[HN Gopher] Aurora, a foundation model for the Earth system
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       Aurora, a foundation model for the Earth system
        
       Author : rmason
       Score  : 63 points
       Date   : 2025-06-05 19:17 UTC (3 hours ago)
        
 (HTM) web link (www.nytimes.com)
 (TXT) w3m dump (www.nytimes.com)
        
       | magicalhippo wrote:
       | The release blog post is here[1], model code released under MIT
       | license here[2], along with weights on Huggingface, and some
       | documentation here[3].
       | 
       | As a layman I've been following deep neural nets being used to
       | solve quantum physics problems, where they do quite well for
       | certain classes of hard problems, so perhaps not terribly
       | surprising they do well with weather prediction as well I
       | suppose.
       | 
       | [1]: https://news.microsoft.com/source/features/ai/microsofts-
       | aur...
       | 
       | [2]: https://github.com/microsoft/aurora
       | 
       | [3]: https://microsoft.github.io/aurora/intro.html
        
         | tomhow wrote:
         | Also, research paper in Nature:
         | 
         | https://www.nature.com/articles/s41586-025-09005-y
        
       | scottcha wrote:
       | The are many great things about Aurora, here are a few as I've
       | been using it since it came out. 1. Its open source & open
       | weights and free to use non-commercially. 2. Its configurable to
       | easily fit on my local gpu for development purposes. 3. I've also
       | gotten great engagement from the repo owners.
        
       | neonate wrote:
       | https://archive.md/vl87I
        
       | roger_ wrote:
       | Been following wesselb on GitHub for a while, great to see his
       | work getting more attention!
        
       | Lyngbakr wrote:
       | AI weather is making great progress with the likes of GraphCast,
       | Aardvark, NeuralGCM, Aurora, etc. It seems like the teams that
       | produce these models often include folks from Microsoft and
       | Google, which makes me wonder if there's much cross pollination
       | within those companies which is helping these advances or if
       | researchers are siloed and the development of these models is
       | entirely independent of one another?
        
       | goochphd wrote:
       | Very cool project. There are some presentations by the PI on
       | youtube that I recommend searching for. One of the interesting
       | takeaways I had was that they were able to do better with
       | mesoscale phenomena and extreme weather prediction than the other
       | players (like Graphcast and Pangu and FourCastNet), in part due
       | to their technique for training a higher resolution data space
       | (0.1 deg vs 0.25 or 0.5). I also found it interesting that they
       | were able to show a scaling relationship where performance
       | increased by 5% every time they doubled the model size - and
       | their loss was still improving when they had to cut it off due to
       | cost constraints.
       | 
       | Very cool stuff!
        
       | xnx wrote:
       | Is this similar to Google's model?
       | https://research.google/blog/zooming-in-efficient-regional-e...
        
       | sieste wrote:
       | Impressive. However, I don't like how AI foundation models are
       | always advertised as alternatives to "traditional" (physics
       | based) forecasting. Virtually all AI weather models are trained
       | on ERA5 reanalysis, which is a blend of observations and
       | numerical model forecasts. Without a good global numerical model
       | of the atmosphere there would be no AI model. I wish this synergy
       | were emphasised more, rather than always going straight for the
       | easy "AI beats physics!!1!" headline.
        
         | xpe wrote:
         | I agree... the public and leaders need to know how the training
         | data is generated: a combination of sensors and physics-based
         | simulation models. Lacking this context could lead to poor
         | decisions around research prioritization and funding.
        
         | tndl wrote:
         | Things are changing quickly in this area. Several
         | projects/companies working on AI data assimilation (an
         | alternative route to creating analysis data like ERA5)[0].
         | 
         | Also a lot of companies working on the data collection side,
         | replacing/augmenting government data collection. Spire's an
         | example of this in the space domain, and Windborne and Sorcerer
         | (my company) do weather balloons.
         | 
         | [0]: E.g. Brightband's AIDA
         | (https://www.brightband.com/blog/aida/) and Project Aardvark
         | (https://www.turing.ac.uk/blog/project-aardvark-
         | reimagining-a...)
        
       | waltbosz wrote:
       | Does this title make anyone else's Asimov senses tingle? In the
       | book "Foundation and Earth", the protagonists travel from planet
       | Aurora to planet Earth.
        
       | dmillard wrote:
       | Two of the authors of the original Aurora system left Microsoft
       | to found https://silurian.ai/ - interesting to keep tabs on if
       | you're interested in this space!
        
       | dunkeltaenzer wrote:
       | We do we advertise for paywalled content here?
        
       | croemer wrote:
       | Nature paper: https://www.nature.com/articles/s41586-025-09005-y
        
       | nxobject wrote:
       | As superficial an application as this sounds, I wonder if this be
       | used to make a fun sandbox simulation game...
        
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