How cool! The paper was last revised on Arxiv over the summer; this blog post announces that MetNet3 is now powering weather predictions across google devices a

How cool! The paper was last revised on Arxiv over the summer; this blog post announces that MetNet3 is now powering weather predictions across google devices and services. (I'll bet it gets picked up and used for electricity demand forecasting if it hasn't already.) From the paper: > While ground based radars provide dense precipitation measurements, observations that MetNet-3 uses for the other variables come from just 942 points that correspond to weather stations spread out across Continental United Stated (CONUS). I don't know a thing about weather prediction, but the fact MetNet-3 can do it using data from less than 1000 points across the continental US is surprising. The other line that stood out to me was: > On a high level, MetNet-3 neural network consists of three parts: topographical embeddings, U-Net backbone and a MaxVit transformer for capturing long-range interactions. If I understand it correctly, MetNet-3 is sort of abstractly treating 'predicting the weather at each geographical patch' like a very big computer vision problem.