The WSF: The Advanced Research WRF (ARW) solver

TheWeather Research and Forecasting (WRF) model is a numerical weather prediction(NWP) model designed to carry out research operations on the atmosphericsystem. The spectrum of options available in it for performing operationspertaining to physics and dynamics is very broad. It is suitable for a broadspan of applications across scales ranging from large-eddy to globalsimulations.

Such applications include real-time NWP, data assimilationdevelopment and studies, parameterized-physics research, regional climate simulations,air quality modeling, atmosphere-ocean coupling, and idealized simulations. Theprincipal components of the WRF system are depicted in Figure 1. The WRFSoftware Framework (WSF) provides the infrastructure that accommodates thedynamics solvers, physics packages that interface with the solvers, programsfor initialization, WRF-Var, and WRF-Chem.

There are two dynamics solvers inthe WSF: The Advanced Research WRF (ARW) solver (originally referred to as theEulerian mass or “em” solver) developed primarily at NCAR, and the NMM(Nonhydrostatic Mesoscale Model) solver developed at NCEP. Community supportfor the former is provided by the MMM Division of NCAR and that for the latteris provided by the Developmental Testbed Center (DTC). TheARW is the ARW dynamics solver together with other components of the WRF systemcompatible with that solver and used in producing a simulation. Thus, it is asubset of the WRF modeling system that, in addition to the ARW solver,encompasses physics schemes, numerics/dynamics options, initializationroutines, and a data assimilation package (WRF-Var).

The ARW solver shares theWSF with the NMM solver and all other WRF components within the framework.Physics packages are largely shared by both the ARW and NMM solvers. TheARW solver is comprised of the following options – •Equations: Fully compressible, Euler nonhydrostatic with a run-time hydrostaticoption available. Conservative for scalar variables.•Prognostic Variables: Velocity components u and v in Cartesian coordinate,vertical velocity w, perturbation potential temperature, perturbationgeopotential, and perturbation surface pressure of dry air. Optionally,turbulent kinetic energy and any number of scalars such as water vapor mixingratio, rain/snow mixing ratio, cloud water/ice mixing ratio, and chemicalspecies and tracers.•Vertical Coordinate: Terrain-following, dry hydrostatic-pressure, with verticalgrid stretching permitted.

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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-Kuttascheme with smaller time step for acoustic and gravity-wave modes. Variabletime step capability.•Spatial Discretization: 2nd- to 6th-order advection options in horizontal andvertical.•Turbulent Mixing and Model Filters: Sub-grid scale turbulence formulation inboth coordinate and physical space.

Divergence damping, external-modefiltering, vertically implicit acoustic step off-centering. Explicit filteroption.•Initial Conditions: Three dimensional for real-data, and one-, two- andthree-dimensional for idealized data. Digital filtering initialization (DFI)capability available (real-data cases).•Lateral Boundary Conditions: Periodic, open, symmetric, and specified optionsavailable.•Top Boundary Conditions: Gravity wave absorbing (diffusion, Rayleigh damping,or implicit Rayleigh damping for vertical velocity). Constant pressure level attop 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. Multiplelevels and integer ratios.•Nudging: Grid (analysis) and observation nudging capabilities available.•Global Grid: Global simulation capability using polar Fourier filter andperiodic east-west conditions.

 ModelPhysics has the following options – •Microphysics: Schemes ranging from simplified physics suitable for idealizedstudies to sophisticated mixed-phase physics suitable for process studies andNWP.•Cumulus parameterizations: Adjustment and mass-flux schemes for mesoscalemodeling.•Surface physics: Multi-layer land surface models ranging from a simple thermalmodel to full vegetation and soil moisture models, including snow cover and seaice.•Planetary boundary layer physics: Turbulent kinetic energy prediction ornon-local K schemes.•Atmospheric radiation physics: Longwave and shortwave schemes with multiplespectral bands and a simple shortwave scheme suitable for climate and weatherapplications. Cloud effects and surface fluxes are included.   Modelinitialization – TheARW may be run with user-defined initial conditions for idealized simulations,or it may be run using interpolated data from either an external analysis orforecast for real-data cases.

Both 2D and 3D tests cases for idealizedsimulations are provided. Several sample cases for real-data simulations areprovided, which rely on pre-processing from an external package (usually the WRFPreprocessor System, referred to as WPS) that converts the large-scale GriBdata into a format suitable for ingest by the ARW’s real-data processor. Theprograms that generate the specific initial conditions for the selectedidealized 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 physicalcharacteristics, and projection details.   Initializationusing the real data – Theinitial conditions for the real-data cases are pre-processed through a separatepackage called the WRF Preprocessing System (WPS, see Fig. 2). The output fromWPS is passed to the real-data pre-processor in the ARW— program real— whichgenerates initial and lateral boundary conditions. This section is primarilyabout the steps taken to build the initial and the lateral boundary conditionsfor a real-data case.

Even though the WPS is outside of the ARW system, a briefdescription is appropriate to see how the raw meteorological and static terrestrialdata are brought into the model for real-data cases. TheWPS is a set of programs that takes terrestrial and meteorological data(typically in GriB format) and transforms them for input to the ARWpre-processor program for real-data cases (real). Figure 2 shows the flow ofdata into and out of the WPS system. The first step for the WPS is to define aphysical grid (including the projection type, location on the globe, number ofgrid points, nest locations, and grid distances) and to interpolate staticfields to the prescribed domain.

Independent of the domain configuration, anexternal analysis or forecast is processed by the WPS GriB decoder, whichdiagnoses required fields and reformats the GriB data into an internal binaryformat. With a specified domain, WPS horizontally interpolates themeteorological data onto the projected domain(s). The output data from WPSsupplies a complete 3-dimensional snapshot of the atmosphere on the selectedmodel grid’s horizontal staggering at the selected time slices, which is sentto the ARW pre-processor program for real-data cases. Theinput to the ARW real-data processor from WPS contains 3-dimensional fields(including the surface) of temperature (K), relative humidity (and thehorizontal components of momentum (m/s, already rotated to the modelprojection).

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, vegetationgreenness fraction, annual mean temperature, and latitude/longitude. The2-dimensional time-dependent fields from the external model, after processingby WPS, include: surface pressure and sea-level pressure (Pa), layers of soiltemperature (K) and soil moisture (kg/kg, either total moisture, or binned intototal and liquid content), snow depth (m), skin temperature (K), sea surface temperature(K), and a sea ice flag. Nesting– TheARW supports horizontal nesting that allows resolution to be focused over aregion of interest by introducing an additional grid (or grids) into thesimulation. In the current implementation, only horizontal refinement isavailable: there is no vertical nesting option. The nested grids arerectangular and are aligned with the parent (coarser) grid within which theyare nested. Additionally, the nested grids allow any integer spatial (?xcoarse/?xfine)and temporal refinements of the parent grid (the spatial and temporalrefinements are usually, but not necessarily the same). This nestingimplementation is in many ways similar to the implementations in othermesoscale and cloud scale models (e.

g. MM5, ARPS, COAMPS). The majorimprovement in the ARW’s nesting infrastructure compared with techniques usedin other models is the ability to compute nested simulations efficiently onparallel distributed-memory computer systems, which includes support for movingnested grids. The WRF Software Framework, described in Michalakes et al.(2004), makes these advances possible. In this chapter, we describe the variousnesting options available in the ARW and the numerical coupling between thegrids.  1-wayand 2-way grid nesting – Nestedgrid simulations can be produced using either 1-way nesting or 2-way nesting asoutlined in Fig. 3.

The 1-way and 2-way nesting options refer to how a coarsegrid 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) areinterpolated from the coarse grid forecast. In a 1-way nest, this is the onlyinformation 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 gridsolution replaces the coarse grid solution for coarse grid points that lieinside the fine grid. This information exchange between the grids is now inboth directions (coarse-to-fine for the fine-grid lateral boundary computation andfine-to-coarse during the feedback at each coarse-grid time step). Hence, thename 2-way nesting.

 The1-way nest set-up may be run in one of two different methods. One option is toproduce the nested simulation as two separate ARW simulations as described inthe leftmost box in Fig. 3. In this mode, the coarse grid is integrated firstand the coarse grid forecast is completed. Output from the coarse gridintegration 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 thisis followed by the complete time integration of fine (nested) grid. Hence, this1-way option is equivalent to running two separate simulations with aprocessing step in between.

 Thesecond 1-way option (lockstep with no feedback), depicted in the middle box inFig. 3, is run as a traditional simulation with two (or more) grids integratingconcurrently, except with the feedback runtime option shut off. This optionprovides lateral boundary conditions to the fine grid at each coarse grid timestep, which is an advantage of the concurrent 1-way method (no feedback). Possiblegrid configurations – Asimulation involves one outer grid and may contain multiple inner nested grids.In the ARW, each nested region is entirely contained within a single coarsergrid, referred to as the parent grid. The finer, nested grids are referred toas child grids. Using this terminology, children are also parents when multiplelevels 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 turnmay successively contain one or more child grids; Fig. 4a), and several finegrids may share the same parent at the same level of nesting (Fig.

4b). Anyvalid fine grid may either be a static domain or it may be a moving nest (witheither prescribed incremental shifts or with automatic moves via a vortex followingalgorithm, such as tracking the minimum of the 500 mb height). The ARW does notpermit overlapping grids, where a coarse grid point is contained within more thana single child grid (i.e., both of which are at the same nest level withrespect to the parent; Fig.4c). In addition, no grid can have more than a single parent (Fig. 4d).

Forglobal domains, a fine grid domain cannot cross the periodic lateral boundaryof the parent domain. Forboth 1-way and 2-way nested grid simulations, the ratio of the parenthorizontal grid distance to the child horizontal grid distance (the spatialrefinement 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 modeldoes allow the time step refinement ratio to differ from the spatial refinementratio. Also, nested grids on the same level (i.e.

, children who have the sameparent) may have different spatial and temporal refinement ratios. For example,in Fig. 4b, the horizontal grid resolution for domain 1 could be 90 km, fordomain 2 could be 45 km, and for domain 3 could be 30 km.