... And here's the graph for up-to-the-minute detail: https://forecast.weather.gov/MapClick.php?lat=37.3367&lon=-1...
It ain't pretty, but it works and you've already paid for it.
I've been following the public weather data scene for a long time and I'm pretty sure that UX for all the NOAA/NWS web sites is horrible by design, so to speak.
After all, there are plenty of well designed .gov sites (e.g. https://recreation.gov/), but just try browsing around weather.gov for a few minutes. It's horrible. I mean, the data is top-notch and it technically works, but the experience is awful. Weather.gov has had the exact same site for at least a decade.
This is completely speculative, but I would not be surprised to find a link between campaign contributions from the likes of the The Weather Channel and the decision to completely underfund the web development teams at NWS. Nothing else explains why some of the United States most valuable public data is presented on a web site barely more functional than Craigslist.
The local radar maps still use flash for loop animation so they're going to have to do something in the next year to keep that going.
https://radar.weather.gov/radar.php?rid=DIX&product=NCR&over...
That's spot on, John Oliver did a whole episode about this: https://www.youtube.com/watch?v=qMGn9T37eR8&feature=emb_titl...
I use Dark Sky and it is typically much much more accurate than other weather apps when it comes to heads up of inclement weather coming in the next ~hour, and I'm very sad to see Apple killing it.
Almost every podcast player for iOS and Android use it.
> If it weren’t for capitalism, Dark Sky wouldn’t have existed.
If we enabled citizens of this country to pursue their passions by providing them with a UBI, it absolutely would have existed.
Damn it.
Switch to radar view in the app and it will show you the exact geographical distribution of rain in your city right now. This comes from national weather data.
If that's inaccurate then something is wrong with the government data. Or maybe Seattle rain is somehow much patchier at a finer level than government data is able to resolve?
FWIW in New York City its assessment of the next five minutes has always been literally perfect. It might say it's going to go from hard rain to nothing in two minutes... and it does, right on time.
It gets increasingly noisy as you go to 15, 30, and 60 min out, though.
I'm curious then what users see in Seattle when it's raining out the window but the Dark Sky radar maps says it isn't.
Are there swiss-cheese holes that are obvious processing artifacts? Is the whole city somehow below some threshold so it doesn't show rain anywhere? Are there patterns of extreme local variation in rain that get mistaken for noise and deleted?
The solution is not necessarily simple, but identifying the culprit should be fairly obvious, no?
For one thing, the resolution of radar is finite where one "pixel" (gate) is quite large, and everything under it is assumed to be constant. Precipitation at a spot on the ground is the result of everything happening from sea level up 40,000', and we don't have exact properties (temperature, dew point, humidity) for the atmosphere at all of those locations. We can fill in some gaps with models on supercomputers, but they're just models.
I’d like to know more about their modeling. How much do they need to specialize on region for accurate prediction or data collection? Are varying prediction qualities based on different climates, or is it from bias in data collection?
I know nothing about weather, but I have to assume the weather patterns in Seattle are just very difficult to predict or analyze at such a fine granularity.
I think these personal takes are plagued with confirmation bias.
Its fast and has most of the radar products, including velocities. I originally bought it to answer the question "Should I shelter from this tornado?"
The downside is that there is no forecasting, but thats freely available through the NWS [1].
Not affiliated, just stoked. [0] https://www.radarscope.app/ [1] https://www.weather.gov/
1: Both seemly wrong more often and more importantly unclear whether they mean a greater or lesser chance or amount of rain or snow, which makes a big difference when you are, say, biking to work.
My team pulls a lot of weather data for utility engineering work we do, and the NOAA APIs have proven very hard to navigate. Some data only available via FTP, lots of different weather station networks with different metrics (e.g. lots of variety in the cloud/irradiance data).
Darksky provided a nice clean interface, but I guess we'll go back to cobbling things together directly from NOAA.
I'd floated a Weather-Service-In-A-Box to my clients a few months ago, but got no takers. The idea was something you could set up on a small number of instances fairly quickly and leave running to harvest NOAA/ECMWF/etc data. Fairly simple front end to answer queries. Data tiles for the visuals. That sort of thing.
If people (with money) are interested now, hit me up.
Storage and processing is relatively easy since it's just a matter of throwing resources at it. However, it'll be damn expensive to store months or years of historic simulation data.
The difficult part is writing and maintaining the processing scripts. The different weather services store data in different formats, have different ways to download the data and sometimes change data structures or fall over.
Edit: removed a spurious sentence.
If you wouldn't mind: how do you go about creating a simulation around the empirical data? This seems to be very core to how Dark Sky is able to provide such accurate realtime data for geographies in between weather stations.
I think it will be pretty hard to do well in open source though. Server costs and keeping things working when NOAA (etc.) sources change is probably going to be expensive.
There are also a lot of open projects that distribute the work of handling tweaks to parsers and source lists when the upstreams drift.
Even globally I can't imagine the minutely data from reporting stations everywhere being "big data", maybe in the 10s of gigabytes a day (very rough guess, I haven't looked into this specifically)? I'd still be willing to bet API / web requests dwarf the processing and bandwidth requirements of the raw data for a public service like this.
That probably does put it pretty reasonably in the realm of self-hosting if you put a threshold on how much historical data you want to keep and geofence the region you care about.
Develop the (Ideally extensible) logic to parse a given government funded weather source into a common format, and have “API consumers” run their own instance of services (or set them up an instance for $ and use the profits to help with project costs).
Depending on your needs, I may have data you can use. I only process forecast weather simulation data - not weather stations. I'm a solo dev so I can't offer high levels of service, etc... but feel free to contact me.
https://play.google.com/store/apps/details?id=ca.gc.ec.weath...