Weather Research and Forecasting (WRF) Model
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| Weather Research and Forecasting (WRF) Model | |
|---|---|
| Name | Weather Research and Forecasting Model |
| Developer | National Center for Atmospheric Research; National Oceanic and Atmospheric Administration; community contributors |
| Initial release | 2000s |
| Latest release | ongoing |
| Written in | Fortran; C; MPI (message passing interface) |
| Operating system | Unix-like; Linux; macOS |
| License | open-source (various community licenses) |
| Website | WRF home (community) |
Weather Research and Forecasting (WRF) Model The Weather Research and Forecasting (WRF) Model is a numerical weather prediction and research system developed for both atmospheric research and operational forecasting needs, used by institutions such as the National Center for Atmospheric Research, National Oceanic and Atmospheric Administration, European Centre for Medium-Range Weather Forecasts, United States Air Force and university groups. It supports nested grids and multiple dynamical cores and is widely applied across agencies including NASA, NOAA, Met Office, Chinese Academy of Sciences and Indian Institute of Tropical Meteorology for studies spanning mesoscale phenomena to regional climate downscaling. The WRF user community includes contributors from Princeton University, Massachusetts Institute of Technology, University of Oklahoma, Colorado State University and international partners like Japan Meteorological Agency and Australian Bureau of Meteorology.
Overview
WRF is a nonhydrostatic, compressible model framework with modular components that enable research on convection, boundary layer processes, and mesoscale dynamics, used in operational centers such as National Weather Service and research programs at Scripps Institution of Oceanography, Lamont–Doherty Earth Observatory, Woods Hole Oceanographic Institution and Pacific Northwest National Laboratory. The system comprises a dynamical core, physical parameterizations, data assimilation interfaces, and post-processing tools maintained by communities around University Corporation for Atmospheric Research, NOAA Earth System Research Laboratory and international consortia including World Meteorological Organization participants and regional centers like ECMWF and Météo-France.
Development and History
WRF development began as a collaboration among NCAR, NOAA and university partners building on earlier models such as the Penn State/NCAR Mesoscale Model (MM5), Advanced Research WRF (ARW), and influences from research at GFDL and UK Met Office; milestones include community releases, implementation of the ARW and Nonhydrostatic Mesoscale Model (NMM) cores, and adaptation by institutions like CIRA and NSSL. Funding and coordination came through programs at NSF, DOE, and agency partnerships including projects with DOD testbeds and collaborations with European Commission–funded initiatives, leading to widespread adoption across NOAA testbeds, university consortia, and international meteorological services such as KMA and MeteoSwiss.
Model Architecture and Components
WRF architecture integrates dynamical cores (for example the ARW core influenced by ARW community work and the NMM core used in some NCEP operational systems), a land surface model interface linking to schemes from NOAH and research at Colorado State University, and coupling options with ocean models developed at JPL and NOAA GFDL. The software stack relies on parallelization libraries such as MPI (message passing interface) and compilation environments used at facilities like National Energy Research Scientific Computing Center and supercomputing centers including Argonne National Laboratory and Oak Ridge National Laboratory for high-resolution ensembles and convection-permitting runs.
Physics and Parameterizations
WRF contains numerous parameterization suites: microphysics options developed from research at Colorado State University and NCAR, long- and shortwave radiation schemes informed by work at NASA Goddard Space Flight Center and NOAA ESRL, planetary boundary layer schemes traced to studies by Penn State University and Rutgers University, and cumulus parameterizations used in operational centers like NCEP and ECMWF. Users select combinations drawn from community contributions and research groups such as CSU CHILL and Cooperative Institute for Research in the Atmosphere, enabling simulations of convective storms studied by NSSL, tropical cyclones monitored by National Hurricane Center, and winter systems analyzed by NOAA NESDIS.
Data Assimilation and Initialization
Data assimilation in WRF employs systems like the WRF Data Assimilation System (WRFDA) and external interfaces to variational and ensemble methods developed in collaboration with NCAR, EMC at NCEP, and research teams at University of Maryland. Assimilation ingests observations from platforms operated by NOAA, NASA, EUMETSAT, and national services such as Japan Meteorological Agency, Met Office, and China Meteorological Administration, including satellite retrievals from GOES, METEOSAT, and Himawari, radar data from networks managed by NEXRAD and profilers from ARM sites, plus surface and radiosonde networks run by WMO member services.
Applications and Operational Use
WRF is applied in forecasting by agencies including National Weather Service, Hydrometeorological Centre of Russia, Bureau of Meteorology, and research centers like NOAA ESRL for ensemble forecasting, emergency response simulations used by FEMA and military planning in USACE contexts, air quality coupled studies with EPA frameworks, wind-energy forecasting for firms and research at NREL, and climate downscaling projects within programs funded by IPCC contributors and regional initiatives at ICLEI and national laboratories.
Performance, Validation, and Limitations
WRF performance is evaluated in validation campaigns involving collaborations among NCAR, NSSL, ECMWF, MIT, Harvard University, and international observatories such as Svalbard and Mauna Loa, using verification frameworks from OPeNDAP communities and metrics adopted by WMO task teams. Limitations include sensitivity to parameterization choices documented in comparative studies by University of Utah, computational constraints noted by supercomputing centers like NERSC and XSEDE, and challenges in representing processes at unresolved scales emphasized in literature from AGU and AMS conferences; mitigation involves multi-model ensembles, targeted observations from field campaigns such as VORTEX and Hurricane Field Program collaborations, and continued development coordinated through stakeholder groups including UCAR and national meteorological services.