Weather modeling - X kilometers by Y layers of atmosphere can get expensive to compute really quick. And NOAA does more than just simulate weather, they're running climate/sea level rise/arctic ice modelling, aggregating sensor data from buoys/balloons/satellites, processing maps, and more.

I can't speak for NOAA, but my experience with supercomputing has been that there is no abstraction of computation, your workload is very much tied to hardware assumptions.

They are used to do large-scale high-resolution analysis or simulation of complex systems in the physical world. The codes typically run on the bare metal with careful control of resource affinity, often C++ these days.

They aren't just used for global-scale geophysical processes like weather and climate or complex physics simulations. For example, oil companies rent time to analytically reconstruct the 3-dimensional structure of what's underneath the surface of the Earth from seismic recordings.

no, its a gang scheduled process - at least that's been the standard model. those processes are run either close to bare metal or as a process on linux. containers would be useful to package up the shared dependencies, so that may have changed.