From the dawn of time, we astronomers have had a mortal enemy. This enemy is devious because it can show up when you least expect it. The forecast says it will be clear and then out of nowhere. So how do we astronomers avoid that fate? Well, we have to get better at interpreting weather forecasts so that we can get better at knowing both where and when we will have clear skies. And in this video, I'm going to equip you with lots of tools to do just that. And I got some help in the making of this video from a true expert on astronomy weather, Daniel Fiordalis, who is the creator of Astrospheric. This video is not sponsored by Astrospheric, but it does have a sponsor which is Brilliant. And I'm going to tell you more about Brilliant about Halfway through. So the first thing that we're going to go through here, and I will do it through a series of questions is breaking down some of the terminology and to make sure that once you start digging into these tools and data sets and forecasts that you really know how to interpret them to get the best results out of them. And I'm going to start with this question, the forecast model, because it's something that I admittedly didn't fully understand the last time I made a video on this topic. A weather forecast model is really complex to make. It involves lots of investment in things like satellites and weather balloons and supercomputers. So it's really only governments that are making these weather models. And it is the model that creates predictions about the weather, including cloud cover. And then all the different websites and apps are taking information from a model or sometimes from an API, that's sort of a middleman between the model and the forecast. And the website or app chooses what to display, how to display it, and so forth. So that's the differences you see. But it's important to understand this because there's no point in taking time to consult multiple websites and apps if their predictions are all coming from the same weather model. What we really want to do is compare predictions coming from multiple models. And so I'm going to break down some of the different weather models out there. And before I do that, let's hear from Daniel from Astrosphere Egg on what exactly goes into creating a model, because I think you'll find this super interesting like I did. On the left, there's a few ways that data gets assimilated into the system. There's certainly kind of like radar systems around the continent that are scanning for cloud patterns, intense weather, hail, et cetera. The other way is weather balloons. It kind of blows me away, like NOAA launches like 100 weather balloons every 12 hours from dedicated spots around the U.S. And the same goes on in Canada and other countries. And these provide really accurate vertical data that is also accessible if folks want to look at. They're generally shown in something called a skew-T graph. They're really popular for pilots. These are usually launched relatively near an airport and can provide some really good information about the stability of the air. Then just scattering the landscape are NOAA weather instruments. So these kind of provide a set of data that is at ground level. And then, yeah, there's satellites orbiting us or in geosynchronous orbits that are sending back data that the models can use. There are other places like airplanes, commercial airplanes flying to the sky, actually send in some weather data, and that can be incorporated into the model. And so let's see if I can draw on this here. We'll just pull up a blank sheet. All of these will basically go into what is called data assimilation. I don't know. I have some chicken scratch handwriting, but we'll go for now. And so what that means is you end up with a ton of data points that are just really sparse relative to the land mass and the amount of atmosphere above you.  And depending on how much compute resources you have, how big your supercomputer is, kind of determines then when the model runs how much you can break this down and how detailed you can get. And so there are models that divide the earth every half a degree, right, of latitude or longitude. And then there's models like the high resolution rapid refresh, which I'll show a bit of today, that can divide all the way down to like, you know, kilometer scale. And so you can get a really high resolution. I think, you know, interestingly, let me see if I can quickly turn this into kind of like an isometric view, not only the grid directly over, but this extends up many miles.  And it will go down to like, you know, 50 bar, which is really way up at the top of the atmosphere. And so this then turns into a really a three dimensional grid of cubes that you can then pull weather data out of or predict weather data into as it goes forward. There's some advanced there's like, you know, super advanced things going on where it's not always a cube that gets generated, it might be, you know, like a dodecahedron or some other like shape that helps them increase resolution and kind of optimize compute power. And so from assimilation, you go into really what the model is. And so that's going to take all that intro data, it's going to initialize the model and run some physics over it, and produce the next hour, or, you know, may go out three hours or something like that. And then fundamentally, it's just going to feed that back into itself, reinitialize and keep doing that as far out as the as the model runs and different models have different lengths. The GFS goes out something like 240 hours, like hour by hour, where others may only go out like 36 hours. And again, it's it's all about just optimizing the compute and accuracy of the forecast. Out of these models comes the data that Astrospheric uses and a few other services use. And there are these binary files that are called Grib2, which stands for gridded binary file. And it's kind of like the common currency within the weather community to pass data around and save data into. And this is where it's like, okay, do I get my data directly from those or their APIs in between us? Kind of like, what distance is in between? Because I think, you know, in your weather video, you called out really accurately, check multiple sources. I actually think another big one is like, try to get as close to the source as you possibly can. And so from here on out, there's there's going to be a few things that happen. We could go up into some company that runs a weather API. And this would be something like dark sky. I need to stop calling that Apple now it's Apple weather that runs that. And there's just tons of people that that feed off of those.  But one thing they're not great at is delivering map data. And so some of these will go to services that will generate maps. And others will go to a service like Astrospheric where we'll generate both maps and point forecasts. Okay, so looking at the important things in a forecast for astronomy, cloud cover is number one, right? Because if it's cloudy, none of the other things matter. And the way that cloud cover is typically presented is is the percentage of the sky that's going to be covered in clouds at a particular time and a particular location. So if it's 0%, that means that that particular time and location when you look up, it'll be completely clear. If it says 25%, they're predicting that a quarter of the total area of the sky will have clouds. Now they're not saying where in the sky those clouds are going to be. But what I see some people misinterpret this as is 25% meaning there's a 25% chance of clouds. That's not what they're saying. They're saying there is a high likelihood that there will be some clouds somewhere in the sky and they think that it will cover about 25% of the total sky area. Now that might not be so bad if it's just passing clouds, right? Like sometimes you'll see a forecast go from 0 to 25 and back to 0. And that just means there's a few passing clouds to deal with could still be a perfectly good night. So you have to really look at this. And depending on the website or app you're using, it might not even give you a percentage. It might instead give you a phrase like clear, partly cloudy, mostly cloudy. So here's the cheat sheet for what those terms really mean. And there's a third column here with a lesser used unit for cloud cover measurement called the octa. Octa meaning 8. And so 0 octas means it's perfectly clear. 8 octas meaning completely overcast. Now I've long felt that cloud cover predictions are pretty good a day or two in advance, but a week out they really can't be trusted. And something pretty interesting that I've uncovered in the making of this video is the weather models actually agree with me there. If you can actually find some weather models that will give you error bars showing the level of confidence in any given prediction. So for instance, here I'm on the European Center for medium range forecast website. And if I click on the map, I get this handy chart. And you can see as we get further away from the present, these error bars are getting bigger to the point where a week out the likely range of outcomes is huge. Here it's saying that a week out it's basically a coin flip, whether it's clear or mostly cloudy. Now we've all probably been in a situation where you're literally outside the forecast on your phone says it is clear right now, yet it is cloudy. And so that sucks, it will probably happen to you if you're in this hobby long enough. But there is an extra step that I suggest taking basically for extra assurance that that doesn't happen. And I'd say this step is probably more critical if you plan to spend time driving to a dark location. And that is to look through both real time, but also forecasted map data. When you zoom out and you sort of see how the clouds are predicted to be moving, that is sometimes can give you a much better handle on just where the forecast may be going wrong. And also if you can expect the clouds that you're currently under to stick around or to pass over. And so I'll be showing various tools later in the how to part for viewing map data. So but to wrap up this part about cloud cover a few takeaways. Number one cloud cover is typically presented as a prediction of the percentage of the sky that will be cloudy. Number two, it's very difficult to predict more than a couple days out at a time, which is, you know, because clouds are unpredictable, which way they're going to move. And so for a particular location, it's really hard to say. And the hours leading up to a trip to a dark site, beyond just a point forecast, I would also start looking at map data to get an idea of how the clouds are going to be moving. For transparency and seeing, which are two other important factors for astronomy and an astronomy forecast. Let's go back to Daniel for an explanation. There aren't models that produce transparency and seeing just directly out of the model. It's a bunch of post processing on variables that the model did produce that help us that help us understand and put them together in interesting ways. And so transparency, you actually nailed it in your weather video. What we're looking at is from the base of the earth all the way to the top of the atmosphere, sorry, the base of the atmosphere all the way to the top of the atmosphere. We're going up that column and looking at water vapor primarily, because exactly as you called out, the higher the concentration of water vapor, the more things like light pollution from a nearby city will reflect off of that. And the more that that's in the air, it also is occluding light coming through the atmosphere. The other big factor in our transparency forecast is smoke, which is unfortunately kind of like a necessity now if you really want to have an accurate transparency forecast. And so we pull data in that comes from the RAP model, which predicts and forecasts how aerosols move through our atmosphere. And so we're able to take that and kind of integrate it in with transparency and produce something hopefully a little bit more accurate, although we're like constantly tuning it. And it's one of those things where each summer smoke season that comes, we're able to tune it a little bit further. And so that ends up being transparency. And the value you end up getting out of transparency like out of these functions are basically something akin to an aerosol optical depth, which is a value that just indicates how much light can pass through the gas of our atmosphere and the higher the number, the worse it is and the lower the number, the more transparent it is. I think one thing to call out with transparency, because I see it on some weather websites is people use transparency and visibility interchangeably. And it's not the same thing. Transparency is a measurement of how clear it is directly above you. And visibility is saying at the surface, how far can I see at the surface level? And the reason this is important is because it can be very clear at the surface, but a few miles above your head, there could be serious clouds floating over you or something like that that are actually going to really destroy transparency. So that's just like, yeah, visibility is generally the horizontal measurement of how clear the air is. And transparency is the vertical measurement of how clear the air is. For seeing, I actually, I was going to do a little more drawing for this one. I set up something for this, because seeing is a really complicated variable. And it's one that combines so many that I think a good rule of thumb when you look at seeing is to say like, let me find large spots of good seeing. And if I just see a tiny, really good spot of seeing, like maybe ignore that, because there's so many variables. And if there's any error anywhere in the forecast, they're just kind of compounds all the way up the stack. And so I think the easiest way to illustrate seeing, and this is similar for transparency, is, you know, I'll use one of my favorite sites. I like to go out to the Olympic Peninsula to take photos. It's just ridiculously dark out there. And seeing can be okay. But what happens is behind the scenes, you know, when when astrosphere or whatever, you know, astronomy software that's producing the seeing data is coming from, it's kind of looking at a set of cells above your location. So in that case, you know, I'm out on the coast, these are the cells above me. And so let me just draw a little bit. There's a few factors, a few variables that we look at here. And so let me zoom in just to help with how I draw. The two primary things we're looking at are the wind speed and direction, and then the temperature. Because what's seen is this is a measurement of turbulence in the air above your current location. And the reason that's important is because turbulence is caused by, you know, mixing of low density and high density air or warm and cold air. And as light travels through that, it, you know, it refracts in funny ways. And we end up going from what used to be a beautiful just point of light for a star, something that grows out, you know, much larger. And so let me just do this. So like, if I were to draw on this and say like, you know, I'm on the coast, definitely at the surface, I'm going to have a wind that's kind of like coming right at me. It's usually coming in from the west. And then as we go up this, it may be a little bit similar, it may start turning. There may be a few pockets where things get real wild. Maybe there's a total reversal in airflow in one of these cells. And then as we go back up, it kind of normalizes again. And maybe as we get real high, we're starting to really look at the jet stream, right? These arrows are getting much longer. Hopefully they're going in a similar direction. The ideal situation is they're all pointing the same way. You have like laminar flow above you. And seeing through that type of atmosphere should be pretty good. The other attribute, so this is kind of like a, you know, wind is creating a two dimensional look. And we're looking at where there's wind shear. The other element we have to look at is what is the temperature in each one of these, you know, at the surface, it may be real high. And then it will begin to drop off very quickly. And I'll just, you know, use little dots to show that it's getting cold as we go up. Although there may be a little infusion, it gets a little warmer than the cell below it as we go up. And so what we do at this point is we combine these together and produce what is called a C squared N profile of this column. And that's really just saying like, okay, given these wind speeds and these wind directions and these temperature changes, all these gradients, let's weight this and produce a number that will allow me to say like, okay, I have that point that is a star. It's either going to turn into a little bit bigger of a star or it's going to turn into just, you know, chaos. And that's, that's kind of the final number that comes out of one of these seeing forecasts. So this is a bit complex. I'm not going to go into every model out there. In general, though, there are two types of models, global models and regional models. And global models have to be more low resolution because they're covering the entire earth, but they're good for predicting large weather patterns, extreme weather, like the path of hurricanes. And they can also be helpful for clouds and astronomy. Regional models, though, can be even better because they're typically higher resolution. So you can dial in like your specific location. They usually combine more data sources like the hundreds of NOAA weather balloons that Daniel was talking about. And they also update more frequently sometimes. But the downside to these regional models as the name suggests is that their domain usually only covers, you know, one country or a few countries. Usually the countries are always probably the countries that are making these models that are paying into them. So in the USA where I live, we have the high resolution rapid refresh. It's a weather model that's updated hourly down to a resolution of three kilometers. But its domain, its coverage area, doesn't extend much past the USA. In terms of global models, the European Center for Medium Range Forecasts, often shortened to just the European model, is probably the best one out there in terms of a global model. And I'll be sharing more on how to use their website when we get to the how to's. Another global model is NOAA's GFS, Global Forecast System. And it's another one that's considered pretty accurate. Now, the reason Astrospheric hasn't expanded beyond North America is because Daniel draws from regional models in addition to global models and those regional models that he's drawing from only cover North America. But he feels some of these, especially the Canadian model, are the most accurate in the US and Canada for cloud cover. You know, the thing that made me go with the RDPS model from Canada is that they really focused on cloud coverage. And their prediction model is attempting to predict what you'd see if you just look from one of the satellites, like goes 16, right, looking down on the planet and seeing those clouds. And so what it was trying to do is predict that, which I think is really good for astronomers because we kind of want that detail and what clouds are coming up. So in this video, as you've seen, I wanted to really dive into the models behind the forecasts. But of course, there's always more to learn about any topic. And to truly understand something as complex as a weather model, I think today's sponsor Brilliant is where I'd go for a deep dive on everything you need to know, including statistics and probabilities. Brilliant.org is a hands on way to learn science and math in a really fun and interactive way. And there are thousands of lessons with new ones added each month. The lessons that I think could really enhance your understanding of weather modeling are the ones on probability. And what I'm showing here is the course intro to probabilities that I've been really enjoying. It has all these interactive activities that are really fun. So to try everything Brilliant has to offer completely free for a full 30 days, visit Brilliant.org slash Nebula Photos or click on the link in the description. The first 200 will get 20% off Brilliant's annual premium subscription after the free trial. So hopefully you've made it this far because now that you know all the needed background on weather models and all that stuff, you know, cloud cover, seeing, transparency, what all these things mean, we're going to actually dig into where to find the data and how to use these tools to more confidently predict clear and steady skies for your location. Okay, so the first tool I want to show you here is the ECMWF, European Center for Medium Range Weather Forecasts. And of course, I'll put all of the links in the description. So on here, there's actually a lot of cool information that I've been reading, but I just want to show you the most important chart here. So to get to it, go to forecast and then scroll down to charts and then click into medium range forecast charts. Okay, and then over here on the left hand side, you can see there's different parameters you can select. And so if we just select cloud, it gives us seven different charts that we can look at. And these simulated images might be what you're looking for. Those are really good for just sort of seeing the cloud fronts. But I find them a little bit hard to see like the thinner low altitude clouds on these images. And it's a little bit easier with this total cloud cover map. So I would suggest this one. It's the one I'm going to show here. And because this is the European center, it starts with the European area, the European region. But this is a global forecast. So you can change it to wherever you live, or you can look at the whole, the entire globe. There is a legend down here. It's a little bit confusing. But basically what this is saying is that low clouds are this beige color, medium clouds are the magenta color and high clouds are the blue. And so where they're all, where you have all three types of clouds, you get this sort of dark grayish black. And where it's perfectly clear in the, you know, anywhere that it's perfectly clear, it's going to be white, you're just seeing down to the base map. So I can quickly see here that right now, or pretty close to right now, it's clear throughout much of India, throughout the Midwest area of the United States, through a big part of Argentina, many countries in Africa, looks like all of France is clear. So it's pretty interesting just to look at a global perspective here. But of course you can click in here into the region where you live. So let's just pick Northwest Europe. And I will say for Europeans, I'm guessing that this is more useful than for some other places because for instance, I noticed like in Europe, it breaks it down by many different regions, while in North America, it's just all of North America. So this is probably the one I think to look at if you are in Europe or the UK. Okay, so what is this telling us here? We can see sort of the cloud patterns. And then we can advance it by three hours at a time to see how those clouds are going to move every three hours. And so I can see 12 hours from now-ish. Now, I should stop saying now because this is actually today at 12 universal time. So you'd have to do a little bit of math to figure out what that means. If you're actually in the UK, it's very close to the universal time. But let's just say for the ease of this tutorial that right now it is 12 noon universal time. So in 12 hours at around midnight, I can see that the south of England here is perfectly clear. Now, what happens if I actually click on a location? So let's say I live right there. Okay, so let's look at how to actually read this in a little bit more depth here. So we're going to be looking at this first chart right here, total cloud cover in octas. And remember from earlier, octa is a term that means zero octas would be perfectly clear sky. So that's at the bottom of the chart, eight octas is perfectly cloudy overcast. And so we can see right now in that part of the UK, it's perfectly clear.  If I go out 12 hours, they're very sure there's going to be no clouds. So that's great because six hours from now would be six p.m. So that's sort of the start of the night. 12 hours from now would be midnight. So this looks like it's going to be a clear night. Then six a.m. We're again showing some probability of some clouds coming back in. Now, how do you actually read this probability? It's easier to see down here how the boxes work. But basically, the bigger box contains 50% of the outcomes from 25% to 75% in a confidence interval. And then this smaller box extends down to 90% of the outcomes. And then the line coming up from that is the maximum number of outcomes, 100%. So what this one is saying right here is that in most outcomes and about the majority of outcomes, it looks like it's going to be clear or maybe just one octa. So just a few passing clouds, like 10% clouds. And then in 90% of the outcomes, it's clear or scattered clouds, like 20% of the sky covered. And then in all outcomes, it's either clear or up to 5.5 octas. So 5.5 octas would be more than half the sky covered in clouds. So that's not good, but there's only about a 10% or less chance of that happening by 6 a.m. Okay, so hopefully that made sense. You can see then as we get further out here, they really just don't know. All of these ones are saying it's more likely to be cloudy than clear, but you can see the range of possible outcomes gets pretty crazy. Well, in here, in the next few days, they're saying that it's going to be, they're pretty sure it's going to be cloudy. It's only like a 10% chance that it won't be or something like that. So I do find this useful. I do think it is a little hard to read because you have to actually find like what time a certain probability is talking about. You basically have to just sort of count the hash marks each one representing six hours. And for some reason, they're not marked. So but I wanted to show you how to read that because I think it is pretty cool that they even have this data available. But the main way that I think this website is really useful is to be able to go into your area and just use this map data to see how the clouds are moving and at what speed. Okay, next I want to show you Astrospheric and I just have up the free edition here just to show you how useful even the free edition is if you live in North America. I live here. This isn't my actual location, but I'm just going to pick up spot here in New Hampshire. I'll click get new forecast. And now you can see that red target mark is what this forecast is for. This is the location marked. And then if I want to save that, I can just go over here and type in an H for New Hampshire, click save. And it's that easy. I now have one of my favorites marked and whenever I log back into Astrospheric, that favorite will come up, whether it's on the app or the website. So how do we read this? So one thing I really like about Astrospheric is that it presents a lot of information in a compact way. So once you get good at reading it, it's really nice just to glance at it and understand what's going on. So up here, of course, we have the map of the clouds and we can animate that to show what the clouds are going to be doing. Down here, we have a point forecast with cloud cover, transparency, and seeing. And then he's also put in what the sun and moon are doing. So when they're rising and setting, along with ISS passes, and we have dew point and temperature. And if you click on any spot, so let's go to Tuesday night here at 10pm, it gives you the stats down here. Cloud cover zero percent, transparency average, seeing average wind one miles per hour, temperature 21 degrees Fahrenheit. And if I look up at the map, I can see how far away that cloud bank is. And then if I advance it, I can see this particular model, rdps predicts that it will be hitting me at right around 1am. And by 4am, I'll be in the thick of it. Now one thing to keep in mind with these predictions is I found that a lot of times they don't get quite when the clouds will reach you, right? But this at least gives you an idea that probably some part of the night on Tuesday is going to be clear. And then at some point, maybe it'll be 1am, like predicted, or maybe it'll be 11pm, this cloud bank is going to hit my location, right? So I really like the combination of the point forecast down here with the map up here, which updates when I click on the point forecast. Okay, what else to say about this? So the free one just comes with one model loaded, rdps, which is the Canadian model. But if you update to professional, which is 2.99 per month, or if you belong to an astro society that meets the conditions to get the astro society addition, you get all the additional models, like let's see here what is available today, rdps, nam, nbm, and gfs. So you get all of those. And you can do some really cool stuff that I'll let Daniel explain here. If I just look at live data right now, this is for a spot I love to go view in central Oregon. You can see, if I click this cloud later, this is just the rdps model. And let's look at, you know, right about now. And so this is rdps's prediction. But I can quickly switch over to the ensemble here and it kind of turns into a Christmas tree of lights here. But what we're seeing is each model and it's showing what it thinks the clouds are going to be at that time. And so the legend, which is maybe a little bit small off to the side is red is the gfs, green is the North American meso scale, and then blue is the rdps model. And so we can blow this up and look at this. And it lets us pretty quickly take a look at this and say, yeah, things are agreeing versus things are pretty out of whack. They're very much out of agreement. Let me back up a little here. And so this large structure that's moving through kind of coming off the west coast, likely we'll be moving east here, we can see how each of the models feels that's going to move across. You know, you have the gfs kind of having its leading edge a little bit further out than the other two. And then where they all agree, it'll turn white. It's just like, yep, we, everyone agrees. And then where they all agree, there won't be clouds. It's just clear all the way down to the ground. This is really, I think, crucial in various parts of the country. And I think the west coast is a great place to kind of show it off. I found over time, the rdps model doesn't do an awesome job predicting the clouds that exist just over the marine layer basically that exists over the Pacific here. And I actually am curious if we just kind of flip over and take a look at the satellite right now. Marine layer is kind of like these sets of clouds that form, and it can get absolutely massive. It can be a shelf that exists and kind of like covers an entire, almost continent size area off to the off the west coast. And I found over time, you know, the rdps model, let me sync these up. It's 530 here right now. We'll always catch these things. So if I kind of like toggle quickly between these, okay, interesting, right, like these, these kind of clouds here, we're kind of seeing those pop up. However, I found that the North American mesoscale does a great job of picking out and trying to find these marine layers, which end up impacting folks that are trying to view down in like Los Angeles areas where it's like you're low enough that this marine layer can actually flow in a ways and totally, you know, ruin a night of viewing. And so that's pretty interesting, right, like each model again, has kind of like sets of things that's really good to add and sets of things that it's still learning, I guess I'll say. Okay, this last one, this last tool that we're going to show in this video is Noah's Weather and Climate Toolkit. It does feel a little bit US centric when you first open it, but it can open these GRIB 2 files from any data source. So it does have maps for the whole world if you find GRIB files on, you know, non-US sites. But I'm mostly going to let Daniel explain how to actually use this program. But I just wanted to explain something really quickly here, which is this is a Java program. So you do need to install a Java runtime on your computer in order to use this. And if you happen to be on Mac, like I am, it's a little bit confusing, because if you don't already have the right runtime installed, you might do what I did and just Google Java Mac. And this first result is not what you want. It will bring you to Java.com with downloads. And it's the wrong thing. What you actually want is on Oracle's website. And to get Java 17, go down here, go to Mac OS, and either install this X64 or ARM64 DMG installer.  If you're on an Intel Mac, you probably want this X64 one. But anyways, you just then download this DMG file, install the Java 17 runtime, and then when you download NOAA's Weather and Climate Toolkit, it should actually run giving, if you just follow the instructions that are included in the zipped bundle there. So hopefully that helps get you set up if you are on Mac. I assume that it's a similar situation with Linux. You can get the right Java runtime from Oracle's website and then run it this way. And again, Windows, maybe that's the same thing. I think Windows is usually better about already having Java installed. All right. So now I'm going to turn it back over to Daniel to show how to actually use this toolkit, which I think is really something I've never seen, and it seems very powerful. NOAA's Weather and Climate Toolkit. And this is a great program, I think, for being able to get directly to the model data.  It's a really kind of like a versatile tool to slice and dice and look at data. And generally, before we introduce any new data onto asterospheric, it's been months in the Weather and Climate Toolkit and looking at it and animating it and changing legends ever so slightly and making sure that we're really dialing it for public consumption. So this is definitely, I think, a great tool to use. And it has some built-in functionality that makes things kind of easy to get at. So it can connect directly to NOAA Big Data, which apparently is stored on Amazon these days. But you could, if you wanted, just pull up a next-rad radar of your local area and just grab the latest data from it and look at it directly on the map. And so, I don't know, we could look at, I wonder if there's any out on the east coast. I mean, I'm sure there are, unfortunately. Let's take a look at Albany here. So I can say, yep, I want to look at data from today. Let's list these files. And so it'll go out and it'll chew through everything. Everything in here is usually in UTC time. So you'll have to convert to whatever time zone you're in. But generally, at the very bottom is the newest data and then going back all the way to the initial data from that day. And yeah, a quick double-click on that will download it and usually zoom you right into where you need to be looking. And so, as you can see, it looks like some thunderstorms are all across Massachusetts and Vermont, main area right now. So heads up. The other thing is you can change things like the elevation of the radar you're looking at. So this is one thing it can do.  And so where I go for grid data is somewhere anyone can go. It's the Nomads kind of like central distribution center of many, many models. And so this whole list, each one of these represents either a different model that's running or some subdomain of a larger model or maybe just a model running at different resolutions overall. And it's broken out into things like there are global models.  And a lot of the differences are the resolution of it. This S flux is usually the highest resolution that you can get from GFS right now, as well as parts of the model that they run separately like the chemistry section of the GFS, which is more predictive of aerosols and things like that in the air, which I think is kind of interesting. Folks, not in the US or in Canada, you could technically grab data from this and look at what the GFS thinks kind of the aerosol optical depth is above your location based on forest fire, smoke, etc. Then we get into the regional models. And this is just a fantastic list of everything that you could possibly want to look at. We'll focus on a few here. And most of the time when you go to a weather website or get weather data in the US at least, you're probably looking at this, which is the high resolution rapid refresh. You may be getting data from the North American mesoscale model. Or let's see, the rapid refresh is also one that's sometimes used. And so these are kind of like three big models that you'll end up seeing over and over again. There's a few new ones that are popping up here and there and I'll show some data from them. I think a really interesting one and one that we're excited to start working with just at Astrospheric is the National Blend model. So this is the NBM. And this is a model that runs hourly but incorporates data from like over 40 other models and builds up an ensemble based on that. So I think that's actually a really interesting thing. Let's start with the National Blend model because I think this is one that probably not many people see really ever that's out there kind of like looking at either civilian weather or certainly astronomy weather. So the way to do this is I think for starters at least, you can use this nice script that Noah's written that allows you to look at the GRIB file and kind of pull apart and pull out exactly what you want. Over time, especially if you're writing scripts or code against these to grab the models, automatically you may switch over and just go directly against, they just have big directory listings where you can get this. For today, we're going to use the GRIB filter. And again, so this is the National Blend model it's being computed hourly and it goes out 36 hours hour by hour and then they kind of decrease the temporal resolution of it. So okay, we click there and we say, all right, we want, this is like common formatting, you have the first four digits of the year followed by the month followed by the day. Let's go with 12 because there'll be plenty of data in there. Nope, sorry, I take that back. Let's go to 13. Today is the 13th. And then we can look at this and say like, okay, sub directors, now we're getting into the hour, right? So this is run a lot. I'm going to choose the 18th hour. And we're basically just traveling through a directory structure at this point. So this is where the need of it is. And so what this program has done or this website is it's gone through that GRIB and it's pulled out all the available variables inside of it. And you can select just one of them and download it. Or I can click start download. So if I don't know what these variables mean, like if I don't know what TCDC is, I can just say, start this download. This will be for the model that started at 18Z. So what is that? That's like, it's 11AM Pacific time. And it's the first hour of that model. And if we click this, we can see like 18Z then produced a second hour, a third and so on and so forth out into the future. And it goes a ways. And you can see at 36, it stops being hourly by hourly and skips to every three hours. This is again, this helps just save on compute and making sure they can go out of ways here. So we'll just take the first hour here and I'll start the download. And we're basically going to download the whole thing. Annoyingly, it didn't say how big it was, but I think if I open up my folder here, so this is like 105 megabytes. Okay. And so from here, I'm just going to put this on my desktop and then take the handy dandy weather and climate toolkits and say, I've got a file on the local disk. It's actually just on my desktop. Let's take a look and list those files. And so there it is. There's the blend dot 18Z, et cetera, et cetera here down to the grip to file. And I can open that and it will just load up. And so what I end up with is this rib file contains this list of variables. And we'll pick out a few interesting ones here in a moment. You can see the domain that it's covering. It covers basically the continental US minus Alaska here and pretty big chunks of Canada as well. At least covers a lot of the population of Canada at least.  This is actually a pretty high resolution model. I'm actually seeing like valleys in the mountains that this is predicting. We're looking at a parent temperature height above ground right now. And so we can just go to the filter and say like, okay, well, maybe I want to know just what the temperature is, right? Like tell me the temperature. And we're going to take a look at the height here. And so it's saying the temperature at two meters above the ground. And just to kind of put things in perspective, this is at 12 o'clock. So this is noon Pacific time. So the sun should have been pretty high in the sky for us. And if I take my mouse now and hover over any part of this, you'll see down in this cell that totally just disappeared, you'll see latitude and longitude and then the value. And then we can look at the legend and know that that value is being reported in Kelvin. So then at that point, you can convert 296.4 Kelvin to your favorite unit of measurement for temperature. And just like that, we can look at data directly out of this model. There's been no kind of like processing in between by any websites or apps or anything like that. And just see what the model is producing. And I think this is really cool. I'm very much a data nerd. So I get this won't be cool to everyone. But I hope that someone finds this exciting at least as we go into the dark cold months, there's just gigabytes of data out there to be played with. So we originally went into this model to look at its clouds. And so we are going to look at a variable called total cloud cover at the surface. And so this would be, I'm at the surface looking up what percentage of like cloud is going to be directly above me. It doesn't really care about where that cloud is. It could be fog. It could be it could be really high up in the upper atmosphere. And so this is going to show me that. And again, we'll take a look at the legend dark blue is clear. There are a lot of options for what layers you use. And so like these colors actually kind of like hurt my eyes. So you could easily go into grayscale here and say great now, you know, if it's black, it's actually totally clear. And then white is predicted to be very thick clouds. So this is producing a very high resolution prediction of what it thinks the clouds are going to be at or we're at that 12 noon. And so to save us the pain of me downloading tons of files, I preemptively downloaded gigabytes of data the other nights and produced some animations because one thing that we can do is we can actually tell this program to look at a bunch of grid files and kind of run through them in a time series and produce an animation. So let me let me keep that up. So I have weather data and BM cloud and view data. If I open this and list, I have just a ton of NBM files that I've stored. I think these were from yesterday. So unfortunately, I'm not picking anyone's weather tonight. Although if this video comes out in the future, it doesn't really matter anyways. And again, I can click through these, actually, stable this reset zoom. And you can see if I if I slowly click through them, you'll see, okay, the clouds kind of move. Cool. This is great. This is, you know, okay, how can I make an animation of this. So in this software, if I click the first one and then shift click the rest, I can select them all. And then this animation option becomes available to me. And I can say, okay, do I want to load an animation directly here? Or do something else can produce kind of like a video right away. I'll do here. This may take a while. And it's just going to run through each of those files and generate an animation. I will say, as this animates, there are some interesting things that you can see just based on what's going on here. And the first one that catches my eye, the primary thing that catches my eye, these clouds look really high resolution, like a very detailed, I can't zoom in while it animates. But if you look up just above this kind of, there's like a line here. Things get a little fuzzier, maybe lower resolution. And this is a property of this forecast model. The national blend of models is literally taking different models and statistically blending them together to make a ton of sense. And not all models are of the same resolution. And so something that's covering, say, the US here is probably pulling from the high resolution rapid refresh, where something up here may be pulling from a lower resolution model. And so that's kind of why you're seeing those lines. And so a lot of these models come with little quirks like that that you can either see right away or you learn about as you begin using it. And so relatively quickly, I can build up an animation and see what the NBM is projecting. And so if I were using this data for the night, if I was really traveling somewhere, this was very important to me. And let's say this is the area that I was going to go somewhere in Virginia here, I can then take a quick look at this model and exactly how these clouds may be moving. And again, you're probably not going to find this model on any other website at this time or really any other service. So this I think is kind of a cool thing to be able to do. The time of this is off. I don't think this is at night. But regardless, conceptually, it's the same. You just download the appropriate ribs that cover the time period that you'd want to be able to look at. And is what we're seeing a visualization or is this using actual photography? Total visualization. I love that question because it's like this model is getting high enough and like obviously if I zoom in, we can start to see pixels. And I'll do that actually over here. But it's getting high enough resolution to where this, you know, you could pretty easily fool someone into thinking this came right from a satellite, you know, just from one of the ghost satellites. If we zoom in, we'll start to see the pixelation. And I guess technically you could do this with satellite data as well. But the interesting thing maybe to look at again, because this is a grid, if you look right here in this cell when I hover over, it's going to tell you where in the grid I am. I'm 1,602 grid cells from the left edge of this. And I'm 686 cells up from the bottom of this. And so this is the native resolution of this model. Which again, I think is really cool because, you know, even on astrospheric, it's computationally expensive to produce imagery at native resolutions. And so sometimes we have to we de-resid a bit, you know, to make the system go a little bit faster. And kind of decrease the amount of data we have to send to everyone's phones. But with this app, you can absolutely tear your computer apart and zoom in and see kind of like actual native resolutions of the weather models. So you're now seeing the names of everyone who supports this YouTube channel over on patreon.com slash nebula photos. It's an excellent community of dedicated amateur astrophotographers, just people who want to learn and are very willing to share their own expertise. We have over 800 members now. There's an active Discord that you can get involved in. And I can't thank my Patreon members enough because I'm now doing this full time thanks to all of you. And it is what has allowed me to make these videos and to really pursue this as my own business. So thank you so much to all my current Patreon members. And if you enjoy this channel, I think you will get a lot of benefit out of joining my Patreon community. It starts at just $1 a month. And for that, you get a bunch of perks, including direct messaging support with me, a monthly zoom chat with the whole community, a monthly imaging challenge organized on Discord, where we pick different targets every month, and a whole lot more. So if interested, head over to patreon.com slash nebula photos. Till next time, this has been Nico Carver, Clear Skies.