I love modeling... I run my own WRF-ARW here at home over my small domain of Minnesota when I'm considering chasing.... a few notes that I'll add to Jeff's excellent posts..
- The term WRF isn't nearly as important as its core... ARW or NMM. "NAM" (as Jeff explained) is using WRF-NMM... however its important to note that the ARW core is MUCH more accurate, but it is also MUCH slower to run. This is why operationally they use the NMM core because its easier to make the domain large and get it done quickly. However, you're better off to look at an ARW variant if you want to increase your odds at a correct forecast.
- The WRF EMS can be packaged with a lot of different variables, and almost nobody identifies the combinations of microphysics, longwave radiation, shortwave radiation, cumulus scheme (Kain Fritsch, none or other), land surface model, turbulence parameterization, type of advection of moisture, how many vertical levels, the time step, domain size and even the resolution that the model is set up to use. This is what makes the model... and sometimes even the core is not identified. Without knowing this information, people might try to compare two WRF runs and have two completely different results and not know why... well the simple answer is they are two different models with the same name!
- The lower the resolution, the better the model can 'naturally' handle convection, so you can actually 'turn off' convective parameterization. If the model is down under 10km (some would argue much less than that), your model will handle thunderstorms better without convective parameterization because CP is essentially stunting the growth of convection and sizing it to fit within the large grid spacing of say a 30km model for example. A storm is not that large, so you need to try to mathematically grow the storm on a more natural scale. If your grid spacing is closer to that of a real storm (say 2-5km) the storm will grow naturally within the grid spacing so you can shut off CP. With that said, if you have to use CP; Kain Fritsch I believe is pretty well accepted as one of the best to use.
- Last note... in my experimenting I'm finding that if you make your grid spacing too small and the domain has to suffer because of it, the model will be crappy. So high resolution isn't always everything. Look at GFS. GFS is a good model, despite having huge grid spacing (0.5 degree, or about 35-40km). It does so well (besides its physics) because of its large domain (whole world). So if you find a 1km WRF out there, but its running a domain the size of a state or smaller, don't think it's going to do well.
There's also nesting of the domain but this is probably way more than what you want to talk about.
