WRF Modeling System Overview

As shown in Figure 1, the WRF-NMM Modeling System consists of these major components:

  • WRF Preprocessing System (WPS)
  • WRF-NMM solver
  • Post-Processing and graphical tools
  • Model Evaluation Tools (MET)

WRF Preprocessing System (WPS)

This program is used for real-data simulations. Its functions include:

  • Defining the simulation domain;
  • Interpolating terrestrial data (such as terrain, land-use, and soil types) to the simulation domain;
  • Degribbing and interpolating meteorological data from another model to the simulation domain and the model coordinate.

(For more details, see Chapter 3 of the WRF-NMM User's Guide .)

WRF-NMM Solver

The key features of the WRF-NMM model are:

  • Fully compressible, non-hydrostatic model with a hydrostatic option (Janjic, 2003);
  • Hybrid (sigma-pressure) vertical coordinate;
  • Arakawa E-grid;
  • Forward-backward scheme for horizontally propagating fast waves, implicit scheme for vertically propagating sound waves, Adams-Bashforth Scheme for horizontal advection, and Crank-Nicholson scheme for vertical advection. The same time step is used for all terms;
  • Conservation of a number of first and second order quantities, including energy and enstrophy (Janjic 1984);
  • Full physics options for land-surface, planetary boundary layer, atmospheric and surface radiation, microphysics, and cumulus convection;
  • One-way and two-way nesting with multiple nests and nest levels.

The WRF-NMM model code contains an initialization program (real_nmm.exe; see Chapter 4 of the WRF-NMM User's Guide) and a numerical integration program (wrf.exe; see Chapter 5 of the WRF-NMM User's Guide for more details and references.)

Unified Post-Processor (UPP)

This program can be used to post-process both WRF-ARW and WRF-NMM forecasts and was designed to:

  • Interpolate the forecasts from the model's native vertical coordinate to NWS standard output levels;
  • Destagger the forecasts from the staggered native grid to a regular non-staggered grid;
  • Compute diagnostic output quantities;
  • Output the results in NWS and WMO standard GRIB format.

(For more details, see Chapter 7 of the WRF-NMM User's Guide .)

Read, Interpolate, Plot (RIP)

This program can be used to plot both WRF-ARW and WRF-NMM forecasts. Some basic features include:

  • Uses a preprocessing program to read model output and convert this data into standard RIP format data files;
  • Makes horizontal plots, vertical cross sections and skew-T/log p soundings;
  • Calculates and plots backward and forward trajectories;
  • Makes a data set for use in the Vis5D software package.

(For more details, see Chapter 7 of the WRF-NMM User's Guide .)

Model Evaluation Tool (MET)

MET is designed to be a highly-configurable, state-of-the-art suite of verification tools. It was developed using output from the Weather Research and Forecasting (WRF) modeling system (both the ARW and NMM dynamic cores) but may be applied to the output of other modeling systems as well.

MET provides a variety of verification techniques, including:

  • Standard verification scores comparing gridded model data to point-based observations;
  • Standard verification scores comparing gridded model data to point-based observations;
  • Standard verification scores comparing gridded model data to gridded observations;
  • Spatial verification methods comparing gridded model data to gridded observations using neighborhood, object-based, and intensity-scale decomposition approaches;
  • Ensemble and probabilistic verification methods comparing gridded model data to point-based or gridded observations;
  • Aggregating the output of these verification methods through time and space.

MET is developed and supported by the Developmental Testbed Center and full details can be found on the MET User's site.

Gridpoint Statistical Interpolation (GSI)

The current version of GSI is a three dimensional variational (3D-Var) data assimilation (DA) system. The analysis variables in GSI are

  • stream function
  • unbalanced velocity potential
  • unbalanced virtual temperature
  • unbalanced surface pressure
  • pseudo relative humidity (qoption =1) or normalized relative humidity (qoption=2)

GSI can assimilate (but not limited to) the following observation types:

  • Conventional observations, e.g., radiosondes, pibal winds, synthetic tropical cyclone winds, wind profilers, conventional aircraft reports, ASDAR aircraft reports, dropsondes, MODIS IR and water vapor winds, GSM, METEOSAT and GOES cloud drift IR and visible winds, GOES water vapor cloud top winds, surface land observations, surface ship and buoy observations, SSM/I wind speeds, QuikScat wind speed and direction, SSM/I precipitable water, SSM/I and TRMM TMI precipitation estimates, GPS precipitable water estimates, GPS radio occultation profiles, SBUV ozone profiles...
  • Radar observations: Doppler radial velocities, VAD (NEXRAD) winds ...
  • Satellite observations: GOES 11 and 12 sounders, AMSU-A, AMSU-B/MHS, HIRS, AIRS...

The observation operator is used to simulate observations from model variables. It can be a simply interpolation procedure from model space to observation space or a more complex procedure depending on the assimilated observation types. For satellite radiance data, the GSI uses the Community Radiative Transfer Model (CRTM) developed by the Joint Center for Satellite Data Assimilation (JCSDA).

The background and observation errors are critical components for a variational data assimilation system since they form weights for the background and observation terms of the cost function. While they are up to the users'definition, the released GSI tar file provides their estimations computed from NCEP/EMC as a start applicable to both global and regional applications.

GSI is supported by the Developmental Testbed Center and more details can be found on the GSI User's site.