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Weather Research and Forecasting Model

The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting, supporting simulations across scales from tens of meters to thousands of kilometers using real or idealized initial conditions.^[1] It features the Advanced Research WRF (ARW) core as its primary dynamical core, which emphasizes higher-order accuracy and conservation properties; the Nonhydrostatic Mesoscale Model (NMM) core was previously available but is no longer supported by NCAR—along with a comprehensive suite of physics parameterizations for processes such as microphysics, planetary boundary layer, and land surface interactions, as well as an integrated data assimilation system.^[2] The latest version, WRF 4.5, was released in 2023, with ongoing updates enhancing its capabilities.^[1] Developed collaboratively by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Air Force, the Naval Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration, the model addresses limitations of earlier systems like the Fifth-Generation Mesoscale Model (MM5) and was publicly released in December 2000.^[2] WRF's software framework is built for parallel computing and extensibility, allowing users to customize configurations for diverse applications, including idealized studies, real-time forecasting, regional climate modeling, and coupled Earth system simulations.^[1] The preprocessing system, known as WPS, handles terrain data interpolation, domain configuration, and input preparation from various observational and global model datasets, facilitating seamless integration into operational workflows at centers like NOAA's National Centers for Environmental Prediction (NCEP).^[2] Since its inception in the late 1990s, WRF has fostered a large international community, with over 57,000 user registrations from more than 160 countries as of 2021 and more than 12,000 peer-reviewed publications as of 2025, underscoring its role in advancing meteorological science, education, and commercial applications.^[2]^[3]^[4] Ongoing development efforts focus on enhancing physics schemes, leveraging high-performance computing for higher-resolution simulations, and expanding applications to areas like air quality, renewable energy, and disaster response, ensuring WRF remains a cornerstone of modern atmospheric modeling.^[1] The model's open-source nature, supported by NCAR through workshops, documentation, and a user forum, promotes continuous community contributions and innovations.^[2]

Introduction

Overview and Purpose

The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction (NWP) system designed to support atmospheric simulations across a wide range of scales, from tens of meters to thousands of kilometers.^[1] This flexibility enables its application in diverse meteorological contexts, including high-resolution regional forecasts and broader-scale climate studies. As a community-supported framework, WRF integrates modular components that allow users to configure simulations for specific research or operational needs.^[5] The primary objectives of WRF are to advance atmospheric research through both idealized simulations and real-data case studies, while also facilitating operational forecasting for weather prediction services worldwide.^[2] It serves as a tool for investigating atmospheric processes, testing hypotheses, and improving forecast accuracy in operational environments, such as national weather centers. By providing open-source access to its codebase, WRF promotes collaboration among scientists and forecasters to refine modeling techniques.^[1] Initially released to the public in December 2000 as Version 1.0 through a collaborative effort involving the National Center for Atmospheric Research (NCAR) and partners, WRF has evolved into one of the most widely adopted NWP models globally.^[6] As of 2025, it boasts a large user community, with over 39,000 registered users across more than 160 countries, reflecting its broad impact on meteorological science and applications.^[7] The model features the Advanced Research WRF (ARW) as its primary dynamical core; the Nonhydrostatic Mesoscale Model (NMM) core was discontinued in version 4.4 (2022).^[8]

Development and Community

The development of the Weather Research and Forecasting (WRF) Model was initiated in the late 1990s as a collaborative effort among several key institutions, including the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA) through its National Centers for Environmental Prediction (NCEP) and Earth System Research Laboratory (ESRL), the U.S. Air Force, the Naval Research Laboratory (NRL), the University of Oklahoma, and the Federal Aviation Administration (FAA).^[9] This partnership aimed to create a next-generation mesoscale model that could serve both research and operational forecasting needs, building on prior systems like the Penn State/NCAR Mesoscale Model (MM5) and the Eta model.^[2] Leadership of the WRF project has been provided by NCAR's Mesoscale and Microscale Meteorology (MMM) Laboratory, which coordinates development, maintenance, and community engagement.^[1] Key milestones include the release of the first version (Version 1.0) in December 2000, which marked the model's debut as a freely available, open-source tool in the public domain to encourage widespread adoption and contributions.^[2] Subsequent enhancements, such as the addition of nesting capabilities in Version 2.0 (2004) and a global capability in Version 3.0 (2008), were supported by working groups from the original partners.^[9] The current version, 4.7, was released in April 2025.^[8] Annual WRF Users' Workshops, organized by NCAR and hosted in Boulder, Colorado, began in 2000 and have continued each year, fostering collaboration and knowledge sharing among developers and users.^[10] The WRF community has expanded significantly, with NCAR providing ongoing support through online forums, tutorials, and documentation to assist users in model implementation and customization.^[1] As of 2025, the registered user base exceeds 39,000 individuals across more than 160 countries, reflecting its global adoption in academic, governmental, and private sectors.^[7] The Developmental Testbed Center (DTC), a joint facility involving NCAR, NOAA, and the U.S. Air Force, plays a crucial role in rigorous testing and evaluation of WRF configurations, ensuring reliability for research and operational transitions, such as its integration into NOAA's Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) models.^[11]

Model Architecture

Dynamical Cores

The Weather Research and Forecasting (WRF) Model employs dynamical cores as the computational solvers responsible for advancing the atmospheric state through the integration of prognostic equations, enabling simulations of nonhydrostatic, compressible flows at various scales.^[2] These cores differ in their numerical frameworks, grid structures, and vertical coordinate systems, allowing flexibility for research and operational applications. The model originally supported two primary cores: the Advanced Research WRF (ARW) and the Nonhydrostatic Mesoscale Model (NMM).^[12] The ARW core, developed by the National Center for Atmospheric Research (NCAR), implements a fully compressible, nonhydrostatic formulation on an Arakawa C-grid, which staggers mass and wind variables for improved numerical stability and conservation properties.^[2] It utilizes a terrain-following, mass-based hybrid sigma-pressure vertical coordinate (default since version 3.9), facilitating high-resolution simulations with support for nested domains at refinement ratios of 3 or 5, two-way interactive nesting, and adaptive time-stepping options.^[12] This core's design emphasizes scalar conservation and high-order accuracy, making it suitable for a broad range of applications, including regional and global modeling, idealized cases, and integration with data assimilation systems like WRFDA.^[2] In contrast, the NMM core, derived from the Eta model and developed by the National Centers for Environmental Prediction (NCEP) with support from NOAA's Developmental Testbed Center (DTC), employs a nonhydrostatic solver on an Arakawa E-grid with a sigma-pressure hybrid vertical coordinate and rotated latitude-longitude projection.^[2] Optimized for operational mesoscale forecasting, it features step-mountain representation for terrain handling and was tailored for efficiency in high-resolution predictions, such as those in the Hurricane Weather Research and Forecasting (HWRF) system, though it offers less flexibility in domain configuration compared to ARW.^[12] Following the release of WRF version 4.4 in April 2022, the NMM core was removed from the main WRF distribution by NCAR (initially deprecated in version 4.0 in 2018), with a separate implementation continuing in specific operational contexts like HWRF; as of version 4.7 (April 2025), ARW is the sole actively maintained dynamical core for the broader WRF community.^[12]^[8]

Preprocessing and Postprocessing Systems

The Weather Research and Forecasting (WRF) model's preprocessing and postprocessing systems facilitate the preparation of input data and the analysis of simulation outputs, enabling accurate initialization and interpretation of results across various dynamical cores.^[13] The WRF Preprocessing System (WPS) serves as the primary tool for real-data simulations, handling the ingestion and interpolation of static and dynamic meteorological data to generate initial and boundary conditions compatible with the model's input requirements.^[13] WPS comprises three core programs: geogrid, ungrib, and metgrid. Geogrid defines the model domains by interpolating static geographical data, such as terrain elevation, land-use categories, and soil types, from predefined datasets at resolutions ranging from 30 arc seconds to 10 arc minutes onto user-specified grids.^[13] It uses a namelist.wps configuration file to specify domain parameters like projection, extent, and nesting ratios, producing intermediate files in the WRF I/O API format (e.g., NetCDF) that include fields like height above mean sea level.^[13] Ungrib extracts time-varying meteorological fields, such as temperature, humidity, and wind components, from GRIB Edition 1 or 2 formatted datasets sourced from global models like GFS or ECMWF, outputting them in an intermediate format (default WPS) after vertical interpolation to pressure levels.^[13] Metgrid then horizontally interpolates these meteorological fields onto the geogrid-defined domains, combining multiple data sources if needed and generating final input files for the WRF real.exe program, with options for boundary-only processing to enhance efficiency.^[13] Postprocessing in WRF is exemplified by the ARW postprocessor (ARWpost), which converts model output files into visualization-friendly formats. ARWpost reads WRF-ARW history and restart files, along with WPS-derived geogrid and metgrid data, primarily in NetCDF format, and computes diagnostic variables such as convective available potential energy (CAPE) and radar reflectivity.^[14] Configured via a namelist.ARWpost file, it produces GrADS-compatible files (.dat and .ctl) for analysis and plotting, with support for GRIB1 inputs though less extensively tested; Vis5D output was discontinued after version 3.0.^[14] Data assimilation enhances preprocessing by integrating observational data into the initial conditions, primarily through the WRF Data Assimilation (WRFDA) system. WRFDA supports three-dimensional variational (3D-Var) methods, which minimize a cost function using static background error covariances to assimilate observations like surface and radiosonde data into a 3D analysis.^[15] Four-dimensional variational (4D-Var) extends this by incorporating temporal evolution via the WRF forecast model over an assimilation window, enabling the use of asynchronous observations such as satellite radiances and radar data.^[15] Ensemble-based approaches, including hybrid ensemble-variational techniques like 3DEnVar and 4DEnVar, leverage flow-dependent error statistics from ensemble forecasts to improve analysis quality, with preprocessing tools like OBSPROC handling observation formatting in LITTLE_R and BUFR standards.^[15] These systems incorporate parallel computing capabilities to support scalability on high-performance computing platforms. Geogrid and metgrid in WPS utilize Message Passing Interface (MPI) for distributed-memory parallelism, allowing domain decomposition across multiple processors.^[13] WRFDA employs both MPI for inter-processor communication in variational minimizations and OpenMP for shared-memory threading within nodes, facilitating efficient handling of large datasets and ensemble generations.^[16] ARWpost, while primarily serial, can process outputs from parallel WRF runs without additional parallelization.^[14] This infrastructure ensures compatibility with the ARW dynamical core by adhering to the WRF I/O API standards.^[16]

Physics and Parameterizations

Microphysics and Cumulus Schemes

The Weather Research and Forecasting (WRF) model incorporates microphysics schemes to explicitly resolve cloud and precipitation processes within its grid, providing tendencies for heat, moisture, and non-convective rainfall rates. These schemes simulate the evolution of hydrometeors such as cloud water, rain, ice, snow, and graupel, often using bulk parameterization approaches that assume predefined size distributions for efficiency. Single-moment schemes predict only the mass mixing ratios of hydrometeor species, while double-moment schemes additionally predict number concentrations for improved representation of particle size variability and aerosol interactions.^[17] Key microphysics options in WRF include the Thompson scheme (mp_physics=8), a double-moment scheme that predicts mass and number concentrations for ice crystals and raindrops, with an aerosol-aware variant (mp_physics=28) incorporating prognostic aerosol effects for better simulation of cloud-aerosol interactions in polluted environments.^[18] The WSM6 scheme (mp_physics=6) is a single-moment, six-class ice scheme that predicts mixing ratios for cloud water, rain, cloud ice, snow, graupel, and vapor, offering computational efficiency suitable for operational forecasts while capturing mixed-phase processes. Another prominent option is the Lin et al. scheme (mp_physics=2), a Kessler-type single-moment scheme derived from the Purdue Lin cloud model, which includes detailed ice-phase processes and is particularly effective for high-resolution simulations of deep convection. These microphysics schemes rely on continuity equations for water vapor and hydrometeor species, typically expressed as \frac{\partial q_v}{\partial t} + \nabla \cdot (q_v \mathbf{v}) = P - L, where q_v is the water vapor mixing ratio, \mathbf{v} is the wind vector, and P and L represent production and loss terms from phase changes, respectively; advection and diffusion terms are handled by the dynamical core.^[17] Cumulus parameterization schemes in WRF address subgrid-scale convection by representing vertical fluxes of heat, moisture, and momentum that cannot be resolved explicitly, particularly for deep and shallow convective clouds, thereby influencing large-scale circulation and precipitation patterns. These schemes employ mass-flux approaches, where updrafts and downdrafts transport air parcels based on convective available potential energy (CAPE) triggers.^[17] Notable cumulus schemes include the Kain-Fritsch (cu_physics=1), a mass-flux scheme that triggers deep and shallow convection via CAPE removal, incorporates downdraft effects, and offers optional feedback to the cloud radiative scheme for improved diurnal cycle representation. The Grell-Freitas scheme (cu_physics=3) is a multi-scale, scale-aware parameterization designed to smoothly transition from coarse to cloud-resolving resolutions, using multiple closure assumptions to handle both meso- and synoptic-scale convection without abrupt spin-up issues. The Tiedtke scheme (cu_physics=6) employs a mass-flux framework with CAPE-based triggering for deep convection, includes organized entrainment for shallow clouds, and transports momentum to better simulate tropical disturbances. Selection of microphysics and cumulus schemes in WRF is resolution-dependent: cumulus parameterizations are generally required for grid spacings of 10 km or coarser to represent unresolved convection, but are typically disabled for grids finer than 3–4 km where explicit resolution of clouds is feasible, avoiding double-counting of convective precipitation.^[17] In the "gray zone" of 3–10 km resolutions, scale-aware schemes like Grell-Freitas or Multi-scale Kain-Fritsch are preferred to mitigate biases in rainfall forecasts.^[21] These choices significantly impact rainfall predictions, with double-moment microphysics often improving quantitative precipitation forecasts in convective events by better capturing hydrometeor diversity, while cumulus schemes like Kain-Fritsch enhance timing and intensity of organized storms.^[18] Microphysics and cumulus schemes interact with boundary layer parameterizations to modulate vertical mixing of moisture, influencing overall convective initiation.^[17]

Boundary Layer and Radiation Parameterizations

The planetary boundary layer (PBL) parameterizations in the Weather Research and Forecasting (WRF) model simulate vertical mixing processes near the Earth's surface, which are essential for accurately representing momentum, heat, and moisture fluxes in the atmosphere. These schemes address subgrid-scale turbulence that cannot be resolved by the model's dynamical core, influencing near-surface winds, temperature profiles, and pollutant dispersion. WRF offers several PBL options, each employing distinct approaches to closure assumptions for turbulent diffusion. The Yonsei University (YSU) scheme is a first-order nonlocal closure model that incorporates countergradient transport to account for convective mixing in unstable conditions and explicit entrainment processes at the PBL top. It uses a bulk Richardson number to diagnose the PBL height and applies eddy diffusivities based on a mixing length formulation, making it suitable for a wide range of synoptic conditions, including convective boundary layers. The YSU scheme has been widely adopted for operational forecasting due to its computational efficiency and performance in simulating low-level jets and daytime mixing.^[22] In contrast, the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme is a higher-order turbulent kinetic energy (TKE)-based parameterization that predicts TKE evolution and employs an eddy-diffusivity mass-flux approach for both vertical and horizontal mixing.^[23] This scheme resolves local gradients more explicitly through level-2.5 closure assumptions, improving simulations of stable boundary layers and nocturnal turbulence compared to simpler nonlocal schemes. MYNN is particularly effective in complex terrain and for coupling with urban canopy models, as it better captures anisotropic turbulence structures.^[24] The Bougeault-Lacarrère (BouLac) scheme provides a TKE-based local closure option, emphasizing a mixing length proportional to the distance between surface and inversion layers, which enhances representation of turbulence in stably stratified flows.^[25] Designed initially for mesoscale applications, it integrates well with building environment parameterization (BEP) for urban simulations, reducing biases in surface temperature and heat fluxes in built environments. BouLac's formulation avoids excessive vertical mixing in stable conditions, leading to more realistic nocturnal boundary layer depths.^[26] Land surface models in WRF provide the lower boundary conditions by simulating soil-vegetation-atmosphere interactions, including evapotranspiration, soil moisture dynamics, and surface fluxes that feed into PBL schemes. The Noah-Multiparameterization (Noah-MP) model extends the original Noah LSM with multiple options for key processes such as canopy interception, snow albedo, and groundwater flow, enabling flexible representation of vegetation and frozen soil effects. Noah-MP uses four soil layers and incorporates dynamic vegetation parameters, improving simulations of seasonal snow cover and latent heat fluxes in diverse ecosystems like forests and croplands. Its multi-physics framework allows users to select configurations for specific climates, enhancing accuracy in energy and water budget closure.^[27] The Rapid Update Cycle (RUC) land surface model emphasizes high-resolution soil moisture and temperature predictions with nine vertical soil layers, including a multilayer snow scheme for rapid updates in short-range forecasts.^[28] RUC accounts for fractional snow coverage and phase changes in soil water, which is critical for severe weather events involving frozen surfaces, and it couples directly with PBL schemes to provide realistic skin temperature and moisture fluxes. This model has been refined for operational use in NOAA's Rapid Refresh system, showing reduced biases in near-surface variables over arid and snowy regions.^[29] Radiation parameterizations in WRF compute solar and terrestrial radiative fluxes, influencing the model's energy budget through absorption, scattering, and emission processes. The Rapid Radiative Transfer Model for General Circulation Models (RRTMG) employs the correlated-k method to efficiently handle gaseous absorption in both shortwave and longwave spectra, supporting cloud overlap assumptions like maximum-random for realistic radiative transfer. RRTMG includes treatments for aerosols and trace gases, making it robust for climate and air quality simulations, and it typically calls every few model time steps to balance accuracy and computational cost. Its implementation in WRF has demonstrated superior performance in capturing diurnal temperature cycles and surface insolation compared to older schemes.^[30] The Community Atmosphere Model (CAM) radiation scheme, adapted from the CAM3 framework, integrates comprehensive longwave and shortwave parameterizations with diagnostic cloud properties derived from microphysics, emphasizing interactions with greenhouse gases and water vapor. CAM uses a two-stream approximation for radiative transfer and supports time-varying ozone profiles, which enhances simulations of stratospheric-tropospheric coupling and regional climate biases. It is favored for global-scale applications within WRF due to its consistency with broader earth system models.^[17] These parameterizations interact through the surface energy balance equation, which equates storage changes to net radiation minus turbulent fluxes and ground heat conduction: S = R - H - LE - G, where S is heat storage, R is net radiation, H is sensible heat flux, LE is latent heat flux, and G is ground heat flux.^[31] In Noah-MP and RUC, this balance is solved iteratively to determine skin temperature, providing boundary conditions for PBL and radiation schemes; a brief coupling with microphysics enables cloud-radiation feedbacks for improved precipitation and temperature forecasts.^[27] Ongoing updates to WRF physics schemes continue to expand options and refine performance. As of WRF version 4.7 (released April 2025), new microphysics schemes include the UFS Double Moment (UDM; mp_physics=27) for double-moment cloud and rain processes with advected cloud condensation nuclei, and RCON (mp_physics=29) for improved warm-rain representation in the Thompson scheme. Enhancements also feature updates to the MYNN eddy-diffusivity mass-flux (EDMF) PBL scheme for better efficiency and stability corrections in the k-epsilon PBL option, along with minor fixes to Noah-MP for soil and urban mapping.^[8]

Numerical Methods

Governing Equations

The Weather Research and Forecasting (WRF) model's Advanced Research WRF (ARW) core solves a set of fully compressible, non-hydrostatic equations that govern atmospheric dynamics. These equations are formulated in a terrain-following coordinate system to accommodate complex topography, using a hybrid sigma-pressure vertical coordinate denoted as η. The η coordinate transitions smoothly from terrain-following levels near the surface to pressure levels aloft, defined as p_d = B(\eta)(p_s - p_t) + [\eta - B(\eta)](p_0 - p_t) + p_t, where p_d is the dry-air pressure perturbation, p_s is surface pressure, p_t is top pressure, p_0 is a reference pressure, and B(\eta) is a monotonic function ensuring the transition. This mass-based coordinate employs \mu_d = \partial p_d / \partial \eta as the prognostic variable for dry-air mass per unit area, enabling flux-form conservation properties.^[9] The core prognostic equations are expressed in flux form on an Arakawa C-grid in Cartesian or curvilinear horizontal coordinates, incorporating map-scale factors m_x and m_y for projections. The prognostic horizontal momentum variables are defined as U = \frac{\mu_d u}{m_y} and V = \frac{\mu_d v}{m_x}, where u and v are the velocity components in map coordinates (related to physical velocities via rotation by angle \alpha between true north and grid y-axis). The horizontal momentum equations for these variables are:

\frac{\partial U}{\partial t} + m_x \left[ \frac{\partial (U u)}{\partial x} + \frac{\partial (V u)}{\partial y} \right] + \frac{\partial (\Omega u)}{\partial \eta} + \frac{m_x}{m_y} \left[ \mu_d \alpha \frac{\partial p}{\partial x} + \frac{\alpha}{\alpha_d} \frac{\partial p}{\partial \eta} \frac{\partial \phi}{\partial x} \right] = F_U

\frac{\partial V}{\partial t} + m_y \left[ \frac{\partial (U v)}{\partial x} + \frac{\partial (V v)}{\partial y} \right] + \frac{m_y}{m_x} \frac{\partial (\Omega v)}{\partial \eta} + \frac{m_y}{m_x} \left[ \mu_d \alpha \frac{\partial p}{\partial y} + \frac{\alpha}{\alpha_d} \frac{\partial p}{\partial \eta} \frac{\partial \phi}{\partial y} \right] = F_V

Here, \Omega is the vertical velocity in η-coordinates, p is perturbation pressure, \phi = gz is geopotential height, \alpha = 1/\rho is specific volume, \alpha_d is the dry-air specific volume, and F_U, F_V include Coriolis, diffusion, and forcing terms. The vertical momentum equation for the physical vertical velocity W is:

\frac{\partial W}{\partial t} + \mathbf{U} \cdot \nabla W = -\frac{\partial \phi}{\partial z} - g \frac{\alpha - \alpha_d}{\alpha_d} + F_W

where \mathbf{U} = (u, v, w) is the three-dimensional velocity vector and F_W encompasses additional terms. The thermodynamic equation advances the moist potential temperature \theta_m:

\frac{\partial \Theta_m}{\partial t} + m_x m_y \left[ \frac{\partial (U \theta_m)}{\partial x} + \frac{\partial (V \theta_m)}{\partial y} \right] + m_y \frac{\partial (\Omega \theta_m)}{\partial \eta} = F_{\Theta_m}

with \Theta_m = (\mu_d + \mu_w) \theta_m as the flux form, and F_{\Theta_m} representing heating sources. The continuity equation for total density (dry plus water) is:

\frac{\partial \mu_d}{\partial t} + m_x m_y \left[ \frac{\partial U}{\partial x} + \frac{\partial V}{\partial y} \right] + m_y \frac{\partial \Omega}{\partial \eta} = 0

where \mu_w accounts for water mass. For water vapor, the equation for the mixing ratio q_v (or total moisture Q_m) is:

\frac{\partial Q_m}{\partial t} + m_x m_y \left[ \frac{\partial (U q_m)}{\partial x} + \frac{\partial (V q_m)}{\partial y} \right] + m_y \frac{\partial (\Omega q_m)}{\partial \eta} = F_{Q_m}

with Q_m = (\mu_d + \mu_w) q_m in flux form, and F_{Q_m} including phase change and advection sources. These equations close with the equation of state for an ideal gas: p = p_d + p_h = \rho R_d T_v, where T_v is virtual temperature, R_d is the dry-air gas constant, and p_h is hydrostatic base-state pressure; an exner function form \Pi = (p / p_0)^\kappa (with \kappa = R_d / c_p) is often used for thermodynamic variables.^[9]^[32] An optional hydrostatic approximation simplifies the vertical momentum equation by assuming \partial p / \partial z = -\rho g, neglecting vertical acceleration terms, which reduces computational cost for large-scale simulations while maintaining non-hydrostatic horizontal dynamics. This formulation ensures conservation of mass and scalars in flux form, with the full set solved by the ARW dynamical core.^[9]

Discretization and Time Integration

The Weather Research and Forecasting (WRF) Model employs distinct numerical methods for discretizing its governing equations on a computational grid and advancing the solution in time, tailored to its dynamical cores. In the Advanced Research WRF (ARW) core, horizontal discretization utilizes an Arakawa C-grid staggering, where prognostic variables such as velocity components (u and v) are positioned at the faces of grid cells, while scalar variables like potential temperature and pressure are centered within the cells.^[9] This staggered arrangement enhances the representation of divergent flows and reduces computational modes, with finite-difference approximations applied using 2nd- to 6th-order schemes for advection terms.^[9] The Nonhydrostatic Mesoscale Model (NMM) core (deprecated in WRF v4.4, 2022), in contrast, adopted an E-grid structure with co-located variables, which favored the simulation of rotational modes but required adjustments for divergent physics.^[8]^[9] Vertically, the ARW core implements a terrain-following hybrid sigma-pressure coordinate system, denoted as η, which transitions smoothly from sigma levels near the surface to pressure levels aloft, with the model top fixed at a constant pressure surface.^[33] This coordinate follows the terrain to resolve boundary layer processes accurately while minimizing distortions in the free atmosphere; however, to mitigate numerical errors from steeply sloping surfaces—such as Gibbs oscillations or artificial vertical velocities—smoothing techniques are applied during coordinate definition and grid generation.^[34] Vertical discretization uses finite differences on mass points, supporting stretched grids for enhanced resolution near the surface.^[9] As of WRF v4.4 (2022), the NMM core was removed, with ongoing developments enhancing the ARW core's numerical methods for higher resolutions and efficiency.^[8] Time integration in the ARW core relies on a third-order Runge-Kutta scheme, which advances prognostic variables in three sub-steps per large time step, providing low numerical damping for meteorological modes while treating acoustic waves semi-implicitly.^[9] This approach splits the integration into smaller time steps for fast-propagating acoustic and gravity waves, using a forward-backward method horizontally and an implicit scheme vertically to maintain stability.^[9] The NMM core (deprecated since 2022) employed a leapfrog scheme with a Robert-Asselin time filter to suppress computational modes, combined with second-order Adams-Bashforth steps for horizontal advection and Crank-Nicolson for vertical terms, though without explicit splitting for gravity waves.^[8]^[35] Diffusion and slower advection terms are integrated over the larger time step in the ARW core to balance computational efficiency and accuracy.^[36] Model stability is enforced through the Courant-Friedrichs-Lewy (CFL) condition, which restricts the time step Δt to satisfy Δt < Δx / |u|{max} for horizontal advection, where Δx is the grid spacing and |u|{max} is the maximum wind speed, ensuring that information does not propagate across more than one grid cell per step.^[36] For acoustic modes in the ARW, a stricter acoustic CFL applies to the small time step Δτ < Δx / (2 c_s), with c_s the speed of sound, typically limiting Δτ to around 17 seconds for a 10 km grid.^[9] Adaptive time-stepping options adjust Δt dynamically based on domain-wide CFL criteria, preventing instabilities from evolving winds or vertical motions.^[36]

Applications

Operational Forecasting

The Weather Research and Forecasting (WRF) Model forms the foundation for several operational numerical weather prediction systems at the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP), enabling routine high-resolution forecasts to support weather services across North America.^[37] These systems leverage WRF's flexibility in data assimilation and physics parameterizations to produce timely guidance for severe weather, aviation, and public safety applications.^[38] The Rapid Refresh (RAP) model, operational since May 2012, utilizes the WRF Advanced Research core (WRF-ARW) at a 13 km horizontal resolution over the North American domain, with hourly updates to provide short-range forecasts out to 18 hours.^[37] It incorporates frequent assimilation of radar and surface observations to initialize cycles, enhancing predictions of rapidly evolving weather patterns such as thunderstorms and fronts.^[39] Building on the RAP framework, the High-Resolution Rapid Refresh (HRRR) model operates at 3 km resolution since September 2014, delivering convection-allowing forecasts over the contiguous United States with hourly cycling and sub-hourly radar updates every 15 minutes.^[40] The HRRR, also based on WRF-ARW, excels in resolving mesoscale features like supercells and heavy precipitation, supporting nowcasting and short-term outlooks up to 48 hours.^[41] The North American Mesoscale (NAM) Forecast System employs the WRF nonhydrostatic mesoscale core at 12 km resolution, with nested 3 km grids for high-resolution regional domains, and runs four times daily for forecasts extending to 84 hours.^[42] Operational as a key component of NCEP's suite, the NAM provides detailed guidance for mid-latitude weather systems, including winter storms and temperature extremes.^[43] In tropical cyclone forecasting, the Hurricane Weather Research and Forecasting (HWRF) model was operational at NCEP from the 2007 Atlantic hurricane season until October 2025, using a WRF-based configuration to predict storm tracks and intensity up to 126 hours in advance.^[44] The HWRF integrated vortex initialization and ensemble capabilities to improve accuracy for wind speeds, rainfall, and surge hazards associated with hurricanes.^[45] It was replaced by the Hurricane Analysis and Forecast System (HAFS), which uses the FV3 dynamical core.^[46] These WRF-driven operational models demand substantial computing power and are executed on NOAA's Jet supercomputer, which provides over 55,000 CPU cores for parallel processing of high-resolution grids and assimilation cycles.^[47] To address uncertainty, ensemble configurations are routinely generated, such as the NSSL-WRF ensemble with multiple members run on Jet, yielding probabilistic forecasts that quantify spread in track and intensity predictions.^[48] The WRF Preprocessing System (WPS) facilitates real-time ingestion of meteorological data for initializing these production runs.^[38]

Research and Specialized Simulations

The Weather Research and Forecasting (WRF) model is extensively utilized in idealized simulations to investigate fundamental atmospheric processes without reliance on real observational data, enabling controlled experiments on phenomena such as terrain-induced flows, hurricane dynamics, and convection initiation. For instance, idealized terrain simulations, including mountain wave cases, allow researchers to examine orographic influences on airflow and precipitation using analytically defined initial conditions and terrain-following coordinates. Similarly, idealized tropical cyclone setups simulate hurricane vortex evolution and intensification, often incorporating sea breeze or gravity current initializations to study asymmetric convection and eyewall dynamics. Convection initiation studies employ squall line or supercell test cases to explore organized thunderstorm development, with grid spacings as fine as 100 meters for large-eddy simulations (LES) to resolve turbulent structures. These configurations, supported by the WRF's nonhydrostatic core and hydrostatic balance options, facilitate sensitivity analyses of physics parameterizations in isolated environments.^[9] In real-data research, WRF drives investigations into complex environmental interactions, such as severe weather events, air quality dynamics, and climate downscaling, leveraging its high-resolution capabilities and integration with observational datasets. Severe weather studies often focus on supercell thunderstorms or extreme precipitation cases, where WRF assimilates radar data to model convective evolution and validate microphysics schemes during events like the 2016 UAE cyclone. Air quality research employs WRF to simulate pollutant transport and dispersion, particularly through scalar advection for chemistry constituents, as seen in multi-year simulations assessing urban emissions under varying meteorological conditions. Climate downscaling applications use WRF to refine global model outputs for regional scales, such as over the Great Plains, where nested grids improve precipitation forecasts by incorporating terrain and land-use effects from datasets like the National Centers for Environmental Prediction (NCEP). These efforts highlight WRF's role in bridging coarse-resolution climate projections with localized impacts, emphasizing dynamical processes over statistical methods.^[9]^[49]^[50] WRF supports specialized field campaigns by integrating model outputs with in-situ and remote sensing observations, enhancing process-level understanding of targeted weather phenomena. A prominent example is the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign, conducted from 2020 to 2023, which used WRF to simulate East Coast winter cyclones and snowbands, assimilating aircraft, radar, and satellite data to study snowstorm microphysics and dynamics during events like the February 7, 2020, New York snowstorm. This integration allows for high-resolution hindcasts that validate model physics against multi-scale observations, improving representations of precipitation structures in mid-latitude storms.^[51]^[52] Beyond operational contexts, WRF serves as a cornerstone for academic and non-operational research, with thousands of peer-reviewed publications demonstrating its versatility as a tool for hypothesis testing and parameterization development. Researchers frequently employ WRF for sensitivity experiments on physics options, such as varying cumulus or boundary layer schemes to assess their impacts on simulated convection or radiative transfer, often in idealized or real-case frameworks to isolate scheme interactions. Community workshops facilitate the dissemination of these findings, promoting collaborative advancements in model applications.^[9]

Variants and Extensions

Hurricane and Polar Variants

The Hurricane Weather Research and Forecasting (HWRF) model, a specialized variant of the WRF, was developed by NOAA's Atlantic Oceanographic and Meteorological Laboratory and became operational at the National Centers for Environmental Prediction in 2007.^[53]^[45] It builds on the Advanced Research WRF (ARW) dynamical core with targeted adaptations for tropical cyclone simulation.^[53] Key features include vortex initialization to accurately represent the initial storm structure using observational data assimilation and storm-following moving nests that dynamically track the hurricane's path while maintaining high resolution in the inner core.^[53] These nests enable a multi-level grid system, with configurations evolving from outer domains of 18-27 km to 13.5 km resolution and inner nests refining to 2 km or finer, reaching 1.5 km by 2019, allowing detailed capture of eyewall dynamics and rapid intensification processes.^[53] HWRF has been used operationally by the National Hurricane Center for track and intensity forecasts extending up to 126 hours, supporting real-time guidance during the Atlantic and eastern Pacific hurricane seasons until its retirement in 2022 in favor of the Hurricane Analysis and Forecast System.^[53] The Polar WRF variant, released in 2009 by the Polar Meteorology Group at Ohio State University's Byrd Polar and Climate Research Center (with initial public availability in 2008), addresses the unique challenges of high-latitude environments through modifications to the standard WRF framework.^[54]^[55] Based on WRF version 3.1.1, it incorporates polar-optimized parameterizations, including enhancements to the Noah land surface model for improved treatment of sea ice thermodynamics, such as variable sea ice fraction, thickness, snow depth, and albedo to better simulate surface energy balance and heat transfer over ice-covered regions.^[54]^[55] Radiation schemes are adapted for the polar night and midnight sun conditions, with adjustments to longwave and shortwave processes to handle extreme solar angles and persistent low temperatures.^[56] These changes enhance model stability in cold climates by reducing common cold biases in near-surface temperatures and improving simulations of boundary layer processes over sea ice and ice sheets.^[54] Polar WRF has been widely applied in Arctic and Antarctic research, powering systems like the Antarctic Mesoscale Prediction System for daily forecasts at resolutions from 30 km down to sub-kilometer scales, with ongoing updates including version 4.5.1 released in September 2023.^[54] Both variants leverage WRF's flexible nesting capabilities for high-resolution enhancements, such as inner nests at 1-2 km to resolve fine-scale features like hurricane eyewalls or polar cyclones, which improve the representation of convective structures without excessive computational cost.^[53] In terms of performance, HWRF demonstrated notable improvements in the 2010s, achieving average track error reductions of about 20% compared to predecessor models like GFDL at various lead times, particularly through better vortex initialization and nest motion.^[53]^[57]

Coupled Model Systems

The Weather Research and Forecasting (WRF) model supports coupled systems that integrate atmospheric dynamics with other physical processes, enabling multi-physics simulations for enhanced forecasting of interconnected environmental phenomena. These couplings allow for two-way interactions, where meteorological fields influence and are influenced by additional components such as atmospheric chemistry, hydrology, and surface processes. This modularity facilitates applications in air quality, water resource management, and hazard prediction, with ongoing developments improving computational efficiency and process representation.^[9] WRF-Chem represents a key coupled system that integrates WRF's meteorological framework with atmospheric chemistry modules to simulate air quality and pollutant transport. It enables online, two-way interactions between chemistry and meteorology, including effects on radiation budgets and aerosol-cloud processes. Common configurations incorporate gas-phase chemistry schemes like the Carbon Bond Mechanism Z (CBM-Z), which handles 73 species and 237 reactions, coupled with aerosol modules such as MOSAIC for simulating interactions among sulfate, nitrate, ammonium, organic carbon, black carbon, dust, and sea salt.^[58] The CBM-Z/MOSAIC combination (chem_opt=8) is widely adopted for its balance of computational efficiency and accuracy in reconstructing pollutant concentrations and optical depths. WRF-Chem is extensively used for pollution forecasting, such as ozone and particulate matter predictions, and has been applied in studies of climate-chemistry interactions, including aerosol radiative forcing and feedback on regional climate variability.^[5]^[58] WRF-Hydro extends WRF by coupling it with hydrological models to simulate terrestrial water cycle components, addressing flood prediction and land surface interactions. It builds on the Noah-MP land surface model for processes like soil moisture dynamics and evapotranspiration, incorporating routing schemes for overland flow, channel routing, and subsurface exchanges to represent watershed-scale hydrology.^[59] This integration supports multi-scale simulations of precipitation-runoff responses and land-atmosphere feedbacks, forming the core of operational systems like the NOAA National Water Model for streamflow forecasting.^[59] Applications include flash flood guidance, seasonal water resource assessments, and hydroclimate impact studies, with the system's parallelized architecture enabling high-resolution runs on diverse terrains.^[59] Extensions within the WRF framework, often referred to as WRF+ variants, incorporate specialized modules for urban and fire-related processes to enhance coupled simulations in complex environments. The urban canopy parameterization, including single-layer (SLUCM) and multi-layer (BEP/BEM) schemes, models heat, momentum, and moisture exchanges in built environments, capturing effects like urban heat islands on local weather.^[9] For fire weather, the WRF-Fire module provides two-way coupling between atmospheric conditions and wildland fire spread, simulating fuel consumption, heat release, and plume dynamics to predict fire behavior and smoke dispersion.^[60] These extensions are applied in urban climate studies and wildfire risk assessments, integrating with core WRF physics for realistic hazard forecasting.^[60] Advancements in WRF version 4.5, released in April 2023, include improved support for building coupled systems via CMake, streamlining compilation for components like WRF-Chem and WRF-Hydro on modern high-performance computing platforms, with further updates in versions up to 4.7.1 as of June 2025.^[61]^[62] This version updates the hydro interface to align with WRF-Hydro v5.3.0 and Noah-MP v4.5, enhancing interoperability for coupled runs.^[62] Such developments support broader applications, including research campaigns that leverage these systems for integrated environmental monitoring.^[5]

Evaluation and Limitations

Validation and Performance

The accuracy of the Weather Research and Forecasting (WRF) model is evaluated using standard meteorological verification metrics, including root mean square error (RMSE) for surface temperature and precipitation forecasts, bias scores to assess systematic over- or under-predictions, and equitable threat scores (ETS) for categorical events such as heavy rainfall or severe storms.^[63] For instance, in simulations over complex terrain, WRF's 2-m temperature RMSE typically ranges from 1.5 to 2.5°C for 24-hour forecasts, while precipitation bias often shows slight overestimation in convective regimes, with ETS values exceeding 0.4 for moderate rainfall thresholds in mid-latitude cases.^[64] These metrics are computed against observational networks like surface stations and radar data to quantify forecast skill across lead times.^[65] Annual WRF Users' Workshops, hosted by the National Center for Atmospheric Research (NCAR), feature dedicated sessions on model evaluation where successive versions are tested against real-world cases, including idealized benchmarks and operational hindcasts.^[66] For example, version 4.0, released in 2018, incorporated enhanced scale-aware cumulus parameterizations such as the Multi-scale Kain-Fritsch scheme, leading to improved representation of convective processes in high-resolution simulations compared to prior versions.^[9] These evaluations often highlight gains in precipitation timing and intensity, with workshop presentations demonstrating reduced errors in mesoscale convective systems through updated physics suites.^[67] Comparisons with predecessor models show WRF outperforming the Fifth-Generation Mesoscale Model (MM5) in key areas, such as 36-km resolution forecasts of wind and precipitation, where WRF exhibits lower RMSE and higher skill scores due to advanced nonhydrostatic dynamics and physics options.^[68] Against global models like the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System, WRF benchmarks favorably in mid-latitude regions for short-range precipitation, achieving superior spatial detail and reduced bias in convective events within nested domains.^[69] WRF's global verifications confirm its adoption by a large international community exceeding 39,000 registered users across over 160 countries as of 2025, supporting operational and research applications worldwide. The model demonstrates high skill in mesoscale forecasts spanning 1 to 48 hours, particularly for regional weather phenomena, with equitable skill scores often surpassing 0.5 for temperature and wind predictions in real-time evaluations.^[70]

Known Challenges and Improvements

The Weather Research and Forecasting (WRF) model has been noted for its tendency to overpredict the intensity and duration of convective systems, such as squall lines, particularly in simulations of mesoscale convective systems where trailing stratiform regions are underrepresented or precipitation events persist longer than observed.^[71]^[72] This issue arises from challenges in representing microphysical processes and convective triggering, leading to biases in rainfall forecasts. Additionally, the model's outputs exhibit high sensitivity to the selection of physics parameterizations, including cumulus, microphysics, and planetary boundary layer schemes, which can significantly alter simulated dynamics and precipitation patterns across different environmental conditions.^[73]^[74]^[75] High-resolution configurations, essential for capturing fine-scale features, incur substantial computational costs due to the need for extensive grid nesting and parallel processing, limiting operational feasibility over large domains.^[76]^[77] In regions with complex terrain, such as mountainous areas, the standard WRF configuration often struggles with accurate representation of orographic influences on airflow and precipitation, resulting in errors in surface winds and boundary layer processes unless specialized variants are employed.^[78] Similarly, polar simulations without dedicated variants like Polar WRF exhibit limitations in handling stable boundary layers and ice-related physics, leading to biases in temperature and cloud cover over high-latitude environments.^[79]^[80] Data assimilation in WRF also faces gaps in regions with sparse observations, such as oceanic or remote land areas, where the lack of in-situ measurements hinders effective initialization and error correction through techniques like 3DVAR or EnKF.^[81] To address these issues, WRF version 4.5, released in April 2023, introduced enhanced compiler support for architectures like ARM64 and AMD AOCC, improving portability and performance on diverse high-performance computing platforms.^[62] It also incorporated improved testing capabilities in the configure system, facilitating more robust validation of model builds and physics options. Subsequent releases, including version 4.7.1 in June 2025, have added performance enhancements such as refactored schemes for 10-15% speed-ups in boundary layer simulations.^[82] Research as of 2025 has explored AI-hybrid approaches for postprocessing WRF outputs, including machine learning-based bias correction for variables like precipitation and near-surface temperatures in data-sparse scenarios.^[83]^[84] Looking ahead, efforts are underway to integrate WRF physics schemes into NOAA's Unified Forecast System (UFS), aiming to leverage WRF's mesoscale strengths for improved short-range predictions within a coupled Earth system framework. The WRF community is also advancing machine learning-based parameterizations, such as neural network emulators for cumulus convection and subgrid processes, to reduce sensitivity to traditional schemes and enable more stable long-term simulations.^[85]^[86]^[87]^[88]

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[PDF] City-Scale High-Resolution WRF-UCM Urban Weather Predictions ...
Due to high computational costs, it is not feasible to apply the WRF model with fine horizontal grid resolution on a large temporal and spatial scale. Therefore ...
[75]
Efficient Super-Resolution of Near-Surface Climate Modeling Using ...
Jul 27, 2023 · We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables ...
[76]
Evaluation of the WRF-UCM mesoscale model and ECMWF global ...
Oct 11, 2018 · Complex urban terrains significantly modify surface roughness, augment surface energy budgets, and create heat islands, all of which lead to ...Missing: polar | Show results with:polar
[77]
[PDF] Polar WRF V4.1.1 simulation and evaluation for the Antarctic and ...
Previous versions of the model, known as. “Polar WRF” (referred to as PWRF), have been used and evaluated in both the Arctic and the Antarctic. It is confirmed ...Missing: stability | Show results with:stability
[78]
[PDF] Evaluation of Polar WRF forecasts on the Arctic System Reanalysis ...
Nudging the model's boundary layer moisture toward drier conditions decreases convective precipitation improving the prediction. Cloud fraction analysis shows ...
[79]
[PDF] Ensemble Kalman filter assimilation of near-surface observations ...
Using the advanced research version of the Weather Research and Forecasting (WRF) model, results from the assimilation of observations at a single observation ...
[80]
[PDF] UIFCW GUIDE 2024 - Earth Prediction Innovation Center
Jul 22, 2024 · Weather Research and Forecasting model (WRF) and its hydrological ... hybrid ML-parametric post-processing strategy that uses a ...
[81]
AI Improves the Accuracy, Reliability, and Economic Value of ...
Jun 19, 2025 · A novel hybrid model boosts forecast accuracy 4–6 times for flood predictions compared to the National Water Model (NWM) The hybrid model ...
[82]
[PDF] An Overview of Physical Parameterization Development for the ...
2021 WRF/MPAS Workshop. Page 2. Outline. Goals: 1. Overview the primary model physics development in support of NOAA's Unified. Forecast System (UFS). 2 ...
[83]
1.1. - UFS Short-Range Weather App User's Guide (v2.2.0)
The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. NOAA's operational model suite for numerical weather ...
[84]
Machine learning parameterization of the multi-scale Kain ... - GMD
May 7, 2024 · This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme.
[85]
WRF–ML v1.0: a bridge between WRF v4.3 and machine learning ...
Jan 6, 2023 · The WRF model is selected as it is a popular open source NWP model used by many researchers and operational organizations. ... The ML-based ...Missing: transition | Show results with:transition<|separator|>