weather research and forecast (wrf) model

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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

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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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Page 1: Weather Research and Forecast (WRF) Model

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

Page 2: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 3: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 4: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 5: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 6: Weather Research and Forecast (WRF) Model

04/19/23

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 domain

1 Patch, divided into multiple tiles

Inter-processor communication

Page 7: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 8: Weather Research and Forecast (WRF) Model

04/19/23

Parallel Scaling on Compaq Computer

Compaq ES40, 41x81x81

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

processors

spee

du

p

1 (2d)

2 (2d)

4 (2d)

1 (1d)

2 (1d)

4 (1d)

ideal

Page 9: Weather Research and Forecast (WRF) Model

04/19/23

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

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

Page 10: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 11: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 12: Weather Research and Forecast (WRF) Model

04/19/23

Mountain Wave with Step Terrain Coordinate

Page 13: Weather Research and Forecast (WRF) Model

04/19/23

Mountain Wave with Step Terrain Coordinate

Page 14: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 15: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 16: Weather Research and Forecast (WRF) Model

04/19/23

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:

Page 17: Weather Research and Forecast (WRF) Model

04/19/23

2-D Mountain Wave Simulation

a = 1 km, dx = 200 m a = 100 km, dx = 20 km

Mass CoordinateHeight Coordinate

Page 18: Weather Research and Forecast (WRF) Model

04/19/23

5 min 10 min 15 min

Comparison of Gravity Current Simulations

HeightCoordinate

MassCoordinate

Page 19: Weather Research and Forecast (WRF) Model

04/19/23

Comparison of Height and Mass Coordinates

Page 20: Weather Research and Forecast (WRF) Model

04/19/23

Time-Split Leapfrog and Runge-Kutta Integration Schemes

Page 21: Weather Research and Forecast (WRF) Model

04/19/23

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

Page 22: Weather Research and Forecast (WRF) Model

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

– Additional observation operators (radar, satellite, wind profiler, etc.)

– Flexible choice of first guess

– Further enhancements

WRF 3D-Var Data-Assimilation System

Page 23: Weather Research and Forecast (WRF) Model

04/19/23

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)

Page 24: Weather Research and Forecast (WRF) Model

04/19/23

Development Task 2000 2001 2002 2003 2004 2005-08

Basic WRF model (limited physics, standard initialization)

Research quality NWP version of WRF

Model physicsSimple Research suite Advanced

3D-Var assimilation systemBasic Research Advanced

4D-Var assimilation system, ensemble techniques

Basic Advanced

Testing for operational use at NCEP, FSL, & AFWA

Diagnosis of operational performance, refinements

Release and support to community Operational deployment

Projected Timeline for WRF Project

Page 25: Weather Research and Forecast (WRF) Model

04/19/23

12 January

14 February

29-30 March

23 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 Calendar for 2000

WRF Status & Updates: wrf-model.org