PROJECT DOCUMENTATION - Weather Graphics - Weather forecasting
Weather Research and Forecasting Model · Weather Research and Forecasting Model Goals: Develop an...
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Weather Research and Forecasting Model Goals: Develop an advanced mesoscale forecast and assimilation system, and accelerate research advances into operations 36h WRF Precip Forecast Analyzed Precip 27 Sept. 2002 • Collaborative partnership, principally among NCAR, NOAA, DoD, OU/CAPS, FAA, and university community • WRF governance through multi-agency Oversight and Science Boards; development conducted by 15 WRF Working Groups • Software framework provides portable, scalable code with plug-compatible modules • Ongoing active testing and rapidly growing community use – Over 1,200 registered community users, annual workshops and tutorials for research community – Daily experimental real-time forecasting at NCAR , NSSL, FSL, AFWA, U. of Illinois • Operational implementation at NCEP and AFWA in FY04
Transcript of Weather Research and Forecasting Model · Weather Research and Forecasting Model Goals: Develop an...
Weather Research and Forecasting ModelGoals: Develop an advanced mesoscale forecast
and assimilation system, and accelerate research advances into operations
36h WRF Precip Forecast
Analyzed Precip
27 Sept. 2002
• Collaborative partnership, principally among NCAR, NOAA, DoD, OU/CAPS, FAA, and university community
• WRF governance through multi-agency Oversight and ScienceBoards; development conducted by 15 WRF Working Groups
• Software framework provides portable, scalable code withplug-compatible modules
• Ongoing active testing and rapidly growing community use– Over 1,200 registered community users, annual
workshops and tutorials for research community– Daily experimental real-time forecasting at NCAR ,
NSSL, FSL, AFWA, U. of Illinois• Operational implementation at NCEP and AFWA in FY04
WRF Software Design
• Performance-Portable– Scaling on foreseeable parallel platforms– Architecture independence– No specification of external packages
• Run-Time Configurable– Domain size, nest configurations, parallelism– Physics, numerics, data, and I/O options
• Maintainability & Extensibility– Single source code– Modular, hierarchical design, coding standards– Plug compatible physics, dynamical cores– Registry to describe and manage data and I/O
Software Architecture
OMPSolve
DM comm
Thre
ads
Data formats,Parallel I/O
Mes
sage
Pass
ing
• Driver: I/O, communication, multi-nests, state data• Model routines computational, tile-callable, thread-safe• Mediation layer: interface between model and driver• Interfaces to external packagese
DriverLayer Driver
PackageIndependent
Mediation Layer
ConfigInquiry I/O API
PackageDependent
ConfigModule
WRF Tile-callableSubroutines
Model Layer
External Packages
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
• Single version of code enabled for efficient execution on:
– Shared-memory multiprocessors– Distributed-memory multiprocessors– Distributed clusters of SMPs– Vector and scalar processors
Logical domain
1 Patch, divided into multiple tiles
Inter-processor communication
Scaling PerformanceWRF EM Core, 425x300x35, DX=12km, DT=72s
0102030405060708090
100110120130140150160170
0 100 200 300 400 500 600 700 800 900 1000 1100processors
Gflo
p/s
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
simulation speed (hours/hour)
TCS 1-rail
TCS 2-rails
IBM Regatta
IBM Winterhawk IIiJet
Benefit of Higher Order Model Numerics
Error versus resolution
solu
tion
e rro
r
resolution
low-order method
high-order method so
lutio
n e r
ror
cost
low-order method
high-order method
Error versus cost
Eulerian Nonhydrostatic Model Solvers
Full conservation of variables in flux form– Prognostic equations for conserved quantities – Pressure and temperature diagnosed from
thermodynamicsHigh order numerics– Two level, 3rd order Runge-Kutta split-explicit time
integration– 2nd- 6th order centered or upwind advection
Alternative vertical coordinates– Terrain-following height coordinate– Terrain-following mass coordinate
WRF Model Applications• Basic research
– Idealized simulations– Atmospheric process studies– Other geophysical fluid dynamical applications
• Numerical weather research and prediction– Regional NWP– Storm-scale forecasting– Hurricane forecasting (ocean coupling)– Global weather modeling
• Applied meteorological applications– Air quality studies (chemistry coupling)– Fire weather research (combustion coupling)– Regional climate studies
Gravity Current Simulations
5 min 10 min 15 min
HeightCoordinate
MassCoordinate
∆x = ∆z = 100 m
2-D Mountain Wave Simulation
a = 1 km, dx = 200 m a = 100 km, dx = 20 km
Mass CoordinateHeight Coordinate
2D Squall Lines
Supercell Thunderstorm Simulation
Surface temperature, surface winds and cloud field at 2 hours
(dx = 2 km, dz = 500 m, dt = 12 s, 80 x 80 x 20 km domain )
WRF Real-Time Forecasting
NCAR: 10 and 22 km Continental US, 4 km Central US (BAMEX)
NCEP: 8 km Mass and NMM,West, Central, and Eastern US
NSSL: 12 km Continental US3 km Regional
FSL: 10 km Northeastern US
AFWA: 15 km Continental US
U. Of Illinois: 25 km, Midwestern US
(http://WRF-model.org)
36 h Forecast Valid 12Z 27 Sept 02 24 h Precipitation Verification
(mm)
175150125100755035302520151050
12 km Opnl ETA
24 h RFC Analysis
22 km WRF
10 km WRF
3-6 h Accumulated Precip ForecastsValid 18Z 4 June 2002
4 km Analysis 22 km WRF10 km WRF
0
4
8
12
20
50
precip(mm)
(From Mike Baldwin and Matt Wandishin, NOAA/NSSL)
3-6 h Accumulated Precip ForecastsValid 18Z 4 June 2002
0
4
8
12
20
50
precip(mm)
8 km NMM 12 km opnl ETA4 km Analysis
(From Mike Baldwin and Matt Wandishin, NOAA/NSSL)
Power Spectra for 3 h Precipitation
12Z forecasts,15-18 Z accum precip,valid 4 June 2002
(From Mike Baldwin and Matt Wandishin, NOAA/NSSL)
Model Physics in High Resolution NWP
PBL Parameterization
Physics“No Man’s Land”
1 10 100 km
Cumulus ParameterizationResolved Convection
LES
Two Stream Radiation3-D Radiation
Convection Resolving NWP using WRF
Questions to address:
Is there any increased skill in convection-resolving forecasts, measured objectively or subjectively?
Is there increased value in these forecasts?
What can we expect given that the small spatial and temporal scales we are now resolving are inherently unpredictable at forecast times of O(day)?
If the forecasts are more valuable, are they worth the cost?
Realtime 4 km BAMEX Forecast
Radar reflectivity00Z 24 May initialization36 h forecast
24-25 May 2003
Reflectivity, 12 Z 24 May 2003
Observed WRF 12 h 4 km forecast
Reflectivity, 06 Z 25 May 2003
Observed WRF 30 h 4 km forecast
Realtime 4 km BAMEX Forecasts Valid 6/8/03 12Z
4 km BAMEX forecast 36 h Reflectivity
4 km BAMEX forecast 12 h Reflectivity
Composite NEXRAD Radar
Realtime 4 km BAMEX Forecasts Valid 6/10/03 12Z
4 km BAMEX forecast 36 h Reflectivity
4 km BAMEX forecast 12 h Reflectivity
Composite NEXRAD Radar
Realtime 4 km BAMEX Forecasts Valid 6/10/03 12Z
10 km BAMEX forecast 36 h Reflectivity
10 km BAMEX forecast 12 h Reflectivity
Composite NEXRAD Radar
Realtime 4 km BAMEX Forecasts Valid 6/10/03 12Z
22 km CONUS forecast 36 h Reflectivity
22 km CONUS forecast 12 h Reflectivity
Composite NEXRAD Radar
Problems with Traditional Verification Schemes
truth forecast 1 forecast 2
Issue: the obviously poorer forecast has better skill scores
From Mike BaldwinNOAA/NSSL
Ensemble Forecasting
t critical
Deterministic Forecast Probabilistic
Forecast
Initial State Uncertainty
Mean
Truth
• Advantages– Ensemble mean is generally superior – Ensembles provide
• a measure of expected skill or confidence• a quantitative basis for probabilistic forecasting• a rational framework for forecast verification• information for targeted observations
• Limitations/Challenges– Not clear how to optimally specify the initial
conditions (singular vectors, breeding, perturbed observations)
– Requires more computer resources
Coupled Systems
(Source: Rick Allard, NRL)
Model Coupling27km WRF 10m wind vel.
Nov. 7-8 2002
SWAN Wave Heights(Mobile Bay)
• Adapting WRF framework for model coupling
– Extension of WRF I/O API specification
– Use of Model Coupling Environment Library, and Model Coupling Toolkit
• Applications– Atmosphere/ocean coupling
(Hurricane-WRF)– Atmosphere/chemistry coupling
(WRF-Chem)
WRF-Chem Based on EPA CMAQ Model
Development of a WRF-Chem model based on EPA’s Community Multiscale/Multipollutant Air Quality (CMAQ) model to meet both on-line and off-line modeling needs (Institute for Multidimensional Air Quality Studies, U. Houston).
Intended use of coupled air-quality model- forecasting chemical-weather, - testing air pollution abatement strategies, - planning and forecasting for field campaigns,- analyzing measurements from field campaigns - assimilation of satellite and in-situ chemical
measurements
(Daewon W. Byun and Seung-Bum Kim, University of Houston)
Simulated Surface O3 and Horizontal Wind
2130 UTC August 27, 2000
Ozone Surface Winds
High ozone plumes are located in the downwind side of high emission sources in the urban and industrial complexes due to steady southeasterly sea breeze winds
(Daewon W. Byun and Seung-Bum Kim, University of Houston)
Comparison with NOAA Aircraft Obs.
Aug. 27, 2000
TIME (UTC)
18 19 20 21 22 23 0
ALTI
TUD
E (k
m)
0
1
2
3
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alt_obs
0
20
40
60
80
100
120O3_obs O3_m_geos O3_m_prof
O3
(ppb
V)
Model shows higher background ozone; plume locations are well matched with observations.
(Daewon W. Byun and Seung-Bum Kim, University of Houston)
Online WRF-Chem Implementation (FSL)
• Consistent: all transport done by meteorology– Same vertical and horizontal coordinates (no horizontal
and vertical interpolation)– Same physics parameterization for subgrid scale transport– No interpolation in time, or flow/mass adjustments
• Chemistry – Weather interactions / feedbacks– Radiation, microphysics, etc…
• Easy handling (Data management)– Meteorology and chemistry data in same history file
• Often more efficient (CPU costs)
Model Forecasts
Surface O3 forecast
• Similar results!– Wind direction– Front location– Peak O3
• Other AQ models are similar
Figure: Stu McKeen(NOAA/AL)
