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DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

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Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#101

Post by fragmede »

segmondy wrote: post #79
tcumulus wrote: post #32

Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The o…

everything in AI is not focused on LLM, if you think so then that's because you are in LLM bubble. The big idea with LLM is that it's generative AI, the generative could be anything! Not just large languages, we have seen break through in image generation, video, audio, but guess what. Anything that you have enough data and given data you can predict what comes next can have gen AI applied, so we are seeing it with p…

Please don't generate cyclones!

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#102

Post by ghm2199 »

> One key limitation of our approach is in how uncertainty is handled. We focused on deterministic forecasts and compared against HRES, but the other pillar of ECMWF’s IFS, the ensemble forecasting system, ENS, is especially important for 10+ day forecasts. The non-linearity of weather dynamics means there is increasing uncertainty at longer lead times, which is not well-captured by a single deterministic forecast. ENS addresses this by generating multiple, stochastic forecasts, which model the empirical distribution of future weather, however generating multiple forecasts is expensive. By contrast, GraphCast’s MSE training objective encourages it to express its uncertainty by spatially blurring its predictions, which may limit its value for some applications.

As someone who has learned bayesian statistics in social sciences, isn't this a big deal? There is a reason why risk estimates need to be well understood and *explainable* for certain fields like this. Are you willing to bet a government response should issue an evacuation order 30 miles from the center of a hurricane at location X if the model can't tell you why it produced an uncertainty estimate there — or worst the model changed its mind later?

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#103

Post by micro2588 »

sunshinesnacks wrote: post #73
tcumulus wrote: post #61

Earlier quoted context omitted.

Indeed, you can imagine this as some sort of advanced physics-based interpolation of various measurements (land stations, satellite data, ...) to fill in every cell in a latitude-longitude grid. This is not only used for ERA5 (training data for the models), but also to determine the initial conditions for every grid cell which are used to roll out the forecast. So AI weather models depend greatly on the NWP/physics u…

Yeah. I can’t remember names off the top of my head, but there are a few companies, and I think many researchers, working on AI “data assimilation” for this.

ECMWF has an experimental AIFS direct observational prediction model (AIFS-DOP) that has become competitive with their physics based IFS model on certain metrics just in the past year.

https://arxiv.org/html/2606.19093v1

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#104

Post by sunshinesnacks »

micro2588 wrote: post #103
sunshinesnacks wrote: post #73

Earlier quoted context omitted.

Yeah. I can’t remember names off the top of my head, but there are a few companies, and I think many researchers, working on AI “data assimilation” for this.

ECMWF has an experimental AIFS direct observational prediction model (AIFS-DOP) that has become competitive with their physics based IFS model on certain metrics just in the past year. https://arxiv.org/html/2606.19093v1

Ah, yes, that’s one of them! Not to be confused with AIFS and AIFS Ensemble that are competitive with IFS, but start with the same DA as IFS.

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#105

Post by timeisapear »

rumblefrog wrote: post #93
timeisapear wrote: post #92

Earlier quoted context omitted.

Yes. AIFS directly by ECMWF and AIGEFS by NOAA. Every vibecoded weather app these days has them. Google those terms you’ll find them.

Anything more daily human friendly/consumable?

https://sites.gsl.noaa.gov/desi/

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#106

Post by micro2588 »

SirHumphrey wrote: post #99
testfoobar wrote: post #86

Earlier quoted context omitted.

Why does it work for weather at all? Is there something that the mathematical models are over-simulating? Is weather easier to predict than we thought? Just curious what the intuition is to regarding the success of ML weather modelling...

There is just A LOT of data available- usually an order of magnitude more than in any other related problem. And general weather forecasts are not that hard - we have semi useful forecasts for more than 50 years. It’s when you want to do something special: long range, nowcasting of convective storm, other extreme weather etc. that is hard. And even then it’s as much a problem of input data accuracy than the models th…

Traditional physics based weather models also rely heavily on physical parameterization for sub grid scale processes (think clouds, microphysics of rain sleet snow, etc) so even the deterministic physics models are learned approximations from data.

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#107

Post by rationalfaith »

tcumulus wrote: post #32

Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The o…

[dead]

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#110

Post by SoftTalker »

IanCal wrote: post #29
xyzzy123 wrote: post #27

Earlier quoted context omitted.

I know this is uncharitable and I am wrong but I am having trouble coming up with concrete scenarios where you die with 2 days notice but survive with 3. I am nonethless a believer that more accurate forecasting has value.

Could you imagine a scenario where from warning to complete evacuation takes more than two days? Evacuating a whole area is a hard task, particularly once you start looking at more complex problems (elderly, prisons, hospitals).

We can't save everyone, so let's not bother trying to do better.
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