Google MetNet - A Reliable Precip Model?

Randy Jennings

May 18, 2013
In s blog post and accompanying paper, researchers at Google detail an AI system — MetNet — that can predict precipitation up to eight hours into the future. They say that it outperforms the current state-of-the-art physics model in use by the U.S. National Oceanic and Atmospheric Administration (NOAA) and that it makes a prediction over the entire U.S. in seconds as opposed to an hour.

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Reactions: Mark Blue


Mar 1, 2004
Lansing, MI
They are taking existing precip and moving it. That may be effective - but it doesn't account for precip that forms after FH0. So in essence - might be good for future project, but useless as-is.

Mark Blue

Staff member
Feb 19, 2007
Anytime corporate America gets involved with a large-scale weather project I wouldn’t rest on the laurels of NOAA and NWS. With the likes of Google and their deep pockets I’d be looking over my shoulder for the day when privatization comes knocking at the front door. They have a massive amount of computational resources and can also pay salaries well beyond the standard GS11-13 meteorologists make in civil service after earning a PhD. I think it bears watching simply because we cannot seem to equal or best the Euro models. We’re closing the gap but still have work to do. Please don’t take offense @Jeff Duda as I know this is personal to you. After all it’s just my humble opinion.

Jeff Duda

EF6+, PhD
Staff member
Oct 7, 2008
Broomfield, CO
Yeah, we're aware of it at ESRL/GSD (i.e., the HRRR group). Definitely some competition there. I haven't fully scrutinized the work yet (they didn't give adequate details to perform a rigorous scientific comparison anyway).

There is no doubt that machine learning/AI has a place in weather forecasting/NWP, and it certainly appears that, in this restricted framework, Google's software may indeed outperform the HRRR at very short ranges. One big thing that separates ML/AI forecasting from full-blown dynamical forecasting is that ML/AI is still restricted to past data sets and mostly serves as post-processing to existing forecast data, whereas the dynamical method actually uses the laws of physics directly to perform a weather forecast. Thus, ML/AI has an upper bound to how good it can forecast, especially temporally.