WeatherNext 3

So yeah, when it rains, it might take a few hours for that to flow into your weather app.

It's a direct result of DOGE.

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

I remember being _very_ impressed like 15 years ago about how I would have _hourly_ weather forecasts (in particular around the rain) that seemed like magic! And then things... seemed to slowly get worse (at least in Tokyo)

a couple years ago I was chatting with a friend in Kyoto. They used to live in Tokyo and had made the _exactly_ aligned comment like: "I was used to the rain forecasts being not so accurate anymore. After moving to Kyoto they seemed to be quite good! And now they're also bad here. Am I cursed?"

I looked at some 5G rollout maps and you could see Kyoto rollout happened a bit around the time frame they were complaining about....

Anyways I think for most people (at least for myself) weather forecasting seems like this odd dark magic that can't work at all, but there was a window in which it _felt_ like it was super accurate. At least in my personal experience

I'm building a tool right now that uses an ensemble to determine wind gust likelihood, which is useful for safety critical work on construction sites and the like.

As a former Googler, I wouldn't at all be surprised if this is an integration that is "planned" -- but just not done yet.

And some good handful of people are planning to wring a promo out of work. "Implemented weather UI in Android that is 63% more accurate." ;)

I guesstimate that it has less than 50% accuracy for my area

Would that be practical for weather forecasting or not really?

There's also https://nickleenders.github.io/verisky-scoreboard/history.ht... which tracks the trends over time.

It features a couple of AI and NWP model forecasts for comparison.

https://acmeweather.com/

https://www.theverge.com/tech/883089/acme-weather-forecast-a...

'Nowcasting' is an area of active research, both with machine learning and with physics-informed or visual flow approaches.

Part of the problem from the machine learning side is that these are _huge_ problems. NVidia's StormCast (https://research.nvidia.com/publication/2024-08_kilometer-sc...) works globally at kilometer scales, and you can imagine how big those grids are. Even with patch training, you're dealing with very large datasets.

At the same time, this is not exactly a high-profile area of research. National weather centres focus on actionable medium-range weather predictions, and meteorologists can look at radar themselves and perform mark-one-eyeball predictions for very short-range watches and warnings. Some private-sector actors will pay for short range predictions, but they're often looking for something hyperlocalized (e.g. weather at this particular construction site, for crane safety) or specialized (near-real-time cloud and wind predictions for renewable energy).

Most public-accessible weather predictions are downstream of either a public-sector effort (which doesn't internalize benefits, leading to under-resourcing) or a byproduct of another private-sector offering.

It is built into Apple Weather after Apple purchased them.

"We create a 3-channel image where the red channel represents velocity in the x-direction, the blue channel represents velocity in the y-direction, and the green channel represents change in storm intensity" and then use computer vision techniques to predict what happens next

post was written after a discussion here on hn: https://news.ycombinator.com/item?id=3187326

Link?

edit: The link for the demo is as thus, https://deepmind.google.com/science/weatherlab

In a high-level view, it's the result of specialized decoding heads.

Traditionally one would take gridded forecast outputs, then process those with comprehensible actions like "find all local pressure minima in the ocean, then filter to ones which correspond to warm cores, etc." to infer (diagnose) the presence of a cyclone.

One problem with this is that gridded forecasts suffer from known biases and tradeoffs. For example, a forecast on a ~25km grid is just on the edge of being able to represent the eye of a hurricane (50km scales), and it certainly can't accurately represent the sharp transition of wind in the eyewall. That means that the forecast winds are almost certainly a smoothed (and therefore less intense) version of what observers would see.

The WN2 approach (paper: https://www.nature.com/articles/s41586-026-10953-2) adds a direct readout head to the model: given latent-space access to the full forecast, it tries to predict the bona-fide cyclone observations (https://www.ncei.noaa.gov/products/international-best-track-...).

It's kind of like a post-processing or bias correction (see for example https://www.ecmwf.int/en/about/media-centre/aifs-blog/2026/a..., which applies in physical space), but by having access to the model latent space and by being included in model training it is (probably!) higher-quality than a pure, after-the-fact approach.

- Time is UTC rather than local by default.

- Units are imperial rather than metric (i.e. not basing it on users locale)

- The concept of init time is not initially clear at all, so it's confusing when you click the calendar icon to see the weather forecast for a future date only to see it isn't an option, you have to use the slider.

- When changing the sidebar to the "detailed" view, most of the hovering element is cut off by the container so you can't actually read it.

And it took longer to write this comment than it did to find these issues. It's fine since it's clearly marked as experimental, but I disagree that it's "really easy to use" :p

> WeatherNext 3 will power weather features in Google Search, the Gemini app and Google Maps, as well as the Google Maps Weather API and Google Earth Engine.

https://dataconomy.com/2026/09/04/weathernext-3-ai-forecasts...

So I assume the main way would be googling "weather Los Angeles" and it will be powered by the WeatherNext 3 models

They also open sourced the last one and are doing B2B/enterprise arrangements so maybe other weather apps are experimenting with it.

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.

Quite a few country-scale models go down to a 1-2km grid nowadays. This is very helpful in complex geography like mountains and alleys.