You are basically describing ensemble forecasting, which is very powerful for providing statistically meaning forecasts where you can intelligently talk about the uncertainty of the forecast, something single deterministic forecasts cannot do.
In my research, I'm doing single deterministic forecasts to study what happens with tornadoes in supercell thunderstorms, where I am cranking up the resolution to capture flow that is otherwise unresolved. I get one version of a particular storm, which is good for studying certain aspects of storms, but not good at being able to generalize (that takes lots of simulations).
Both big deterministic simulations and ensembles have their place. Of course, today's big simulation can be the resolution of tomorrow's ensembles! Right now, you can do lots of good science with ensembles. Operationally (weather forecasting) this is basically the new paradigm, although forecasters are slow to change from just looking at the single deterministic GFS and NAM forecasts. The ensemble approach, once we start running hundreds of forecasts at higher resolution that we do today, will transform our forecasting accuracy (and precision). However it will be limited to the amount of good observational data we can feed the models (otherwise GIGO). This is where remote sensing comes in. GOES-R will be a big help.
It will indeed take people from atmospheric science, computer engineering, software engineering, etc. working together to best exploit exascale machines. NCSA understand this and that's what makes it (and other similar organizations) great.