Although it's great to see these advancements, I would like to see it integrated with the Google Weather results that show up in search and on Android devices before I get excited. Spinning up the model on my own hardware and feeding it data manually is a decent amount of work, and I'm too lazy to do that.
I'm not surprised. This is the sort of problem machine learning is really good at solving. There's a lot of quality training data, and the results are governed by physics.
You can see the weather forecast maps of Deep Mind here...
https://charts.ecmwf.int/products/graphcast_medium-mslp-wind...
I was watching it during the recent hurricane season, and it did not seem to perform much differently than other models.
Too bad climate change is happening now, so the model will have to extrapolate.
GenCast was trained on data from 1959-2023, so no surprise it can "predict" back 2019.
It's like the super trading algorithms who achieve perfect scores during backtest.
The question is, how does it perform on unknown events.
I wonder if GenCast's 15 day forecast is really the right indicator for forecasting? I could imagine that such long term ML forecasts tend to get closer to yearly averages, which are kind of "washed out", but of course look good for benchmarking and marketing reasons. But they are not so practical for the majority of weather forecast users. In short, it still smells a bit like AI snake oil to me. [1]
[1] more about this: https://press.princeton.edu/books/hardcover/9780691249131/ai...