GraphCast: AI model for weather forecasting

Post by westurner » Tue, Nov 14, 2023, 6:56 PM UTC

wenc wrote: post #25

serjester wrote: post #16

To call this impressive is an understatement. Using a single GPU, outperforms models that run on the world's largest super computers. Completely open sourced - not just model weights. And fairly simple training / input data. > ... with the current version being the largest we can practically fit under current engineering constraints, but which have potential to scale much further in the future with greater compute re…

It builds on top of supercomputer model output and does better at the specific task of medium term forecasts. It is a kind of iterative refinement on the data that supercomputers produce — it doesn’t supplant supercomputers. In fact the paper calls out that it has a hard dependency on the output produced by supercomputers.

Post by borg16 » Tue, Nov 14, 2023, 7:04 PM UTC

carbocation wrote: post #37

wenc wrote: post #25

Earlier quoted context omitted.

It builds on top of supercomputer model output and does better at the specific task of medium term forecasts. It is a kind of iterative refinement on the data that supercomputers produce — it doesn’t supplant supercomputers. In fact the paper calls out that it has a hard dependency on the output produced by supercomputers.

I don't understand why this is downvoted. This is a classic thing to do with deep learning: take something that has a solution that is expensive to compute, and then train a deep learning model from that. And along the way, your model might yield improvements, too, and you can layer in additional features, interpolate at finer-grained resolution, etc. If nothing else, the forward pass in a deep learning model is almo…

Post by mdbmdb » Tue, Nov 14, 2023, 7:09 PM UTC

meteo-jeff wrote: post #42

mdbmdb wrote: post #34

Earlier quoted context omitted.

Is it able to provide data on extreme events. Say, the current and potential path of a hurricane? similar to .kml that NOAA provides

Extreme weather is predicted by numerical weather models. Correctly representing hurricanes has driven development on the NOAA GFS model for centuries. Open-Meteo focuses on providing access to weather data for single locations or small areas. If you look at data for coastal areas, forecast and past weather data will show severe winds. Storm tracks or maps are not available, but might be implemented in the future.

Post by kridsdale3 » Tue, Nov 14, 2023, 7:16 PM UTC

xnx wrote: post #7

mukara wrote: post #6

Earlier quoted context omitted.

DeepMind recently merged with the Brain team from Google Research to form `Google DeepMind`. It seems this was done to have Google DeepMind focused primarily (only?) on AI research, leaving Google Research to work on other things in more than 20 research areas. Still, some AI research involves both orgs, including MetNet in weather forecasting. In any case, GraphCast is a 10-day global model, whereas MetNet is a 24-h…

Good explanation. Now that both the 24-hour regional and 10-day global models have been announced in technical/research detail, I supposed there might still be a general blog post about how improved forecasting is when you search for "weather" or check the forecast on Android.

Post by xapata » Tue, Nov 14, 2023, 7:18 PM UTC

pkulak wrote: post #52

wenc wrote: post #25

Earlier quoted context omitted.

It builds on top of supercomputer model output and does better at the specific task of medium term forecasts. It is a kind of iterative refinement on the data that supercomputers produce — it doesn’t supplant supercomputers. In fact the paper calls out that it has a hard dependency on the output produced by supercomputers.

Why can't they just train on historical data?