ERF vs WRF
The following comparison is based on the WRF Version 4 Technical Report, titled “A Description of the Advancd Research WRF Model Version 4”
Similarities
Equations: both ERF and WRF solve the fully-compressible, Eulerian nonhydrostatic equations, and conserve dry air mass and scalar mass. ERF does not have a hydrostatic option.
Prognostic Variables: velocity components (u,v,w); perturbation moist potential temperature. Optionally, turbulent kinetic energy and any number of scalars such as water vapor mixing ratio, rain/snow mixing ratio, cloud water / ice mixing ratio.
Horizontal grid: both ERF and WRF use Arakawa C-grid staggering.
Time Integration: Time-split integration using 3rd-order Runge-Kutta scheme with smaller time step for acoustic and gravity wave modes. Variable time step capability.
Spatial Discretization: 2nd- to 6th-order advection options in horizontal and vertical
Turbulent Mixing: Sub-grid scale turbulence formulation. Vertically implicit acoustic step off-centering.
Initial conditions: both ERF and WRF have the ability to initialize problems from 3D “real” data (output of real.exe), “ideal” data (output of ideal.exe) and from 1D input soundings.
Lateral boundary conditions: Periodic, open, symmetric and specified (in wrfbdy* files).
Bottom boundary conditions: Frictional or free-slip
Earth’s Rotation: Coriolis terms in ERF controlled by run-time input flag
Mapping to Sphere: ERF supports the use of map scale factors for isotropic projections (read in from wrfinput files).
Nesting: One-way or two-way. Multiple levels and integer ratios.
Key Differences
Vertical coordinates: Unlike WRF, ERF uses a terrain-following height-based vertical coordinate, with vertical grid stretching permitted.
Time Integration: ERF supports using a 3rd-order Runge-Kutta scheme with no substepping as alternative to RK3 with acoustic substepping.
Initial conditions: ERF has an additional mode of “custom” initialization in which the user writes the initialization routine.
ERF does not have the capability for global simulation