Especially since you don't actually _need_ raw pressure data to be useful. Pressure rate of change over time is still useful and produces improved accuracy in some local forecasts.
But not only that, the researchers at UW under Cliff Mass found that you can do on-device ML to clean the quality of the data, remove errors, and live-adjust to MSLP on-device, without even needing the dense network of sensors nearby for error correction.
It's 100% doable, but it just takes some hard science and effort.
> That makes it extremely noisy for detecting slow moving metrics like the weather
Yes, but, the noise problem was solved half a decade ago.
A 10cm change in elevation at sea level results in a 0.0001% change in atmospheric pressure [0]. On the other hand, weather-relevant pressure changes operate on scales of ~0.1% [1]. By my math, a small weather-relevant pressure change would be equivalent of someone changing their elevation by 100 meters.
Additionally, on a modern phone the barometric data can be adjusted against the accelerometer and GPS to mitigate changes in elevation (especially relative to, as you say, a slow-moving measurement).
[0] https://www.mide.com/air-pressure-at-altitude-calculator [1] https://barometricpressure.app/content/understanding-high-lo...
It isn't the GPS or position of the device you need to correlate, its trustworthy data. Is the device on an elevator, or did the pressure actually drop?