Weather Research and Forecast (WRF) Model

1. Weather Research and Forecast (WRF) Model Ü Develop an advanced mesoscale forecast and assimilation system Ü Promote closer ties between research and operations Research: Design for 1-10 km horizontal grids Advanced data assimilation and model physics Accurate and efficient across a broad range of scales Well-suited for both research and operations Community model support

2. WRF Project Collaborators • Original Partners: • NCAR Mesoscale and Microscale Meteorology Division • NOAA National Centers for Environmental Prediction • NOAA Forecast Systems Laboratory • OU Center for the Analysis and Prediction of Storms • Additional Collaborators: • Air Force Weather Agency • NOAA Geophysical Fluid Dynamics Laboratory • NASA GSFC Atmospheric Sciences Division • NOAA National Severe Storms Laboratory • NRL Marine Meteorology Division • EPA Atmospheric Modeling Division • University Community

3. WRFProject Management Steve Lord, Chair NOAA/NCEP Sandy MacDonald FSL &GFDL Bob Gall NCAR/MMM Steve Nelson NSF/ATM Col. Charles French USAF/AFWA WRF Oversight Board Joe Klemp NCAR/MMM WRF Coordinator WRF Science Board WRF Development Teams (5)

4. WRF Development Teams

5. WRF Software Objectives • Performance-Portable • Performance: scaling and time to solution • Architecture independence • No specification of external packages • Run-Time Configurable • Scenarios, domain sizes, nest configurations • Dynamical-core and physics • Maintainability & Extensibility • Single source code • Modular, hierarchical design, coding standards • Plug compatible physics, dynamical cores

6. Single version of code enabled for efficient execution on: Distributed-memory multiprocessors Shared-memory multiprocessors Distributed memory clusters of SMPs Logical domain 1 Patch, divided into multiple tiles Inter-processor communication WRF Multi-Layer Domain Decomposition • Model domains are decomposed for parallelism on two-levels • Patch: section of model domain allocated to a distributed memory node • Tile: section of a patch allocated to a shared-memory processor within a node • Distributed memory parallelism is over patches; shared memory parallelism is over tiles within patches

7. Top-level “Driver” layer Isolates computer architecture concerns Manages execution over multiple nested domains Provides top level control over parallelism patch-decomposition inter-processor communication shared-memory parallelism Controls Input/Output “Mediation” Layer Specific calls to parallel mechanisms Low-Level “Model” layer Performs actual model computations Tile-callable Scientists insulated from parallelism General, fully reusable Mediation Layer uv prep filter scalars physics big_step decouple recouple advance w advance Model Layer WRF Hierarchical Software Architecture Driver Layer wrf initial_ config alloc _and_configure init _domain integrate solve_interface solve

8. Parallel Scaling on Compaq Computer

9. Alpha workstation (EV56) 30 25 20 15 10 5 81 41 Y tile dimension 0 21 21 41 81 X tile dimension VPP 5000 100 80 60 40 20 0 -20 -40 81 -60 41 Y tile -80 dimension 21 21 41 81 X tile dimension Penalty for IJK Loop Order • IJK versus KIJ for all patch dimensions X,Y=(21,41,81); 41 levels throughout • Penalty for IJK decreases with increased length of minor dimension, X • Penalty is most severe for sizes typical of a DM patch • IJK is strongly favored by vector for adequate length of X • Surprise: vector prefers KIJ for short X; but an unlikely result once full physics

10. Numerics for Dynamical Solver • Numerical Modeling Issues: • Equations / variables • Vertical coordinate • Terrain representation • Grid staggering • Time Integration scheme • Advection scheme • Strategy • Identify and analyze alternative procedures • Evaluate alternates in idealized simulations • Evaluate in NWP applications as model complexity increases

11. Treatment of Terrain by Vertical Coordinate Terrain Following • Smooth topography well represented • Selective resolution enhancement near ground • Potential for spurious circulations above steep terrain • Can represent blocking due to step terrain • Reduced errors in computing horizontal gradients • Degraded representation of sloped topography • Maintains horizontal coordinate surfaces • Represents terrain slope accurately • Potential complications in numerics for shaved cells Step Mountain Shaved Cell

12. Mountain Wave with Step Terrain Coordinate

13. Mountain Wave with Step Terrain Coordinate

14. Prototype Nonhydrostatic Model Solvers • Split-Explicit Eulerian Model: • Pressure and temperature diagnosed from thermodynamics • Two time level split-explicit time integration • Flux-form prognostic equations in terms of conserved variables • Accurate shape preserving advection • Both terrain-following height and mass coordinates being tested • Semi-Implicit Semi-Lagrangian Model: • Unstaggered (A) grid • Forward trajectories with cascade interpolation back to grid • High order compact differencing • Terrain following hybrid coordinate

15. Pressure terms directly related to : Flux-Form Equations in Height Coordinates Conservative variables: Inviscid, 2-D equations in Cartesian coordinates

16. Flux-Form Equations in Mass Coordinates Hydrostatic pressure coordinate: Conservative variables: Inviscid, 2-D equations without rotation:

17. 2-D Mountain Wave Simulation a = 1 km, dx = 200 m a = 100 km, dx = 20 km Height Coordinate Mass Coordinate

18. 5 min 10 min 15 min Comparison of Gravity Current Simulations Height Coordinate Mass Coordinate

19. Comparison of Height and Mass Coordinates

20. Time-Split Leapfrog and Runge-Kutta Integration Schemes

21. Strategy for WRF Model Physics • Define “plug-compatible” interface for physics modules • Implement and test basic physics in WRF: • Kessler-type (no-ice) microphysics • Lin et al. (graupel included) microphysics • Kain-Fritsch cumulus parameterization • Shortwave radiation (cloud-interactive) from MM5 • Longwave radiation (RRTM) • MRF (Hong and Pan) PBL • Blackadar surface slab ground temperature prediction • Implement a complete suite of research physics packages • Encourage and facilitate community involvement in advanced model physics development and evaluation

22. WRF 3D-Var Data-Assimilation System • Essential features of initial 3D-Var system: • Basic quality control • Assimilation of conventional observations (surface, radiosonde, aircraft) • Multivariate analysis • Adherence to WRF coding standards • Additional features to be added: • 3-D anisotropic background errors using recursive filters • Additional observation operators (radar, satellite, wind profiler, etc.) • Flexible choice of first guess • Further enhancements

23. WRF Model Testing and Verification Strategy • Analytic and converged numerical solutions • Inviscid dynamics (baroclinic instability, frontogenesis) • Buoyancy driven flow (gravity currents, warm thermals) • Topographic flow (nonhydrostatic, hydrostatic, inertial-gravity mountain waves) • Moist convection (idealized convection with constant eddy mixing) • Regime dependence of nonlinear flows • Topographic flow (finite amplitude waves, wave overturning, lee vortices) • Moist convection (convective behavior as a function of CAPE and shear) • Observational case studies • Extratropical cyclones (STORM-FEST case) • Topographic flow (downslope windstorm, orographic precip., cold-air damming) • Moist convection (supercell case, squall-line case, multi-parameter radar case) • PBL-surface physics (1-D diurnal cycle, sea-breeze case, marine inversion and CTD) • Tropical cyclone (COMPARE case)

24. Projected Timeline for WRF Project

25. WRF Calendar for 2000 • 12 January • 14 February • 29-30 March • June • 30 September First WRF Oversight Board Meeting WRF Planning Meeting WRF Planning Workshop First Annual WRF Users Workshop Release of “bare-bones” WRF Model WRF Status & Updates: wrf-model.org