Jacob Prince-Bieker

Great! Thanks!

The encoder and decoder are the same as GraphCast, but the latent grid is a 5-refined mesh, not a multi-mesh, with 10242 nodes and 61440 edges. 

I think this adds more support to modularizing graph_weather, what is being done in #76, so that it is easier to experiment with/replicate this kind of result

They train on the 12 hour timestep to be in different data assimilation windows, as ERA5 only has 2 a day.

Overall, really impressive results I think. Interesting combination of graph and diffusion model. A lot slower to run and train because of how diffusion models work, and still requires NWP...

Would be really keen to implement this here.

Yes, this could work as a GSoC project. It would be a large project (350h). GraphCast wouldn't need to be ported over, we already have the encoder/decoder graph networks implemented,...

Hi, it should be fairly easy to get this working with those datasets. All that needs to be done would be to concatentate the GOES and MRMS image together, and...

Yeah! Just change the input channels and output channels and it should just work! And of course, use it however you want! We'd love to see how other people use...

To combat 'blurry' predictions, we could also try things like the Mean Gradient Error (https://arxiv.org/pdf/1911.09428.pdf) (unfortunately the github repo implementation doesn't exist anymore) that tries to make sure the model...