SYSTEM DESCRIPTION

WRF is a numerical weather prediction (NWP) and atmospheric simulation system designed for both research and operational applications. WRF is supported as a common tool for the university/research and operational communities by NCAR. The development of WRF has been a multi-agency effort among the National Center for Atmospheric Research’s (NCAR) Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Prediction (NCEP) and Earth System Research Laboratory (ESRL), the Department of Defense’s Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (FAA), with the participation of university scientists. WRF is maintained and supported as a community model to facilitate wide use internationally, for research, operations and teaching.

The key features of the WRF –ARW model include:

• Equations: Fully compressible, Euler nonhydrostatic. Conservative for scalar variables.

• Prognostic Variables: Velocity components u and v in Cartesian coordinate, vertical velocity w, perturbation potential temperature, perturbation geopotential, and perturbation surface pressure of dry air.

• Vertical Coordinate: Terrain-following, dry hydrostatic-pressure, with vertical grid stretching permitted. Top of the model is a constant pressure surface.

• Horizontal Grid: Arakawa C-grid staggering.

• Time Integration: Time-split integration using a 2nd- or 3rd-order Runge-Kutta scheme with smaller time step for acoustic and gravity-wave modes. Variable time step capability.

• Spatial Discretization: 2nd- to 6th-order advection options in horizontal and vertical.

• Top Boundary Conditions: Gravity wave absorbing (diffusion, Rayleigh damping, or implicit Rayleigh damping for vertical velocity). Constant pressure level at top boundary along a material surface. Rigid lid option.

• Bottom Boundary Conditions: Physical or free-slip.

• Earth’s Rotation: Full Coriolis terms included.

• Mapping to Sphere: Four map projections are supported for real-data simulation: polar stereographic, Lambert conformal, Mercator, and latitude-longitude (allowing rotated pole). Curvature terms included.

• Nesting: One-way interactive, two-way interactive, and moving nests. Multiple levels and integer ratios.

• Nudging: Grid (analysis) and observation nudging capabilities available.

Model physics:

• Microphysics: Schemes ranging from simplified physics suitable for idealized studies to sophisticated mixed-phase physics suitable for process studies and NWP.

• Cumulus parameterizations: Adjustment and mass-flux schemes for mesoscale modeling.

• Surface physics: Multi-layer land surface models ranging from a simple thermal model to full vegetation and soil moisture models, including snow cover and sea ice.

• Planetary boundary layer physics: Turbulent kinetic energy prediction or non-local scheme

• Atmospheric radiation physics: Longwave and shortwave schemes with multiple spectral bands and a simple shortwave scheme suitable for climate and weather applications. Cloud effects and surface fluxes are included.

The WRF system is summarized in the following figure:

ROMS is a free-surface, terrain-following numerical model that solves the three-dimensional Reynolds Averaged Navier-Stokes equations (RANS) using the hydrostatic and Boussinesq approximations. ROMS uses finite difference approximations on a horizontal curvilinear Arakawa C grid and on a vertical stretched terrain-following coordinate. Momentum and scalar advection and diffusive processes are solved using transport equations. An equation of state computes the density field that accounts for temperature, salinity and suspended sediment contributions.

ROMS provides a flexible structure that allows multiple choices for many of the model components, such as several options for advection schemes, turbulence models, lateral boundary conditions, bottom and surface boundary layer sub-models, air-sea fluxes, surface drifters, nutrient-phytoplankton-zooplankton-detritus models and an adjoint module to compute model inverses and data assimilation.

The hydrostatic primitive equations for momentum are solved using a split-explicit time stepping scheme which requires special treatement and coupling between barotropic (fast) and baroclinic (slow) modes. A finite number of barotropic time steps, within each baroclinic time step is carried out to evolve the free-surface and vertically integrated momentum equations. In order to avoid the errors associated with the aliasing of frequencies resolved by the barotropic steps, but unresolved by the baroclinic step, the barotropic fields are time averaged before they replace those values obtained with a longer baroclinic step.

SWAN is a third generation spectral wave model specifically designed for shallow water that solves the spectral density evolution equation. SWAN simulates wind wave generation and propagation in coastal waters and includes the processes of refraction, diffraction, shoaling, wave-wave interactions and dissipation due to whitecapping, wave breaking and bottom friction.

The wave model solves the action balance equation:

where is the action density spectrum, σ is the relative radiant frequency (as observed in a frame moving with the ocean current), θ is direction normal to wave crest, x and y are coordinate space and t is time. The action density is defined as the wave energy density E divided by the relative frequency and is solved because the action density is conserved in the presence of currents. The group velocities in x and y directions c_x and c_y in the second and third terms represent the propagation of the action density in geographic space, the fourth term represents changes in relative frequency due to variations in depth and currents with a propagation speed c_σ in frequency space and the fifth term allows depth and current-induced refraction with a speed c_θ in directional space. The S_w term represents sources and sinks of wave energy density.

The MCT coupler allows the transmission and transformation of various distributed data between component models using a parallel coupled approach. MCT is a program written in Fortran90 and works with the MPI communication protocol. It is compiled as a set of libraries, which are linked to the model executable.

At the initialization phase each model decomposes its own domain into sections (or segments) that are distributed to processors assigned for that component. Each grid section on each processors initializes into MCT and the coupler compiles a global map to determine the distribution of model segments. Each segment also initializes an attribute vector that contains the fields to be exchanged and establishes a router to provide an exchange pathway between model components.

During the run phase of the simulation the models will reach a predetermined syncronization point, fill the attribute vectors with data and use MCT send and receive commands to exchange fields.