Absolutely - but large ensembles are just the tip of the iceberg. Why bother producing an ensemble when you could just output the posterior distribution of many forecast predictands on a dense grid? One could generate the entire ensemble-derived probabilities from a single forward model run.
Another very cool application could incorporate generative modeling. Inject a bit of uncertainty in a some observations and study how the manifold of forecast outputs changes... ultimately, you could tackle things like studying the sensitivity of forecast uncertainty for, say, a tropical cyclone or nor'easter relative to targeted observations. Imagine a tool where you could optimize where a Global Hawk should drop rawindsondes over the Pacific Ocean to maximally decrease forecast uncertainty for a big winter storm impacting New England...
We may not be able to engineer the weather anytime soon, but in the next few years we may have a new type of crystal ball for anticipating its nuances with far more fidelity than ever before.
Another very cool application could incorporate generative modeling. Inject a bit of uncertainty in a some observations and study how the manifold of forecast outputs changes... ultimately, you could tackle things like studying the sensitivity of forecast uncertainty for, say, a tropical cyclone or nor'easter relative to targeted observations. Imagine a tool where you could optimize where a Global Hawk should drop rawindsondes over the Pacific Ocean to maximally decrease forecast uncertainty for a big winter storm impacting New England...
We may not be able to engineer the weather anytime soon, but in the next few years we may have a new type of crystal ball for anticipating its nuances with far more fidelity than ever before.