## WRF (the Weather Research and Forecasting model)

WRF is a state-of-the-art atmospheric modeling system designed for both meteorological research and numerical weather prediction.

WeatherResearchForecastingnumerical weather prediction
592

#### Authorship

Homepage:
View
Is authorship not correct? Feed back

#### Classification(s)

Application-focused categoriesNatural-perspectiveAtmospheric regions

#### Model Description

English {{currentDetailLanguage}} English

# Basic Information

## Introduction

The Advanced Research WRF (ARW) modeling system has been in development for the past eighteen years. The current release is Version 4, available since June 2018. The ARW is designed to be a flexible, state-of-the-art atmospheric simulation system that is portable and efficient on available parallel computing platforms. The ARW is suitable for use in a broad range of applications across scales ranging from meters to thousands of kilometers, including:

• Idealized simulations (e.g. LES, convection, baroclinic waves)
• Parameterization research
• Data assimilation research
• Forecast research
• Real-time NWP
• Hurricane research
• Regional climate research
• Fire research
• Coupled-model applications
• Teaching

## The WRF Modeling System Program Components

The following figure shows the flowchart for the WRF Modeling System Version 4.

WRF Modeling System

As shown in the diagram, the WRF Modeling System consists of these major programs:

• The WRF Preprocessing System (WPS)
• WRF-DA
• ARW solver
• Post-processing & Visualization tools

WPS

This program is used primarily for real-data simulations. Its functions include 1) defining simulation domains; 2) interpolating terrestrial data (such as terrain, landuse, and soil types) to the simulation domain; and 3) degribbing and interpolating meteorological data from another model to this simulation domain. Its main features include:

·      GRIB 1/2 meteorological data from various centers around the world

·      USGS 24 category and MODIS 20 category land datasets; USGS GMTED2010 elevation dataset; Global 5-minutes United Nation FAO, and North-America STATSGO 30 sec soil category dataset; 30 and 15 sec MODIS greenness fraction data; 30 sec MODIS-based leaf-area index; MODIS 0.05 degree monthly albedo and snow albedo data; and 1-degree deep soil temperature data; plus a few specialized datasets

·      Map projections for 1) polar stereographic, 2) Lambert-Conformal, 3) Mercator and 4) latitude-longitude

·      Nesting

·      User-interfaces to input other static data as well as meteorological data.

WRF-DA

This program is optional, but can be used to ingest observations into the interpolated analyses created by WPS. It can also be used to update WRF model's initial conditions when the WRF model is run in cycling mode. Its main features are as follows:

·      It is based on an incremental variational data assimilation technique, and has both 3D-Var and 4D-Var capabilities

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

·      It also includes the capability of 3D and 4D hybrid data assimilation (Variational + Ensemble)

·      The conjugate gradient method is utilized to minimize the cost function in the analysis control variable space

·      Analysis is performed on an un-staggered Arakawa A-grid

·      Analysis increments are interpolated to staggered Arakawa C-grid and it gets added to the background (first guess) to get the final analysis of the WRF-model grid

·      Conventional observation data input may be supplied either in 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 the nonlinearity

·      Capability to compute adjoint sensitivity

·      Horizontal component of the background (first guess) error is represented via a recursive filter (for regional) or power spectrum (for global). The vertical component 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 part of the cost function is done via the control variable transform U defined as B= UUT

·      It includes the “gen_be” utility to generate the climatological background error covariance estimate via the NMC-method or ensemble perturbations

·      A utility program to update WRF boundary condition file after WRF-DA

#### ARW Solver

This is the key component of the modeling system, which is composed of several initialization programs for idealized, and real-data simulations, and the numerical integration program. The key features of the WRF model include:

·       Fully compressible nonhydrostatic equations with hydrostatic option

·       Regional and global applications

·       Complete Coriolis and curvature terms

·       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)

·       Mass-based hybrid sigma-pressure vertical coordinate

·       Vertical grid-spacing can vary with height

·       Map-scale factors for these projections:

o   polar stereographic (conformal)

o   Lambert-conformal

o   Mercator (conformal)

o   Latitude and longitude, which can be rotated

·       Arakawa C-grid staggering

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

·       Scalar-conserving flux form for prognostic variables

·       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:

o   small step horizontally explicit, vertically implicit

o   divergence damping option and vertical time off-centering

o   external-mode filtering option

·       Upper boundary absorption and Rayleigh damping

·       Lateral boundary conditions

o   idealized cases: periodic, symmetric, and open radiative

o   real cases: specified with relaxation zone

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

·       Ocean models

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

·       Spectral nudging

·       Digital filter initialization

·       Orographic gravity wave drag

·       Stochastic parameterization schemes

·       A number of idealized examples

#### Graphics and Verification Tools

Several programs are supported, including RIP4 (based on NCAR Graphics), NCAR Graphics Command Language (NCL), and conversion programs for other readily available graphics packages like GrADS.

Program VAPOR, Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers (http://www.vapor.ucar.edu/), is a 3-dimensional data visualization tool, and it is developed and supported by the VAPOR team at NCAR (vapor@ucar.edu).

Program MET, Model Evaluation Tools (http://www.dtcenter.org/met/users/), is developed and supported by the Developmental Testbed Center at NCAR (met_help@ucar.edu).

The details of these programs (with the exception of the MET program) are described more in the later chapters of this user's guide.  See the above link for information about MET.

#### How to Cite

National Center for Atmospheric Research (NCAR) (2019). WRF (the Weather Research and Forecasting model), Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/950852f9-bc9c-4092-9808-a36c4ae4d0c5

#### History

Last modifier :
Yue Songshan
Last modify time :
2020-12-28
Modify times :
View History

#### Authorship

Homepage:
View
Is authorship not correct? Feedback

#### History

Last modifier :
Yue Songshan
Last modify time :
2020-12-28
Modify times :
View History

#### QR Code

• {{curRelation.name}}
{{curRelation.name}}

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}

Related Items
{{props.row.name}}

You can link resource from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

{{ props.row.description }}
{{ props.row.description }}
Drop the file here, orclick to upload.
File size should not exceed 10m.
Select From My Space

These authorship information will be submitted to the contributor to review.

Cancel Submit
Cancel Submit
{{ item.label }} {{ item.value }}
{{props.row.localName}}
Model Name :
Cancel Submit Cancel Submit
Name:
Version:
Model Type:
Model Domain:
Scale:
Purpose:
Principles:
Incorporated models:

Model part of

larger framework

Process:
Information:
Initialization:
Hardware Requirements:
Software Requirements:
Inputs:
Outputs:
Cancel Submit
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
Cancel Submit

Yes, this is it Cancel

OK
Cancel Confirm
Model Classifications 1
Model Classifications 2
Title Author Date Journal Volume(Issue) Pages Links Doi Operation

#### NEW

Name:
Affiliation:
Email:
Homepage:

Yes, this is it Cancel

Confirm
path
:
/{{path.name}}
search results of '{{searchContentShown}}'

#### No content to show

{{item.name}}

.

{{item.suffix}}

.{{item.suffix}}

Max: {{toDecimal1(capacity/1073741824)}} GB
Copy
Delete
Rename
/{{path.label}}
Change
/{{path.name}}
Select File
Cancel Confirm
path
:
/{{path.name}}
/..
Cancel Confirm
{{ data.name }}
##### You have select  {{multipleSelection.length+multipleSelectionMyData.length}} data .
• Output Data
• {{item.computableName}}@{{formatDate(item.runTime)}}
{{scope.row.type}}
{{ scope.row.tag }}
• Fork Data
{{it.category}}

#### NEW

Name:
Affiliation:
Email:
Homepage:
previous next conform
Model Classifications

You can link related {{relateItemType}} from repository to this model item, or you can create a new {{relateItemType.toLowerCase()}}.

Related Items
{{ props.row.description }}
{{ props.row.description }}
Cancel OK