WRF Overview¶

The Advanced Research WRF (WRF-ARW) model was developed over several years. The most recent release is available from the WRF GitHub Repository. The WRF model is a flexible, state-of-the-art atmospheric simulation system, and is portable and efficient on parallel computing platforms. It is suitable for use across scales, ranging from meters to thousands of kilometers, for a broad range of applications, including the following.

Research Applications	Functional Applications
Parameterization	Idealized simulations
Data assimilation	Real-time numerical weather prediction
Forecasting	Model coupling
Tropical cyclones	Teaching
Regional climate
Fire

WRF Modeling System Components¶

The following is a flowchart of the WRF Modeling System.

As shown in the diagram, the WRF Modeling System consists of the following programs.

WRF Preprocessing System (WPS) (WPS)
Initialization (Real and Ideal)
WRF-ARW Solver (WRF)
WRF Data Assimilation (WRFDA) (WRFDA)
Post-processing, Analysis, and Visualization Tools

WRF Preprocessing System (WPS)¶

The WPS is used for real-data simulations. Its functions are to 1) define simulation domains; 2) interpolate terrestrial data (e.g., terrain, landuse, and soil types) to the simulation domain; and 3) degrib and interpolate meteorological input data from an outside model to the simulation domain.

Initialization¶

The WRF model is capable of simulating both real- and ideal-data cases. ideal.exe is a program that simulates in a controlled environment. Idealized simulations are initiated from an included initial condition file from an existing sounding, and assumes a simplified orography. Real-data cases use output from the WPS, which includes meteorological input originally generated from a previously-run external analysis or forecast model (e.g., GFS) as input to the real.exe program.

WRF-ARW Solver¶

The wrf.exe program is the primary component of the modeling system.

Key Features

Fully-compressible nonhydrostatic equations with a hydrostatic option

Complete Coriolis and curvature terms

Mass-based hybrid sigma-pressure vertical coordinate

Map-scale factors for these projections:

Polar Stereographic (conformal)

Lambert-conformal

Mercator (conformal)

Latitude and longitude, which can be rotated

Arakawa C-grid staggering

Scalar-conserving flux form for prognostic variables

Upper boundary absorption and Rayleigh damping

Lateral boundary conditions

Idealized cases: periodic, symmetric, and open radiative

Real cases: specified with relaxation zone

Full physics options for land-surface, planetary boundary layer, atmospheric and surface radiation, microphysics and cumulus convection

Orographic gravity wave drag

Additional Options

Regional and global applications

Two-way nesting with multiple nests and nest levels

Concurrent one-way nesting with multiple nests and nest levels

Offline one-way nesting with vertical nesting

Moving nests (prescribed moves and vortex tracking)

Vertical grid-spacing can vary with height

Runge-Kutta 2nd and 3rd order time integration options

2nd to 6th order advection options (horizontal and vertical)

Monotonic transport and positive-definite advection option for moisture, scalar, tracer, and TKE

Weighted Essentially Non-Oscillatory (WENO) advection option

Time-split small step for acoustic and gravity-wave modes:

Small step horizontally explicit, vertically implicit

Divergence damping option and vertical time off-centering

External-mode filtering option

Ocean models

Grid analysis nudging using separate upper-air and surface data, and observation nudging

Spectral nudging

Digital filter initialization

Adaptive time stepping

Stochastic parameterization schemes

A number of idealized examples

WRF Data Assimilation (WRFDA)¶

WRF Data Assimilation (WRFDA) is an optional program used to ingest observations into interpolated analysis created by WPS. It may also be used to update the WRF model’s initial conditions by running in “cycling” mode. WRFDA’s primary features are:

The capability of 3D and 4D hybrid data assimilation (Variational + Ensemble)

Based on an incremental variational data assimilation technique

Tangent linear and adjoint of WRF are fully integrated with WRF for 4D-Var

Utilizes the conjugate gradient method to minimize cost function in the analysis control variable space

Analysis on an un-staggered Arakawa A-grid

Analysis increments interpolated to staggered Arakawa C-grid, which is then added to the background (first guess) to get the final analysis of the WRF-model grid

Conventional observation data input may be supplied in either ASCII format via the obsproc utility, or PREPBUFR format

Multiple-satellite observation data input may be supplied in BUFR format

Two fast radiative transfer models, CRTM and RTTOV, are interfaced to WRFDA to serve as satellite radiance observation operator

Variational bias correction for satellite radiance data assimilation

All-sky radiance data assimilation capability

Multiple radar data (reflectivity & radial velocity) input is supplied through ASCII format

Multiple outer loop to address nonlinearity

Capability to compute adjoint sensitivity

Horizontal background (first guess) error is represented via a recursive filter (for regional) or power spectrum (for global)

Vertical background error is applied through projections on climatologically-generated, averaged eigenvectors and its corresponding Eigen values

Horizontal and vertical background errors are non-separable. Each eigenvector has its own horizontal climatologically-determined length scale

Preconditioning of the background of the cost function is done via the control variable transform U defined as B=UUT

gen_be utility to generate climatological background error covariance estimate via the NMC-method or ensemble perturbations

Utility program to update WRF boundary condition file after WRF-DA

Post-processing, Analysis, and Visualization Tools¶

Several post-processing programs are supported, including RIP (based on NCAR Graphics), NCAR Graphics Command Language (NCL), and conversion programs for other readily-available graphics packages (e.g., GrADS).

wrf-python (wrf-python) is a collection of diagnostic and interpolation routines for use with output from the WRF model.

NCL (NCAR Command Language) is a free, interpreted language designed specifically for scientific data processing and visualization. NCL has robust file input and output. It can read in netCDF, HDF4, HDF4-EOS, GRIB, binary and ASCII data. The graphics are world-class and highly customizable.

RIP (Read/Interpolate/Plot) is a Fortran program that invokes NCAR Graphics routines for the purpose of visualizing output from gridded meteorological data sets, primarily from mesoscale numerical models.

ARWpost is a package that reads-in WRF-ARW model data and creates GrADS output files.

UPP (Unified Post Processing) is a system deleveloped at the National Centers for Environmental Prediction (NCEP) and is used operationally for models maintained by NCEP. It is currently supported by the Research Applications Laboratory (RAL) at NSF NCAR.

VAPOR (Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers), is a 3-dimensional data visualization tool developed and supported by the VAPOR team at NSF NCAR (vapor at ucar dot edu).

MET (Model Evaluation Tools), is developed and supported by the Developmental Testbed Center at NSF NCAR (met_help at ucar dot edu).

Details of these programs (with the exception of MET) can be found in the Post-processing, Utilities, and Tools section of this users’ guide.