WRF

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The Weather Research and Forecasting (WRF) model is a next-generation mesoscale numerical weather prediction (NWP) and data assimilation system. Main collaborators in the development of this system include; the National Centres for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (the National Centres for Environmental Prediction (NCEP) and the Forecast Systems Laboratory (FSL), the Air Force Weather Agency (AFWA), the Naval Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration (FAA).

Specifically designed for users in operational forecasting as well as atmospheric research, WRF is a “Community Model” (i.e. open source). It may be utilised for a variety of applications with spatial scales ranging from a few metres to thousands of kilometres. The model features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and enables users to generate simulations using real data or idealized configurations. The version of WRF currently available is 3.0.1.1 (released August 2008) with 23 March 2009 the target release date for version 3.1. WRF replaces MM5 as the primary mesoscale NWP supported by NWS/NOAA.

Official WRF website

 WRF system overview

The model system comprises of a number of components, illustrated in the flowchart below, taken from the summer 2009 WRF users tutorial.

Two dynamic solvers lie within the WRF model; the Advanced Research WRF (ARW) solver developed primarily at NCAR, and the NMM (Non-hydrostatic Mesoscale Model) solver developed at NCEP. Idealized and real-data applications with various lateral boundary condition options are supported in WRF. In addition one-way, two-way and moving nest options may be employed. The ability of the model to run on single-processor, shared- and distributed-memory computers, facilitates a broad community of users (currently exceeding 6000 registered users).

ARW model dynamics include;

• Core based on an Eulerian solver for fully compressible non-hydrostatic equations.

• Vertical coordinate = terrain-following hydrostatic pressure coordinate.

• Prognostic variables include;

  • column mass of dry air (mu),

  • velocities u, v and w (vertical velocity),

  • potential temperature,

  • Geopotential.

(Non conserved variables, e.g. temperature, pressure, density, are derived from the prognostic variables).

• Grid staggering = Arakawa C-grid.

• Runge-Kutta 2nd and 3rd order time integration schemes, and 2nd to 6th order advection schemes in both horizontal and vertical.

• Split-explicit 2nd order time integration for acoustic and gravity-wave modes.

WRF-Var is used to assimilate a host of observations to produce optimal initial conditions. Data assimilation combines observations and NWP output to produce an improved analysis of the atmosphere. Variational data assimilation utilises iterative minimisation of a prescribed cost, i.e. The differences between the analysis and the observations/NWP product are penalised based on their perceived error. Originally adapted from the 3DVAR used in MM5, WRF-Var is based on an incremental variation data assimilation technique, and supports 3D-VAR capability in the current release.

The WRF Pre-processing System (WPS) defines the WRF grid and generates map, elevation and land information for WRF. It may take real-data analyses/forecasts from another model and interpolate the data to the WRF grid. The time-dependent (analysis) fields consist of 3d wind, potential temperature, and water vapour, and a number of 2d fields. The standard output is in netCDF format, and can be displayed by one or more graphic tools: NCAR Graphics NCL, GrADS, or RIP4.