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Weather Research and Forecast (WRF) Modeling System. Ü. Develop an advanced mesoscale forecast and assimilation system. Ü. Promote closer ties between research and operations. Context:. Design for 1-10 km horizontal grids Advanced data assimilation and model physics - PowerPoint PPT Presentation
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Weather Research and Forecast (WRF) Modeling System
Promote closer ties between research and operations
Develop an advanced mesoscale forecast and assimilation system
Context:
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
http://wrf-model.orghttp://wrf-model.org
12 January First WRF Oversight Board Meeting
14 February WRF Planning Meeting
29-30 March WRF Planning Workshop
23 June First Annual WRF Users WorkshopFirst Meeting of WRF Science Board
30 October Release of “bare-bones” WRF Model
WRF Events for 2000
WRF Status,updates and codes available from: wrf-model.org
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
WRF Project Management
WRF OversightBoard
WRF ScienceBoard
WRF Coordinator
WRF Development Teams (5)
S. Lord, Chair NOAA/NCEPS. MacDonald FSL & GFDLR. Gall NCAR/MMMS. Nelson NSF/ATMCol. C. Benson USAF/AFWACapt. C. Gunderson NAVYG. Kulesa FAA Joe Klemp NCAR/MMM
WRF Science Board
Morris Bender NOAA/OAR Stanley Benjamin NOAA/OAR Daewon Byun NOAA/ARL Mark DeMaria
NOAA/NESDIS Jim Doyle NRL Jimy Dudhia NCAR Michael Farrar USAF/AFWA John Manobianco NASA/ENSCO Jeffrey McQueen NOAA/OAR
Russell Schneider NOAA/NWS Nelson Seaman Penn State U. Danny Sims FAA/ACT-320 David Stensrud NOAA/OAR Wei-Kuo Tao NASA/GSFC Eric Thaler NOAA/NWS Greg Tripoli U. Wisconsin Robert Wilhelmson U. Illinois Ming Xue Oklahoma U./CAPS
Numerics and Software
(J. Klemp)
Data Assimilation (T. Schlatter)
Analysis and Validation
(K. Droegemeier)
Community Involvement
(W. Kuo)
Operational Implementation
(G. DiMego)
Dynamic Model Numerics
(W. Skamarock)
Wor
king
Gro
ups
Software Architecture,
Standards, and Implementation (J. Michalakes)
Standard Initialization (J. McGinley)
3-D Var (J. Derber)
4-D Var, Kalman Filtering & Other Advanced Tech.
(D. Barker)
Ensemble Forecasting
(S. Tracton?)
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)
Analysis & Visualization (L. Wicker)
Model Testing and Verification
(C. Davis)
WRF Development Teams
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
WRF Software Objectives
http://www.mmm.ucar.edu/wrf/WG2/WRF_conventions.html
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
SMPs
WRF Multi-Layer Domain Decomposition Logical
domain1 Patch, divided into multiple tiles
Inter-processor communication
WRF Hierarchical Software Architecture 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
wrf
initial_config alloc_and_configure init_domain integrate
solve_interface
solve
Model Layer
Driver Layer
prep
filt
er
big_
step
deco
uple
adva
nce u
v
reco
uple
scal
ars
phys
ics
adva
nce
w
Penalty for IJK Loop & Storage Ordering
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 IKJ has been chosen for loop and storage ordering
2141
81
21
41
81
0
5
10
15
20
25
30
X tile dimension
Y tile dimension
Alpha workstation (EV56)
2141
81
21
41
81
-80
-60
-40
-20
0
20
40
60
80
100
X tile dimension
Y tiledimension
VPP 5000
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
Numerics for Dynamical Solver
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 / Partial Step
Step Mountain
Terrain Following
Treatment of Terrain by Vertical Coordinate
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– Runge-Kutta (3rd & 4th order) time integration
Prototype Nonhydrostatic Model Solvers
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 & Betts-Miller-Janjic cumulus parameterizations– Shortwave radiation (cloud-interactive) from MM5 – Longwave radiation (RRTM) – MRF (Hong and Pan) PBL – Blackadar surface slab ground temperature prediction
NCEP working on the NOAH LSM for WRF 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
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
WRF 3D-Variational Data-Assimilation System
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&CTD)– Tropical cyclone (COMPARE case)
Pre-implementation Strategy for WRF Model Testing & Validation
GOAL: perform clean operational vs WRF comparisons Convert existing Meso Eta Model into WRF modeling infrastructure
– use selectable dynamics WRF option– use tested nonhydrostatic component of Meso
Compare computer performance of WRF vs operations– measure performance benefit or penalty of WRF design– if significant penalty is measured, then redesign is called for– if no penalty, then could immediately implement WRF modeling
infrastructure into NCEP operations for both nested & continental Meso Compare forecast performance of WRF vs operations
– Emphasis on REAL-DATA retrospective case studies– Small and large-domain capabilities examined for nested and continental
requirements of NCEP operations
Timeline for WRF ProjectDevelopment Task 2000 2001 2002 2003 2004 2005-8
Release and support to community Implement into operations
Basic WRF model (single dynamic core, limited physics, standard initialization)
Research quality NWP version of WRF
WRF model with selectable dynamic cores
WRF model with hybrid vertical coordinate
WRF model physicsSimple Basic Research suite Advanced suite
3-D VAR assimilation systemBasic Research Advanced
4-D VAR assimilation systemBasic Advanced
Testing for initial operational useat NCEP, AFWA and FSL
Routine diagnosis of operational performance & of future refinements