Comment by shoyer
Glad to see that you can make ensemble forecasts of tropical cyclones! This absolutely essential for useful weather forecasts of uncertain events, and I am a little dissapointed by the frequent comparisons (not just you) of ML models to ECMWF's deterministic HRES model. HRES is more of a single realization of plausible weather, rather than an best estimate of "average" weather, so this is a bit of apples vs oranges.
One nit on your framing: NeuralGCM (https://www.nature.com/articles/s41586-024-07744-y), built by my team at Google, is currently at the top of the WeatherBench leaderboard and actually builds in lots of physics :).
We would love to metrics from your model in WeatherBench for comparison. When/if you have that, please do reach out.
Agree looking at ensembles is super essential in this context and this is what the end of our blogpost is meant to highlight. At the same time, a good control run is also a prerequisite for good ensembles.
Re NeuralGCM, indeed, our post should have said "*most* of these models". Definitely proves that combining ML and physics models can work really well. Thanks for your comments!