Dark Sky - Weather Prediction, Reinvented
kickstarter.com
kickstarter.com
I experimented with something like this in grad school to tell me when it was "safe" to walk home, by scraping the past hour's NOAA radar images, coursely gridding them as a poor man's way of reducing computational needs and noise, and throwing them into some regression algorithms to get a prediction of rain probability for [0,10] mins, [10,20] mins, etc. out to an hour.
It wasn't useless, but I gave up on it when it became clear that some significant computer-vision work was needed to get good accuracy, mainly object-tracking algorithms: correlating when a blob in one radar image is "the same" as a blob in a previous radar image, even though the blobs change shape/size semi-rapidly (and split and merge), in order to extract motion vectors. At least with the regression techniques I tried, it doesn't seem that they were able to implicitly extract the motion information from just the sequence of images as input data, because prediction was much better in a small set of images I manually labeled with motion vectors. Hopefully they have solutions for that!
In any case, I'd definitely use an app like this. I often predict short-term weather by pulling up one of those radar animations and extrapolating the motion of a storm in terms of "finger widths on monitor per 5 frames of animation" or something, which feels like a not-very-21st-century way of doing it.
So... I made a live wallpaper for my phone that would show the latest NOAA radar image for my area. I find that I can do a far better job of short term weather prediction with just a quick glance at my lock screen than I was getting by scanning short term forecasts.
https://market.android.com/details?id=com.appidio.radarlivel...
(That's the free version. The paid version animates the last 10 frames.)
It's rather surprising how easily having a bird's-eye view of the weather sort of fades into the background of your consciousness and becomes an accepted fact. I use my phone often enough that it just feels like I have an extra sense that tells me what the weather's like. Very odd.
Because what appears to be missing is any sort of feedback mechanism by which to improve the accuracy of it's predictions on the same scale as which it is making its predictions.
In other words, if the prediction was rain in 8 minutes for 15 minutes, how can the program determine if it was accurate? (And suggests the question, is rain in 4 minutes for 18 minutes an acceptable level of inaccuracy?). Where does the data that it was 18 minutes of rain come from?
As for competing with looking at NOAA radar images, try a little experiment: Next time rain is headed your way, look at a radar animation and try to quantify when the rain will start. The fact that I'm completely unable to do that (even while looking at the damn radar animation) with any accuracy is the reason I started building this app.
I, myself, happen to work in a meteorological company and have an easy indicator: When our meteorologists all take off at the same time, rain is imminent.
Topological or topographical? It's kinda both in your example.
I'm no expert, but I don't think topology features much in the weather models. It's mostly numerical analysis and statistics.
It is very popular and it beats looking out of the window by a large margin. When it is raining you can see very accurately how long it's going to take for it to stop so you can decide if it's worth waiting. When it's not raining but it looks like it could be going to rain you can similarly make good predictions.
Note that they're not doing actual prediction, they're just showing radar images of the clouds of the last hour.
Your points about feedback are valid, however.
Does anyone think it could also be useful to tie a twitter feed in with weather information? I feel like weather is one of those things that ppl complain about on twitter all the time; some simple geolocation and keyword matching and you could pull up recent geolocated tweets near you. Could be a nice qualitative weather report to accompany the actual data (people like a personal face on the weather).
Thinking about this more, its brilliant that they put their project on kickstarter. It didn't click for me that people are using kickstarter this way. They get some extra marketing and some extra $. Interesting also that they aren't afraid of someone taking their idea because they have a head start.
Good moves.
We figured out how much we needed to survive for 3 - 4 months, plus the cost servers for a certain amount of time, then factored in Kickstarter / Amazon's cut and the cost of the backer rewards.
We kept getting a number that was too high. So we eventually decided "Screw it! $35,000 it is!". It's still probably too high... but we wanted to avoid, at all costs, asking for too little money and then not being able to deliver to our backers.
This reminds me of that, only a much better display.
However, I wonder if it makes more sense to build the business around providing the data on the backend rather than creating a beautiful app.
If the point is this to let users know whether they're about to get rained on, all they need is a bit of text to get the message, rather than a beautiful flowy animation. It looks awesome - I just wonder if it's just going to get in the way after the 10th time checking it. A home screen widget would be ideal (which is why there's lots of weather app widgets already.)
It only offers the live radar map animation, based on your GPS location, but that's often pretty good for eyeballing when the rain is going to hit you, or when you are going to hit it if you are moving. Free, displays ads.
Got a spare fiver? Let them know you agree :)
Why do they need so much money? for servers?
So yeah, we need money for servers, and to pay for rent / food / etc for the several months it'll take us to make this thing.
When you start doing the math, a lot of the money we're asking for disappears. 10% of what we raise goes to Kickstarter and Amazon (for payment processing). Another 30% - $35% goes to paying for the Kickstarter rewards, and then the government takes it's cut. What's left is a minority of the money.
We'll be writing more about how it works for our fellow nerds soon.
Good luck, please come back and post here when you are done. You already have one future customer here :)
Talk about a personal assistant--it could remind you to bring an umbrella, or tell you to take an alternate route because precipitation 5 minutes from now might lead to delays on your regular commute, or change the way we learn about and deal with flight delays.
Of course, this is predicated on its ability to accurately predict the weather. I took a lot of meteorology in college and I don't think it's outside the realm of possibility, especially when coupled with historic data of past weather trends for specific locations.
Only one way to find out.
I was also building an app based on weather data do I have some idea of the expenses involved and I quickly realised that my (admittedly niche) product couldn't be supported by small once-off payments like the app store.
Anyway I guess I'm just spoiled with this type of info already built into android so guess its needed on the iphone which explains why its an iphone exclusive.
No, that's because your average developer is not using the same platform as your average smartphone user.
Very useful for example when I want to go inline skating on the Danube dike in Bratislava: how much time do I have until it starts raining? :-)
I get wanting to keep the app simple, but I have to say the screen shots are crying out for the current temperature up in the header, next to the time. After all, you need to know if it's going to rain AND how warm it is to prepare to go outdoors.
The probability of precipitation (red bar beneath) seems lacking. I think just sticking with the green bar (amount of precipitation) and varying the opacity according to the probability of precipitation would be more intuitive.
Basically, we rely on the fact that while weather is chaotic and turbulent in the long run (by "long run" I mean hours to days), it's fairly linear on the timescale of minutes.
So we're able to extract velocity data from NOAA radar images, and use that to project the storms into the future. Some storms are more coherent than others so we're constantly monitoring how accurate our predictions are and adjusting the projections accordingly.
Sometimes this technique breaks down, but it's surprisingly rare.
The moisture (gaseous water) in the rising air condenses at altitude due to low temperatures, and hence frees additional heat which increases the upwards vertical motion of the airmass. This causes the cloud to grow. At some point, there is too much condensed water in the cloud and the water falls back down - a rain shower.
The development of Cumulonimbus clouds can be observed on weather radars. I'm guessing that they have an algorithm which, given the known conditions of temperature and humidity computes the time and size a Cb cloud has to be in order for a rain shower to start. The growth rate of a Cb cloud can be inferred given the vertical temperature distribution in the atmosphere.
It could also be that things are much simpler and that precipitation can be predicted from knowing the density of water in a cloud, which is what a weather radar measures. I am not a meteorologist.
The first problem is that generally when talking about weather predictions you're talking about a large area. Asking, "will it rain in New York?" can be a loaded question if you're talking about a scattered storm. My understanding is that a lot of the time when you hear 60% chance of precipitation, the weatherman doesn't mean 40% chance it won't rain in the area, but that roughly 60% of the area or population will see rain. So the first advantage this app has is that it's forecasting for you individually in a very specific location, rather than for an entire TV or newspaper market.
Secondly, I think the short time frame is actually much easier to predict. On a short timescale you basically just look at the radar for the past 15 minutes, and see where a storm if any is heading. I do this all the time with weather.com. Open up the radar, see if a storm is nearby and where it's headed. It seems much harder to predict what a storm will do hours or days out, than what it'll do in 15 minutes.
We're not meteorologists, and we're not even fancy-pants computer science academic types. We just got sick of this app not existing...
So we hunkered down and learned about Computer Vision and experimented a whole lot (which resulted in another fun project: http://tinyfaceapp.com). There are so many awesome open-source data-processing libraries out there, which when combined with cloud-based computing, gives individuals so much tremendous power to pursue their weird passions.
Scenario where it's useful: I walked to the grocery store and it's raining outside. Should I wait it out by staying inside or is it going to continue for more than 30mins?
Even looking at the radar map online didn't help. It was nearly impossible to extrapolate mentally the stuttery radar animation.
This is a nice little mobile app that is location aware.
on the topic: pretty neat. depends on how accurate it is though. Hope European version will be released to play around with.
RPI, huh?
Notice something really special about your application and your users: You are LOCAL, geographically LOCAL.
So, if you want to get users in, say, Boston, then it's essentially irrelevant what you are doing in San Francisco, New York City, Miami, etc.
Sooooooo, to get started with only a small, tiny, cheap server farm, get started in just ONE city.
When you have some good 'traction' and/or revenue from that one city, expand your server farm, etc. to another city, say, an adjacent city.
Then grow across the country and world this way.
Besides, your ad revenue will be similarly local.
So, you don't have to bite off the whole country in one bite to be successful and, instead, can do well one city at a time.
Also, this fact gives you a 'geographical barrier to entry' since when you are in Boston you have no competition from a company in San Francisco or Miami.
Er. "Fuck you asshole".
Not the response you were going for I expect, but nonetheless my automatic response.