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

5 min 10 min 15 min

Comparison of Gravity Current Simulations

HeightCoordinate

MassCoordinate

Time-Split Leapfrog and Runge-Kutta Integration Schemes

Two Examples of Possible Vertical Coordinate Structures With The General Hybrid Coordinate

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