The serious answer is that the way you'd try to figure this out is by combining weather radar, satellite imagery, and a nearby surface observation to try to estimate the current conditions. But there can be a latency of up to a few minutes from these sources, and they could disagree with one another. You have to use them to bootstrap your near-term or nowcast product, but enforcing consistency with recent real-time and the nowcast is quite hard.
It's a surprisingly nuanced technical challenge. Most of the time, it works out just fine (e.g. if there is no weather). But people are awfully good at remembering when these sorts of analyses end up being wrong!
If you're interested in providing on-the-ground condition reports, install mPING: https://mping.nssl.noaa.gov/
I keep this app on my homescreen and try to report when very light rain starts, since it's not always obvious from the reflectivity data. Ultimately the user reports get fed into things like improving the model, and more data is always good.
You'd think that if their users are accepting that level of communications with the mothership that they could ship some AI model to hear rainfall in the wild, and thus improve their live weather data.
The thing is, even if you did have a super reliable in situ "rain detector", how do you combine it with the existing datasets like weather radar, which is a gridded product? This is actually a really, really difficult sensor fusion problem when you then super-impose product requirements like the general location real-time detection map and the inputs necessary for whatever internal nowcasting system they use.
Actual ground observation weather stations are fairly rare outside of places like airports and major news stations.
“Is it raining here right now?” Is a harder question than you’re giving it credit for. Radar can show rainfall at as low as a couple thousand feet altitude, but if conditions are right/wrong (depending on how you look at it) it never reaches the ground.
But rain or other objective information? I suppose it works, maybe a bit like “limit IP address tracking” — cloudflare or other edge provider could mediate so Apple gets the data, knows it comes from an iPhone (to prevent bad data attacks), but Apple can’t tell what phone sent which data.
(the privacy concern being documentation of when you were inside/outside/etc).
But this sort of technique only works for medium range forecasts. Short-range precipitation nowcasts are almost always a single, deterministic run of a model that extrapolates from patterns in recent radar imagery. They aren't bias corrected at all, so you can't use observations in the same way to improve them.
And the ship has been towed beyond the environment.
There is nothing out there, all there is is sea, and birds, and fish. And 20,000 tons of crude oil. And a fire.
Less sarcasticaly speaking I think there is always weather. Maybe what you mean is “no significant change in the weather” neither in time, nor in space.
"Hey Siri, is it going to rain?"
"It doesn't look like it's going to rain today."
"It's raining right now."
"It isn't raining right now."
I live in Toronto, Canada, which stretches about 40km east-west, and 20km north-south:
If the west-end (Sherway) gets hit with rain, but the east-end is dry, did it rain "in" Toronto when folks in Scarborough didn't experience it? Was the forecast wrong?
If it snows in North York but is dry at Billy Bishop, was the precipitation forecast "wrong" for one particular group of people?
Meanwhile the Google Weather app constantly insists I live in Frankfurt while I'm in Warsaw.
On a serious note I'm dealing with this as well - I live in a middle-sized city in the centre of the country, not in Kraków!
The technical limitation would be on the weather data size: what is the granularity/resolution of the radar data on where rain is actually falling?
* https://en.wikipedia.org/wiki/Canadian_weather_radar_network
Further: what is the geography of the area, and how does that effect things as well? Toronto specifically has (a) all sort of heat island effects, (b) certain areas are effected by the lake and how weather systems cross it at certain angles, and (c) has enough of an elevation change going north of the lake (e.g., Niagara Escarpment) that there are a few ˚C change in temperature that makes the differences between snow and rain.
So unless you are sitting next to the the weather station that Siri is getting data from, I would not expect it to know 100% of the time.
I don't, but as a result, I expect it not to guess.
Windy.com lets you compare different models for a specific location, it also includes the size of the area per model: https://www.windy.com/?49.339,5.054,5
GFS is area is 22km, ECMWF 9km, ICON-D2 2.2Km, Arome 1.3Km, and UKV is 2Km. Even in a 1.3x1.3Km area it may not rain everywhere at the same time.
And then there's also the time element, so it's 1.3Kmx1.3Kmx1Hrs (or 3Hrs). So lot's of variation possible.
>”These observations are then fed into numerical prediction models to forecast the weather.”
In other words, the forecasts come from models, not necessarily real-time station readings. Those readings are inputs into the model, and the models may not get updated fast enough to reflect current conditions.