I chime in on this because I'm working on deep learning for precipitation nowcasting using radar for my Ph.D.
I was very excited when google released the press statement at NeurIPS about their work in this area. Unfortunately, after reading the paper, I have to say that their approach is fairly basic.
Basically they threshold the precipitation in 4 thresholds (no rain, light, medium, heavy) and then use a U-Net like architecture, treating it as a classification problem.
I think that the works of Shi et al are much more interesting in this regard:
What I think is that Google wanted to use a lighter model that can be applied to the whole continental US.
I expect them to integrate this in google assistant, like: "hey google, tell me when it's going to rain"
https://papers.nips.cc/paper/5955-convolutional-lstm-network...
http://papers.nips.cc/paper/7145-deep-learning-for-precipita...
What I think is that Google wanted to use a lighter model that can be applied to the whole continental US. I expect them to integrate this in google assistant, like: "hey google, tell me when it's going to rain"