Weather Research and Forecast (WRF) Model Promote closer ties between research and operations Develop an advanced mesoscale forecast and assimilation system 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
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 - PowerPoint PPT Presentation
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04/19/23
Weather Research and Forecast (WRF) Model
Promote closer ties between research and operations
Develop an advanced mesoscale forecast and assimilation system
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
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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
WRF Project Collaborators
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WRF Project Management
WRF OversightBoard
WRF ScienceBoard
WRF Coordinator
WRF Development Teams (5)
Steve Lord, Chair NOAA/NCEPSandy MacDonald FSL &GFDLBob Gall NCAR/MMMSteve Nelson NSF/ATMCol. Charles French USAF/AFWA
Joe Klemp NCAR/MMM
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Numerics and Software
(J. Klemp)
Data Assimilation (T. Schlatter)
Analysis and Validation
(K. Droegemeier)
Community Involvement
(W. Kuo)
Operational Implementation
(G. DiMego)
Data Handling and Archive (G. DiMego)
NCEP Requirements
(G. DiMego)
AFWA Requirements
(M. Farrar)
Model Physics (J. Brown)
Atmospheric Chemistry (P. Hess)
Workshops, Distribution, and Support
(J. Dudhia)
Dynamic Model Numerics
(W. Skamarock)
Analysis and Visualization (L. Wicker)
Wor
king
Gro
ups
Model Testing and Verification
(C. Davis)
Software Architecture,
Standards, and Implementation (J. Michalakes)
Standard Initialization (J. McGinley)
3-D Var (J. Purser)
4-D Var,Ensemble
Techniques (D. Barker)
WRF Development Teams
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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
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
Single version of code enabled for efficient execution on:
– Distributed-memory multiprocessors
– Shared-memory multiprocessors– Distributed memory clusters of
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
– Identify and analyze alternative procedures– Evaluate alternates in idealized simulations– Evaluate in NWP applications as model complexity increases
Numerics for Dynamical Solver
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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
Shaved Cell
Step Mountain
Terrain Following
Treatment of Terrain by Vertical Coordinate
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Mountain Wave with Step Terrain Coordinate
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Mountain Wave with Step Terrain Coordinate
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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
Prototype Nonhydrostatic Model Solvers
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0
z
W
x
U
t
Qz
W
x
U
t
z
Ww
x
Uwg
zR
t
W
z
Wu
x
UufV
xR
t
U
,,, wWvVuU
pcR p
Conservative variables:
Inviscid, 2-Dequations inCartesiancoordinates
Pressure termsdirectly related to
Flux-Form Equations in Height Coordinates
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Flux-Form Equations in Mass Coordinates
0
,,
0
p
Rpgw
dt
d
x
U
t
Qx
U
t
w
x
Uwpg
t
W
u
x
Uu
x
p
x
p
t
U
tst ,/Hydrostatic pressure coordinate:
Inviscid, 2-Dequations without rotation:
,,, wWuUConservative variables:
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2-D Mountain Wave Simulation
a = 1 km, dx = 200 m a = 100 km, dx = 20 km
Mass CoordinateHeight Coordinate
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5 min 10 min 15 min
Comparison of Gravity Current Simulations
HeightCoordinate
MassCoordinate
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Comparison of Height and Mass Coordinates
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Time-Split Leapfrog and Runge-Kutta Integration Schemes
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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
Strategy for WRF Model Physics
04/19/23
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