There is little to no evidence that the current degradation of the US upper air backbone is regularly contributing to degraded forecast skill. That might change as we head into the more active northern hemisphere winter.

>and every other app I check shows it.

Other apps have a different government?

> I've heard some commentary in the past that 5G interferes with data measurements heavily used for weather forecasts (something about satellite measurements?)

The short version of the "spectrum" issue is that 5G is being allocated in bands very close to the microwave spectra where atmospheric water vapor emits. A bevy of public and commercial satellites in low-Earth orbit passively monitor these microwave spectra and produce extremely important information that is assimilated into numerical weather models.

The federal government sets limits on "out-of-band emissions" for operators emitting in the allocated 5G spectra. These emissions can bleed over into the microwave water vapor bands, creating noise that masks the natural presence of water vapor. The limits for this out-of-band emission is on the order of ~10-20 dB, and there's some work in the atmospheric science literature suggesting that this is enough to confound certain water vapor retrievals. That basically means we lose these observations that help constrain the forecast.

There isn't much indication that this is a serious issue in day-to-day meteorology at the moment. But it's an issue which will be significantly more difficult to unroll and claw back than to simply protect key spectra in the first place.

A couple of years in Tokyo gave me great trust in certain weather apps for eerily precise minute-by-minute rainfall predictions. After a few years back in Sydney, my trust in them has dropped back down to the usual uncertain baseline, and my reliance on them has been mostly replaced with my own rough assessment of the air and sky.

On recent visits to Tokyo, with my current habits, the general crowd (who presumably check some app) has been the more reliable forecast. Either everyone has an umbrella in their hand or they don't. And I discover whether I should have brought my umbrella or if I'll be lugging it around pointlessly, just far enough from my hotel to be stuck with my choice

WeatherNext 2 was based on the FGN architecture described in [1]. It was explicitly designed and trained to produce ensemble forecasts (it was trained in such a way that the output ensemble optimized a CRPS metrics). In fact, it was a set of 4 different model weights, each of which was seeded with a random noise vector to produce an array of 16 forecasts for a total of 64 ensemble members. WeatherNext 3 trimmed that down from 4 to 2 separate model weights to use.

[1]: https://www.nature.com/articles/d41586-026-02643-w

Nexrad? NOAA?

The "Google Weather" Android app links to a page that says

>The Google Weather forecast is created from an internal forecasting system that utilizes weather models and observations from global weather agencies.

It also lists the data sources it uses, but is vague about what model(s) it feeds the source data into.

https://support.google.com/websearch/answer/13687874

Click on "Explore Weather Lab" at the bottom of the page

Clicking "Try WeatherNext 3" at the top of the page takes you to the section with different ways to use it.

Oh, they finally launched it in Europe. Let’s see whether it’s as good as people say. :)

> It is built into Apple Weather after Apple purchased them.

Did Apple lobotomize the tech when they integrated it, or lose access to whatever upstream data Dark Sky had access to? Apple Weather is comically bad (though not nearly as bad as Google's weather searches).

While that might be true but apple weather still fails compared to dark sky. I still don't use it for radar like I did with dark sky - they effed up the UI.

It feels like dark sky was more proactive with push notifications

its nowhere near as good as it was in Dark Sky. the apple weather app is pretty bad where I'm at.

Time and units are toggled from the 3 dot menu. https://i.imgur.com/Vi6tvGW.png

Init time makes sense to me, it's when the forecast/model is initialized, letting you look back in the past to watch the change to the present and future.

The sidebar thing is weird, but it also can expand out.

I think you’re the wrong audience. The first 3 points are just meteorology standards.

Time being UTC and init times are both meteorological standards, so I'm not sure why those are problems.

The legend and map colors seem strange, too. I haven't really put my finger on how or why (and I'm not going to spend much time on it), but:

Everything seems dark and desaturated, as if the the only clue we have (color!) for matching things up has been deliberately reduced.

And then: It sure does feel like the legend has even more of whatever-that-is going on than the map does.

I find it difficult to look at the map and understand the information it relays at the same time.

> Problem is that the WN3 grid is still quite rough (5km) - but a that's a brutal improvement for many places compared to many other global models.

That's a pretty apples-and-oranges comparison. One would almost always use a high-resolution regional model if you needed certain details for different forecasting applications like renewable energy.

It's also worth noting that the 5km outputs are from a model decoder head that was trained against temperature and dewpoint at surface stations. According to the Rasp et al (2026) preprint, this head was designed for continuous sampling; the choice of a 5km grid is arbitrary. What we don't actually know is how well the model handles shocks like a frontal passage or impacts from things like outflow from storms - or even evaporative cooling from precipitation. We are limited to the output that DeepMind publishes; we can't run the model and stress test these things on our own.

That's all a long way to say that the 5km resolution is (a) limited to temperature fields, and (b) we don't know if the "additional" resolution has any impact whatsoever on the phenomena that one would typically use a mesoscale-resolving forecast for.

You could use this model as another input into the country-scale models.