It's great that they're working on this, but I am puzzled at how absolutely awful the forecasts are in the Google weather app for my area. The forecast will show no rain, the radar view shows nothing, meanwhile it's pouring outside and every other app I check shows it. I know I can't expect it to be perfect, but being terribly wrong even one in ten times is enough to tarnish its reputation permanently.
It's because the current administration cut NOAAs funding, which means fewer weather balloons, which means less data to make a prediction. And it means less real time updates.
So yeah, when it rains, it might take a few hours for that to flow into your weather app.
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.
This isn't a super serious comment (and an opportunity for someone to speak up on this) but...
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'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
Weather 2 doesn't seem to have been an ensemble model. Weather 3 is, so theoretically it can get more accurate outcomes by taking the probabilistic analysis of several models concurrently to determine the most likely weather conditions.
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.
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.
The Google daily and hourly forecasts aren't great, but the weather map forecast is excellent. Where does that come from? What's generating the predicted movement of the storm clouds?
does google actually use their own models in consumer products? i'm not an android user, but the weather widget in the google search results for my area always seems to be attributed to weather.com.
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.
At one point they did release the most accurate weather model by far, but it actually decreased usage compared to showing a weather forecast more skewed towards "happy" temperatures and weather. I'm certain this is still the case and likely explains why you are seeing a more positive (no rain) version of the weather.
Same here, if its raining Windy.com radar will always show it, but often Google Weather does not show it, despite saying its updated "now". Sometimes it seems to be a tiling issue, where there will be a blob of rain on the map but cut off at some boundary over the forecast area.
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.
I had a problem, and that problem is resolved. Thank you for all of your kindness and assistance on this matter. My gratitude for your effort extends beyond all imaginable boundaries.
No worries, glad to help pitch in for all the times someone has helped me find something obviously staring me in the face :). I swear "I can't find my keys"->"they're in your pocket" type things are a mandatory human experience (if given enough time).
Dark Sky is still one of my all time favorite app. The accuracy of the forecast within the coming hour is to this day unmatched. It constantly reminded me of Back to the Future 2, where Doc says the rain will stop in “in exactly 5 seconds”
I never used that, but I like Rain Parrot in Australia. Not sure if it's similar. It will tell me that it's likely to rain in x minutes, then stop y minutes later, and it's almost always fairly accurate.
The only forecasted rainfall for local areas within a very short timeframe. This made it possible to utilize simple factors to predict the near future.
It is built into Apple Weather after Apple purchased them.
> 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).
Apple Weather (followed by Xweather (formerly Aeris Weather)) seems the most accurate forecast for Prague when it comes to TP/FP for rain in Prague by my long term experience when I compared multiple data sources
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.
Dark sky was awful and all of its acclaim comes from confirmation bias. In seattle I'd see its forecast change by the minute. The whole point of a forecast is to know what will happen and if that keeps changing, then we do not know.
There is a great article from the German Meteorological Service on WeatherNext 3 and how AI-based weather models will probably co-exist with physics-based ones.
- 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
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.
This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value.
I imagine a lot of predictive sciences are ultimately about mixing together a bunch of inputs to attempt to decipher some output. Do we end up being able to take stuff from here and figure out some new ideas about modelling the climate as a whole?
> 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.
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?
Its not there because a lot of the providers werent providing until recently, and higher spatial resolutions, referesh times and higher compute costs meant that no one used them in prod. We tried with ECMWF-AIFS however ENS + IFS was bettter better.
> 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.
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. It is produced as a set of vectors during model creation, so would be very useful to see.
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.
It's great that they're working on this, but I am puzzled at how absolutely awful the forecasts are in the Google weather app for my area. The forecast will show no rain, the radar view shows nothing, meanwhile it's pouring outside and every other app I check shows it. I know I can't expect it to be perfect, but being terribly wrong even one in ten times is enough to tarnish its reputation permanently.
It's because the current administration cut NOAAs funding, which means fewer weather balloons, which means less data to make a prediction. And it means less real time updates.
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.
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.
That's because NOAA are not doing the tests that would produce such evidence.
There was a lot of evidence last winter. But with the active El Niño it’s starting earlier this year.
Please share some of that evidence.
>and every other app I check shows it.
Other apps have a different government?
From my experience, it's quite hit and miss for every app.
If you use Google 99% of the time and only check other apps when Google is wrong, then that's a biased experiments.
That person is probably misremembering or looking later after the update.
I pull weather data from multiple apps all the time because I’m a weather nerd and they all agree equally poorly.
This isn't a super serious comment (and an opportunity for someone to speak up on this) but...
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'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
Weather 2 doesn't seem to have been an ensemble model. Weather 3 is, so theoretically it can get more accurate outcomes by taking the probabilistic analysis of several models concurrently to determine the most likely weather conditions.
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.
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
Awesome comment, thanks for the info!
The Google daily and hourly forecasts aren't great, but the weather map forecast is excellent. Where does that come from? What's generating the predicted movement of the storm clouds?
Nexrad? NOAA?
Same experience.
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." ;)
does google actually use their own models in consumer products? i'm not an android user, but the weather widget in the google search results for my area always seems to be attributed to weather.com.
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
At one point they did release the most accurate weather model by far, but it actually decreased usage compared to showing a weather forecast more skewed towards "happy" temperatures and weather. I'm certain this is still the case and likely explains why you are seeing a more positive (no rain) version of the weather.
I see the same sentiment any time any weather app is posted. Especially when Dark Sky comes up
Same experience. I moved to Windy and I just look at the weather radar and make my own prediction.
I guesstimate that it has less than 50% accuracy for my area
Same here, if its raining Windy.com radar will always show it, but often Google Weather does not show it, despite saying its updated "now". Sometimes it seems to be a tiling issue, where there will be a blob of rain on the map but cut off at some boundary over the forecast area.
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
Don't plenty of android devices have barometers? I expect Google being Google and recording all that data unless you opt out.
Would that be practical for weather forecasting or not really?
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.
You're saying these buttons took you to different pages than what was described?
https://i.imgur.com/IVv4y0n.png
Keep in mind both button are "the demo". One is for trying out the API yourself, the other is for seeing a pre-made dashboard.
I had a problem, and that problem is resolved. Thank you for all of your kindness and assistance on this matter. My gratitude for your effort extends beyond all imaginable boundaries.
No worries, glad to help pitch in for all the times someone has helped me find something obviously staring me in the face :). I swear "I can't find my keys"->"they're in your pocket" type things are a mandatory human experience (if given enough time).
It’s crazy we’ll never have forecasts as good as dark sky again. What kind of magic were they doing?
They're back at it again
https://acmeweather.com/
https://www.theverge.com/tech/883089/acme-weather-forecast-a...
Oh, they finally launched it in Europe. Let’s see whether it’s as good as people say. :)
Dark Sky is still one of my all time favorite app. The accuracy of the forecast within the coming hour is to this day unmatched. It constantly reminded me of Back to the Future 2, where Doc says the rain will stop in “in exactly 5 seconds”
I never used that, but I like Rain Parrot in Australia. Not sure if it's similar. It will tell me that it's likely to rain in x minutes, then stop y minutes later, and it's almost always fairly accurate.
The only forecasted rainfall for local areas within a very short timeframe. This made it possible to utilize simple factors to predict the near future.
It is built into Apple Weather after Apple purchased them.
> 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).
I use it in Oregon and it's remarkably accurate at predicting rain, both timing and severity.
Apple Weather (followed by Xweather (formerly Aeris Weather)) seems the most accurate forecast for Prague when it comes to TP/FP for rain in Prague by my long term experience when I compared multiple data sources
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.
Dark sky was awful and all of its acclaim comes from confirmation bias. In seattle I'd see its forecast change by the minute. The whole point of a forecast is to know what will happen and if that keeps changing, then we do not know.
There is a great article from the German Meteorological Service on WeatherNext 3 and how AI-based weather models will probably co-exist with physics-based ones.
https://www.dwd.de/DE/wetter/thema_des_tages/2026/9/6.html (German only)
The exploration page is really easy to use. Having more reliable weather predictions is a genuinely useful product.
I disagree.. Feels very sloppy to me:
- 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
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.
Reading through the paper and...
This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value.
I imagine a lot of predictive sciences are ultimately about mixing together a bunch of inputs to attempt to decipher some output. Do we end up being able to take stuff from here and figure out some new ideas about modelling the climate as a whole?
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.
Mobile app would be awesome.
Last time they have only shipped the model on web in mobile...
Nice, coming from a company mainly impacted by the country currently considered the least trustworthy on climate change
"Weather Lab is not available in your country or region at this time." cool...
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?
Its not there because a lot of the providers werent providing until recently, and higher spatial resolutions, referesh times and higher compute costs meant that no one used them in prod. We tried with ECMWF-AIFS however ENS + IFS was bettter better.
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.
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. It is produced as a set of vectors during model creation, so would be very useful to see.
I agree but this seems like a demo for the underlying data set. The data is the product and they want to sell or use it downstream.
Sweet. This will be a great layer for our hazard prediction engine.
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!