Negative. Clicking on any appearance of "Try WeatherNext 3" brings me to this nonsense:
WeatherNext 3 is being integrated into the Google products and tools that billions of people rely on – like Search, Maps, and Gemini. The model is also available for enterprise use, across a range of different applications.
Gain access to high-resolution forecasts, without model setup. Including real-time operational data and historical forecasts.
Try WeatherNext 3 in BigQuery, Earth Engine, Google Maps Platform and Google Cloud Storage.
---
None of those words are links or demos. It's just noise.
> 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.
The online viewer could really, really use Wind Direction as a compass bearing. Its super important considering wildfire/bushfire, air quality, ocean-going conditions, and a myriad of other things.
In the energy world, this should be such a boon over the classic NWP (Numerical Weather Prediction; complex ML models), but I've not seen it implementated. Anyone with experience of these models over classic NWP?
> 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.
This would not likely be a great idea since you reduce your ability to understand inputs except for a few parameters. Explainable inputs become very important for many down the line processes used by government and industry alike, because said inputs and their predictive certainty can be quite informative, even critical, for accurate mesoscale prediction.
If model members were available, with all the usual measures, thats a fantastic place to start looking at serious inclusion. It doesnt seem thats the case, however.
TLDR: the input data for the model now also includes real-time observations (satellite and weather stations) on top of the typical (re)analysis data, improving model resolution, run frequency and timestep frequency.
There's reduced data available for initial conditions, so I wonder how that will effect things:
* https://apnews.com/article/weather-forecasts-worsen-doge-tru...
* https://www.independent.co.uk/news/world/americas/us-politic...
A good book on the history of forecasting, The Weather Machine: A Journey Inside the Forecast:
* https://www.andrewblum.net/the-weather-machine-2
The exploration page is really easy to use. Having more reliable weather predictions is a genuinely useful product.
They even have an interactive world map with all the layers. It's so cool when an announcement comes with the actual goods.
I'm not seeing that kind of demo. I just see a video, a bunch of future-tense words about what could be done by someone, and the requisite hype.
Link?
edit: The link for the demo is as thus, https://deepmind.google.com/science/weatherlab
Click on "Explore Weather Lab" at the bottom of the page
Thank you! That works.
Here's a URL for the demo for those who -- you know -- like to click on links and see stuff happen: https://deepmind.google.com/science/weatherlab
After signing in I get a 404.
404. That’s an error.
The requested URL was not found on this server. That’s all we know.
Clicking "Try WeatherNext 3" at the top of the page takes you to the section with different ways to use it.
Negative. Clicking on any appearance of "Try WeatherNext 3" brings me to this nonsense:
WeatherNext 3 is being integrated into the Google products and tools that billions of people rely on – like Search, Maps, and Gemini. The model is also available for enterprise use, across a range of different applications.
Gain access to high-resolution forecasts, without model setup. Including real-time operational data and historical forecasts.
Try WeatherNext 3 in BigQuery, Earth Engine, Google Maps Platform and Google Cloud Storage.
---
None of those words are links or demos. It's just noise.
It's literally right below that-- the "Explore Weather Lab" button.
The button label is "Explore Weather Lab", isn't it?
(I'm not trying to be pedantic, but if someone is having trouble finding the button, the exact text is helpful.)
Also, here's where the button takes you: https://deepmind.google.com/science/weatherlab
Apologies, was walking and distracted. Corrected my comment.
Is there a convenient way to use this on iOS? AFAIK Google Search and Maps only show rather basic information.
I searched a bit and seems like the answer is no besides this web app https://deepmind.google.com/science/weatherlab
> 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.
The online viewer could really, really use Wind Direction as a compass bearing. Its super important considering wildfire/bushfire, air quality, ocean-going conditions, and a myriad of other things.
In the energy world, this should be such a boon over the classic NWP (Numerical Weather Prediction; complex ML models), but I've not seen it implementated. Anyone with experience of these models over classic NWP?
Why should they be such a boon? High-resolution, regional NWP with fast refresh has been a standard tool for over a decade.
A few weather forecast sites in Europe have them on their websites. It's just another forecast you can select and compare in an ensemble.
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.
> 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.
This would not likely be a great idea since you reduce your ability to understand inputs except for a few parameters. Explainable inputs become very important for many down the line processes used by government and industry alike, because said inputs and their predictive certainty can be quite informative, even critical, for accurate mesoscale prediction.
If model members were available, with all the usual measures, thats a fantastic place to start looking at serious inclusion. It doesnt seem thats the case, however.
Paper: https://storage.googleapis.com/deepmind-media/papers/weather...
TLDR: the input data for the model now also includes real-time observations (satellite and weather stations) on top of the typical (re)analysis data, improving model resolution, run frequency and timestep frequency.
We'll put that link in the toptext as well. Thanks!