The WSF: The Advanced Research WRF (ARW) solver

Weather Research and Forecasting (WRF) model is a numerical weather prediction
(NWP) model designed to carry out research operations on the atmospheric
system. The spectrum of options available in it for performing operations
pertaining to physics and dynamics is very broad. It is suitable for a broad
span of applications across scales ranging from large-eddy to global
simulations. Such applications include real-time NWP, data assimilation
development and studies, parameterized-physics research, regional climate simulations,
air quality modeling, atmosphere-ocean coupling, and idealized simulations.


principal components of the WRF system are depicted in Figure 1. The WRF
Software Framework (WSF) provides the infrastructure that accommodates the
dynamics solvers, physics packages that interface with the solvers, programs
for initialization, WRF-Var, and WRF-Chem. There are two dynamics solvers in
the WSF: The Advanced Research WRF (ARW) solver (originally referred to as the
Eulerian mass or “em” solver) developed primarily at NCAR, and the NMM
(Nonhydrostatic Mesoscale Model) solver developed at NCEP. Community support
for the former is provided by the MMM Division of NCAR and that for the latter
is provided by the Developmental Testbed Center (DTC).

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ARW is the ARW dynamics solver together with other components of the WRF system
compatible with that solver and used in producing a simulation. Thus, it is a
subset of the WRF modeling system that, in addition to the ARW solver,
encompasses physics schemes, numerics/dynamics options, initialization
routines, and a data assimilation package (WRF-Var). The ARW solver shares the
WSF with the NMM solver and all other WRF components within the framework.

Physics packages are largely shared by both the ARW and NMM solvers.


ARW solver is comprised of the following options –

Equations: Fully compressible, Euler nonhydrostatic with a run-time hydrostatic
option available. Conservative for scalar variables.

Prognostic Variables: Velocity components u and v in Cartesian coordinate,
vertical velocity w, perturbation potential temperature, perturbation
geopotential, and perturbation surface pressure of dry air. Optionally,
turbulent kinetic energy and any number of scalars such as water vapor mixing
ratio, rain/snow mixing ratio, cloud water/ice mixing ratio, and chemical
species and tracers.

Vertical Coordinate: Terrain-following, dry hydrostatic-pressure, with vertical
grid stretching permitted. Top of the model is a constant pressure surface.

Horizontal Grid: Arakawa C-grid staggering.

Time Integration: Time-split integration using a 2nd- or 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

Turbulent Mixing and Model Filters: Sub-grid scale turbulence formulation in
both coordinate and physical space. Divergence damping, external-mode
filtering, vertically implicit acoustic step off-centering. Explicit filter

Initial Conditions: Three dimensional for real-data, and one-, two- and
three-dimensional for idealized data. Digital filtering initialization (DFI)
capability available (real-data cases).

Lateral Boundary Conditions: Periodic, open, symmetric, and specified options

Top Boundary Conditions: Gravity wave absorbing (diffusion, Rayleigh damping,
or implicit Rayleigh damping for vertical velocity). Constant pressure level at
top boundary along a material surface. Rigid lid option.

Bottom Boundary Conditions: Physical or free-slip.

Earth’s Rotation: Full Coriolis terms included.

Mapping to Sphere: Four map projections are supported for real-data simulation:
polar stereographic, Lambert conformal, Mercator, and latitude-longitude
(allowing rotated pole). Curvature terms included.

Nesting: One-way interactive, two-way interactive, and moving nests. Multiple
levels and integer ratios.

Nudging: Grid (analysis) and observation nudging capabilities available.

Global Grid: Global simulation capability using polar Fourier filter and
periodic east-west conditions.


Physics has the following options –

Microphysics: Schemes ranging from simplified physics suitable for idealized
studies to sophisticated mixed-phase physics suitable for process studies and

Cumulus parameterizations: Adjustment and mass-flux schemes for mesoscale

Surface physics: Multi-layer land surface models ranging from a simple thermal
model to full vegetation and soil moisture models, including snow cover and sea

Planetary boundary layer physics: Turbulent kinetic energy prediction or
non-local K schemes.

Atmospheric radiation physics: Longwave and shortwave schemes with multiple
spectral bands and a simple shortwave scheme suitable for climate and weather
applications. Cloud effects and surface fluxes are included.




initialization –

ARW may be run with user-defined initial conditions for idealized simulations,
or it may be run using interpolated data from either an external analysis or
forecast for real-data cases. Both 2D and 3D tests cases for idealized
simulations are provided. Several sample cases for real-data simulations are
provided, which rely on pre-processing from an external package (usually the WRF
Preprocessor System, referred to as WPS) that converts the large-scale GriB
data into a format suitable for ingest by the ARW’s real-data processor.

programs that generate the specific initial conditions for the selected
idealized or real data case function similarly. They provide the ARW with:

input data that is on the correct horizontal and vertical staggering;

hydrostatically balanced reference state and perturbation fields; and

metadata specifying such information as the date, grid physical
characteristics, and projection details.




using the real data –

initial conditions for the real-data cases are pre-processed through a separate
package called the WRF Preprocessing System (WPS, see Fig. 2). The output from
WPS is passed to the real-data pre-processor in the ARW— program real— which
generates initial and lateral boundary conditions. This section is primarily
about the steps taken to build the initial and the lateral boundary conditions
for a real-data case. Even though the WPS is outside of the ARW system, a brief
description is appropriate to see how the raw meteorological and static terrestrial
data are brought into the model for real-data cases.


WPS is a set of programs that takes terrestrial and meteorological data
(typically in GriB format) and transforms them for input to the ARW
pre-processor program for real-data cases (real). Figure 2 shows the flow of
data into and out of the WPS system. The first step for the WPS is to define a
physical grid (including the projection type, location on the globe, number of
grid points, nest locations, and grid distances) and to interpolate static
fields to the prescribed domain. Independent of the domain configuration, an
external analysis or forecast is processed by the WPS GriB decoder, which
diagnoses required fields and reformats the GriB data into an internal binary
format. With a specified domain, WPS horizontally interpolates the
meteorological data onto the projected domain(s). The output data from WPS
supplies a complete 3-dimensional snapshot of the atmosphere on the selected
model grid’s horizontal staggering at the selected time slices, which is sent
to the ARW pre-processor program for real-data cases.


input to the ARW real-data processor from WPS contains 3-dimensional fields
(including the surface) of temperature (K), relative humidity (and the
horizontal components of momentum (m/s, already rotated to the model
projection). The 2-dimensional static terrestrial fields include: albedo,
Coriolis parameters, terrain elevation, vegetation/land-use type, land/water mask,
map scale factors, map rotation angle, soil texture category, vegetation
greenness fraction, annual mean temperature, and latitude/longitude. The
2-dimensional time-dependent fields from the external model, after processing
by WPS, include: surface pressure and sea-level pressure (Pa), layers of soil
temperature (K) and soil moisture (kg/kg, either total moisture, or binned into
total and liquid content), snow depth (m), skin temperature (K), sea surface temperature
(K), and a sea ice flag.



ARW supports horizontal nesting that allows resolution to be focused over a
region of interest by introducing an additional grid (or grids) into the
simulation. In the current implementation, only horizontal refinement is
available: there is no vertical nesting option. The nested grids are
rectangular and are aligned with the parent (coarser) grid within which they
are nested. Additionally, the nested grids allow any integer spatial (?xcoarse/?xfine)
and temporal refinements of the parent grid (the spatial and temporal
refinements are usually, but not necessarily the same). This nesting
implementation is in many ways similar to the implementations in other
mesoscale and cloud scale models (e.g. MM5, ARPS, COAMPS). The major
improvement in the ARW’s nesting infrastructure compared with techniques used
in other models is the ability to compute nested simulations efficiently on
parallel distributed-memory computer systems, which includes support for moving
nested grids. The WRF Software Framework, described in Michalakes et al.

(2004), makes these advances possible. In this chapter, we describe the various
nesting options available in the ARW and the numerical coupling between the



and 2-way grid nesting –

grid simulations can be produced using either 1-way nesting or 2-way nesting as
outlined in Fig. 3. The 1-way and 2-way nesting options refer to how a coarse
grid and the fine grid interact. In both the 1-way and 2-way simulation modes,
the fine grid boundary conditions (i.e., the lateral boundaries) are
interpolated from the coarse grid forecast. In a 1-way nest, this is the only
information exchange between the grids (from the coarse grid to the fine grid).

Hence, the name 1-way nesting. In the 2-way nest integration, the fine grid
solution replaces the coarse grid solution for coarse grid points that lie
inside the fine grid. This information exchange between the grids is now in
both directions (coarse-to-fine for the fine-grid lateral boundary computation and
fine-to-coarse during the feedback at each coarse-grid time step). Hence, the
name 2-way nesting.


1-way nest set-up may be run in one of two different methods. One option is to
produce the nested simulation as two separate ARW simulations as described in
the leftmost box in Fig. 3. In this mode, the coarse grid is integrated first
and the coarse grid forecast is completed. Output from the coarse grid
integration is then processed to provide boundary conditions for the nested run
(usually at a much lower temporal frequency than the coarse grid time step),
and this
is followed by the complete time integration of fine (nested) grid. Hence, this
1-way option is equivalent to running two separate simulations with a
processing step in between.


second 1-way option (lockstep with no feedback), depicted in the middle box in
Fig. 3, is run as a traditional simulation with two (or more) grids integrating
concurrently, except with the feedback runtime option shut off. This option
provides lateral boundary conditions to the fine grid at each coarse grid time
step, which is an advantage of the concurrent 1-way method (no feedback).


grid configurations –

simulation involves one outer grid and may contain multiple inner nested grids.

In the ARW, each nested region is entirely contained within a single coarser
grid, referred to as the parent grid. The finer, nested grids are referred to
as child grids. Using this terminology, children are also parents when multiple
levels of nesting are used. The fine grids may be telescoped to any depth
(i.e., a parent grid may contain one or more child grids, each of which in turn
may successively contain one or more child grids; Fig. 4a), and several fine
grids may share the same parent at the same level of nesting (Fig. 4b). Any
valid fine grid may either be a static domain or it may be a moving nest (with
either prescribed incremental shifts or with automatic moves via a vortex following
algorithm, such as tracking the minimum of the 500 mb height). The ARW does not
permit overlapping grids, where a coarse grid point is contained within more than
a single child grid (i.e., both of which are at the same nest level with
respect to the parent; Fig.

4c). In addition, no grid can have more than a single parent (Fig. 4d). For
global domains, a fine grid domain cannot cross the periodic lateral boundary
of the parent domain.


both 1-way and 2-way nested grid simulations, the ratio of the parent
horizontal grid distance to the child horizontal grid distance (the spatial
refinement ratio) must be an integer. For 2-way and concurrent 1-way nesting,
this is also true for the time steps (the temporal refinement ratio). The model
does allow the time step refinement ratio to differ from the spatial refinement
ratio. Also, nested grids on the same level (i.e., children who have the same
parent) may have different spatial and temporal refinement ratios. For example,
in Fig. 4b, the horizontal grid resolution for domain 1 could be 90 km, for
domain 2 could be 45 km, and for domain 3 could be 30 km.