Comment by brunosan

Comment by brunosan 4 days ago

Can we help you? We build the equivalent for land, as a non-profit. It's basically a geo Transformer MAE model (plus DINO, plus matrioska, plus ...), but largest and most trained (35 trillion pixels roughly). Most importantly fully open source and open license. I'd love to help you replace land masks with land embeddings, they should significantly help downscale the local effects (e.g. forest versus city) that afaik most weather forecast simplify with static land cover classes at most. https://github.com/Clay-foundation/model

nikhil-shankar 4 days ago

Hi, this looks really cool! Can we meet? Shoot us an email at contact@silurian.ai

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jonplackett 4 days ago

Maybe between the two of you, you can tell me why my Alexa is telling me there’s no rain today, but it’s raining right now.

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- brunosan 4 days ago
  
  You'll need to subscribe to Alexa weather plus, for only 9.99$/month. Now seriously, yes, hyperlocal short-term weather forecast should be a commodity, even public utility?
  
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  iammattmurphy 4 days ago
  
  That makes me appreciate that in Vancouver we have Weatherhood, which is free to use.
  
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  tgtweak 4 days ago
  
  I like accuweather's minutecast which is a higher resolution short-term forecast (+60 min) that is not just pulling the forecast for the nearest weather station to you.
  Windy(.com) premium also has a great hybrid weather radar+forecast view which was recently released and which I find has been very effective at predicting rain at a specific location on the map vs "nearby". With smaller weather patterns it is entirely possible for it to rain a few blocks away but not at your location. An 11-KM resolution weather forecast (as referenced above) will not be able to capture this nuance.
  
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- gabinator 3 days ago
  
  In case you're curious -- computer scientists have been trying to simulate/predict weather over half a century and it's led to some really awesome math/compsci discoveries.
  If you've ever heard of the Lorenz/Butterfly Effect/Strange Attractors, those chaotic systems were discovered because of a discrepancy between two parallel weather simulations. One preserved the original simulation's calculation train while the other started off with simply the previous results (out to like 10 decimals) and suffered from a rounding error and thus both simulations diverged hugely.
  Lorenz was trying to simulate weather by subdividing the atmosphere into tons and tons of cubes. Really interesting reading/video watching tbh.
  
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