees because of like, you know, a faulty, a faulty ground station that we were distrusting. Most big data problems is 90% sanitizing the data, munging the data, and it's same with weather, right? Is that's, I think that's, that's most of the, our issues come from just the data is weird in some way that we could have caught, but we didn't catch it because we were, we were young and naive. How did you guys get better at that? Is that just simply the process of trial and error? Were there certain light bulb moments down the road there? I mean, I think the biggest thing is, so we didn't just have a weather app, right? We had a weather app and a weather service to make forecasts, right? And so most indie weather apps, they work on the UI and then there's a, they call it a third party weather service. So Apple has WeatherKit, which is their weather service that developers who make weather apps can tap into. But I think an important thing is, you know, I feel strongly that if you're good to make the best weather app, you should have your own weather service for many reasons that we can get into above. A big one is because you're going to get a lot of complaints and emails from users who paid you for this weather app and your forecast was wrong. And by far user complaints are the number one way that we learn about problems and then go and learn how to fix the problems. But there's no light bulb moment. It's always, there's a million problems and it's always something different. And so we wait for really angry customers to email us and say we ruined their wedding because it rained when we said it wasn't going to. Then we go back and we try to, you know, try to figure out what the commonality between these complaints are and see what we could do to fix it. Is there a good, do you have like a funny or good example of a reader thing where you guys are just like, oh no, oh geez, this something that like stands out to you? It's things like ruining people's day and that makes us, that makes us really sad. There was in dark sky days, we, well, according to the user, we ruined their wedding because we botched a forecast and it just makes you feel bad. People tend to not email you when you get things right. Right? They don't, it's just like, good job. Go you. So it's, you know, you kind of need thick skin, but it's super useful, right? So in Acvee right now, we have a this community reports section of the app where people can submit, you know, what the weather actually is outside. You make a report, you could see it on the map, you could see everyone else in your areas reports on the map. That's useful for a couple things, but one of the things is it gives us real world data of what people are actually saying so that we can then look at that and say, does this actually match our forecast? If it doesn't, why doesn't it? Right? I mean, we're never, there's always going to be noise, right? There's always going to be error, but are there systematic things we can catch? The other nice thing about it is the weather forecast is always going to be wrong. And so it's kind of nice to have that ground truth from other users in your area that are like a sanity check. Right? So we'll give you, you can turn on notifications for that. So if multiple people say, hey, it's raining, you'll get a notification if you turn it on that, hey, other people in your area are saying it's raining, right? And so I think that helps us get around just the inherent lack of certainty in the forecast. Let's talk a little bit about going to Apple. You guys go in there and the Apple weather app is, so many people have these and so many people default to the one that's on there. I don't think I'm allowed to say how many users. It is a crap ton of users. It's amazing how many users use Apple. It is scary. Yeah. Someone working at Apple on Apple, it is very scary because it's a ton of users. Was that just an unbelievable amount of pressure to be in there? I guess what were you Dark Sky guys doing in there specifically? And then secondarily, was it just like, oh crap, we have to make sure the stakes are so high right now? Yeah. So Apple always had a weather app, right? And they just used third-party data. Apple decided, we really, this is an important app for people. Apple is really big on sort of owning the technology that powers their ecosystem. And so they decided we need to have a weather service. We need to have that capability in-house so that they can do all the things that they want to do. They don't have to be reliant on a third-party. And so that's why they brought us in was to work on that. And that turned into Weather Kit, which is again, it's the behind-the-scenes API for developers to deliver weather forecasts. And that's what the Apple Weather app uses. So Apple Weather uses the same Weather Kit that, you know, if you're an iOS developer and you want to make your own weather app, you would use Weather Kit for that. And so that was what we did was come in there and work on Weather Kit. Again, it's kind of scary going from sort of a very niche, small, tiny company with what we thought were a lot of users, but not compared to Apple. And then going to this giant company, yeah, it was a little stressful. What was the reason to leave and start Acme? What prompted that? You know, so I have been a huge Apple fanboy ever since I was a tiny little kid getting to go to Apple and work on that was, for me, a dream come true. It was just absolutely amazing. Everyone there was great. The problem is, it's a giant company, right? So you go from like the smallest company in the world where you could just do whatever you want. And then you go to a enormous company with that just, well, there's a ton of stakeholders, right? You can't do whatever you want. Myself and the other Dark Sky people just found us that we missed the small, scrappy startup days of that Dark Sky where you could come up with a crazy idea one day, work on it the next couple of days, and then just ship it out. And if something breaks or people don't like it, you know, you can go and you could fix it and you can iterate. And then we just missed that, right? And so it's just not something you can do at a big company, whether it's Apple or anyone else. What do you or did you, maybe that's the same, see as the current hole in the market right now for WeatherApps? So I think when I left Apple and there are a few of us at Dark Sky who ended up leaving around the same time, I don't think we thought we'd get back into the weather business again. But then it's kind of hard having done it for so many years and then having to use someone else's WeatherApp, right? It's just like, oh, but I want the WeatherApp to do this, right? Why are they doing it this way? I want to do it this way. And so we ended up just getting frustrated with the existing WeatherApps. And I, you know, I think the biggest thing, and so our focus at Acme is, you know, it's sort of the realization that all weather forecasts are going to be wrong, right? There's just nothing you can do about it. The key is how do you convey that uncertainty? My favorite UI for weather by far is your TV meteorologist. You watch her, she says, hey, you know, there's a storm coming in, but you know, the European model has it, you know, being pushed up to the north. And so, you know, maybe instead of snow, it will get rain in the afternoon. They convey the uncertainty. They tell you what may or may not happen. It's, right, it's, and I think that makes a huge difference, especially for storms, right? WeatherApps, pretty much every WeatherApp on the market just says, hey, you're, you know, here's what we think is going to happen. And this is our best guess. It's how do you convey that uncertainty and how do you deal with it, I think, is what was lacking in a lot of WeatherApps. And that's sort of our focus with Acme. I want to dig a little more on this with the current state of WeatherApps. And someone who's made them, how, how is the need for information change, do you think, over the last decade, decade and a half, as weather has gotten more extreme? Is it just that people are just more information hungry, you think now than they were? Or do you think that there's actually a genuine need given the rise of more unpredictable or extreme weather? It's probably both. And it's not so much that it's more unpredictable. Actually, weather prediction has been improving faster than the weather has becoming more chaotic. So weather forecasts are getting better over time. You know, everyone listening to this is probably going to complain and say, my WeatherApp sucks, it's not getting better. But statistically, they are getting better. But yet to the extent that there are more just things that impact your day, people are just sort of more demanding now. Again, you would used to watch the weather, you'd read it in the paper in the morning and then watch it at night and then just on the news and then hope for the best. Now that everyone has WeatherApps on their phone, it's much more obvious when things, you know, I think they're more demanding for the information that they need right now or in the immediate future. Right, definitely. Because I think people are checking it way more often than they used to. So three years ago, I spoke to this weather forecasting consultant and he told me, quote, the general public has access to more weather information than ever and I'd pause it, that's a bad thing. Agree or disagree? No, I well, I don't know the context in which he said that. But I know more information is always better than less information, I think, right? Information overload is definitely a thing, right? And so WeatherApps used to be very simple. It was just what are the current conditions and then maybe like an icon in temperature for the next 10 day. And now people are demanding more than that. And it's not that having that extra information is bad, it just makes it more challenging on what you would you do with that information, right? Like how do you convey that in a way that isn't information overload? That's really on the people making the UIs and presenting that data, right? That's not, I think the demand for more data is I think totally legitimate. If that data exists, give it to me and give it to me in a way that I can understand it, I think is the way to go. The context of that quote was this thing that you had just said a minute ago, right? Where people are like, just ask why they suck, right? Why WeatherApps suck? And I'm like, do they suck? And I just, I am a little bit frustrated on your behalf about this because it's like- They do not suck. They're wrong sometimes, but they, you know, I guess it depends on what you mean by suck. Like you can get into the statistics of it and be like, okay, what's the briar score for your precipitation probabilities, right? And you can measure, you can measure things. I think that, yes, always having your weather on you at all times does make it more obvious when it's wrong. I think we notice way more when it's wrong than when it's right, right? When it's right, it's just like, okay, of course it should be doing what it should be doing. When it's wrong is when you get mad, right? And that's when you, that's what you remember. So yeah, I don't know, they don't suck. They're getting better slowly. Forecasting is getting better, but you know, we're contrast that with people are checking it way more often. And so you notice, you know, if you're just doing tick marks on how often it's wrong, you're going to have a lot more tick marks now just because you're checking it way more often. I sort of agree with this. I think that it's that people want certainty. They want definitive, and I think this is just like the way that things are right now, right? Like we are in a moment of low trust, right? We are like, just broadly speaking in the world, I work in news. It's a moment of relatively low trust of institutions of all kinds, right? They want something definitive when things feel uncertain. And I think at the core, nobody can offer a definitive, like a truly definitive thing. Do you agree with that? People would love certainty, but I think what they're really after is if it's uncertain, they want to know that. It's sort of, what is your certainty around your certainty of your forecast, right? And I think that's what people really want. If there's a storm incoming, and it's just different models are saying different things, it's very different for a weather app to just make a guess and be like, okay, I'm just going to go with this and give this. It's a very different thing to say, okay, look, the forecast is uncertain now. Here's what might happen. Here's how you can prepare yourself. Sample money of certainty in both cases, but being able to actually convey and tell people we are uncertain is I think a form of certainty. I'm trying to figure out what the right word is, right? But I think people want that information. If it's uncertain, they want to know that it's uncertain. You said that these forecasts are getting better. You mentioned machine learning and artificial intelligence. What, as you see it, is the impact right now of AI? Is AI actually making these forecasts better? Is it giving you as someone who's running their own service more opportunities to crunch the data better, organize it, present it? What is the generative AI stuff doing for you right now as someone building this? Yeah, I mean, so there's different places to insert AI machine learning into forecasting. The big one is using it to do these numerical simulations of the atmosphere. The benefit there isn't outright getting better forecasts. The real benefit is it's computationally orders of magnitude more efficient and faster to run a forecast. Just doing the physics is just ludicrously expensive. And AI can do it at a minuscule fraction of the cost. And what that gives you is, is, A, you can run these much more frequently. So something like GFS, which the National Weather Service, their global model, that updates four times a day. If with AI you could do it once an hour or once every half hour, you could get much more rapid updates, which is important for things like extreme weather. If you have a storm coming through the Midwest and it could spawn tornadoes, you want the best, most up-to-date forecast you can. And so doing it faster is huge because it's so much more efficient. You can do it at higher resolution. You can capture more of those micro climates and potentially get better forecasts just by doing it that way. And so I think that's where AI is helping. And I should note that when we say AI here, we don't mean plugging in data to chat GPT. Chat GPT, yeah, exactly. These are weather specific machine learning models. And so what we do is we take those models, the model outputs, and then we use machine learning to do things like micro climate adjustments so that we can take advantage of high resolution terrain data to give you better forecasts. We do it for generating thunderstorm probabilities, precipitation probabilities. And so we train models to do that, which I think really interesting. We haven't done this yet, but I think generative AI, things like chat GPT, might be able to help convey that information. Again, like I said, I think the best UI is your TV meteorologist. But maybe with new on-device models that are coming out, things like that, maybe it could figure out how best to convey that information, how to convey the uncertainty. If it knows who you are and what you care about and that you walk your dog every morning and every evening, maybe it can help you tailor the forecast for that. I think that's more speculative, but there's different places where I think machine learning can slot in and it can help in each one of those steps to make it better. It sounds like the project of ACME weather right now is, as we were talking about, not just convey the uncertainty in a way, but to build some of that trust, to work through that. Something that this makes me think of, and again, not to get overly political, but there's been a lot of government. The government is what collects a lot of this data. There's been a lot of change in the government, a lot of shake-ups around research, but also around funding. Data collection. Right, satellite earth science. Yeah. Yeah, cuts to different government organizations that may or may not be collecting this disinformation. Does that offer concerns for the quality of the forecast? Anytime projects and funding gets cut, there are downsides to that. You don't have as much data that you would otherwise have, or maybe with proper funding, you would have gotten new satellites that have new capabilities that can push forecasting further, and then you just end up not having that. The improvement in your forecasting isn't where it needs to be. That's what I'm worried about is things like that. I'm not worried about politicizing the data itself, because I don't think I see much of that, but I think the issue is, if as funding gets cut, there's less we can do, less data that we can collect. Do you feel like that makes your job more difficult as someone who's building one of these things if there are those concerns just out there in the ether? It adds uncertainty, right? I'm an optimist, and I think we're going to muddle through. I don't envision no just dropping all their weather forecasting. We rely so much on their data collection and data from other organizations. I worry about it in the abstract, but I don't think it affects our day-to-day yet, and again, fingers crossed. What would you say to the person who, again, this is similar to the do weather app suck or whatever, and I'm not asking you to defend them, but in terms of the state of this particular slice of the weather industry, the weather apps, what's your message to them right now? Is it trust us? Is it we're getting better? Is it tell us exactly what you need? Yeah, I mean, well, that's the thing about the weather space, is especially weather apps, is everyone has their platonic ideal of what they want their weather app to do, and everyone's idea is different. Our pitch is, if we're wrong, we don't want to surprise you that we're wrong, right? If we're wrong and that's surprising to you, then I think that's a failure on our part, right? We want to tell you if we think we're going to be wrong so that if we are, you're not like, god damn it, you ruined my wedding, right? I want to avoid that, right? And so I think that is what we're striving for, is to not catch people off guard but if you are and you think the weather sucks, please let us know because again, that's the best way to fix it is for people to yell at us. Adam, this has been extremely eye opening and informative and I feel like I have a better handle than I did when we got into this conversation about what the heck's going on when I pulled to refresh on my phone. So thank you so much for this. Thank you. This was fun. Feel free to email me if the forecast is wrong. Hey, Sainsbury's, we get through so many snacks. Have you got anything to help me save? Well, we're always matching and lowering prices. So hundreds of Sainsbury's fresh fruit, veg and everyday products are price matched to Aldi and every week with Nectar, you can save money on thousands of the products your family loves. So you can snack away knowing you're saving money. Sainsbury's, good food for all of us. Selected products, Aldi price match, not in an eye. Nectar prices require Nectar account. Terms at sainsbury's.co.uk slash Aldi price match and netto.com slash prices terms. Idle money lies in your current account picking crumbs out of its belly button wondering, should I eat them? But when you start investing with Monzo, your money's always busy. It turns on regular investments, invests your spare change and tops up your stocks and shares, Iso. It even helps you make sense of risk and return. Monzo, the bank that gets your money moving. You could get back less than you invest. Monzo current account required UK residents 18 plus T's and C's apply. That's it for us here. Thank you again to my guest, Adam Grossman. You can email him, but please be nice if his weather forecast ruins your day. If you liked what you saw here, new episodes of Galaxy Brain drop every Friday and you can subscribe to the Atlantic's YouTube channel or on Apple or Spotify or wherever it is that you get your podcasts. And if you appreciated this work and you want to support it and the work of all my other colleagues, you can subscribe to the publication at theatlantic.com slash listener. That's the Atlantic.com slash listener. Thanks so much and I'll see you on the internet. This episode of Galaxy Brain was produced by Renee Clark and engineered by Dave Grine. Our theme is by Rob Smersiak. Claudine Abade is the executive producer of Atlantic Audio and Andrea Valdez is our managing editor.