36h WRF Precip Forecast

1
Weather Research and Forecasting Model
Goals Develop an advanced mesoscale forecast and
assimilation system, and accelerate research
advances into operations
36h WRF Precip Forecast

• 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

Analyzed Precip
27 Sept. 2002
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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

3
Software Architecture
Driver
Driver Layer
Config Inquiry
I/O API
Mediation Layer
DM comm
Package Independent
Solve
OMP
Config Module
WRF Tile-callable Subroutines
Data formats, Parallel I/O
Package Dependent
Message Passing
Model Layer
Threads
External Packages

• 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

4
WRF Multi-Layer Domain Decomposition

• Single version of code enabled for efficient
  execution on
• Shared-memory multiprocessors
• Distributed-memory multiprocessors
• Distributed clusters of SMPs
• Vector and scalar processors

• 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

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Scaling Performance
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Benefit of Higher Order Model Numerics
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Eulerian Nonhydrostatic Model Solvers

• Full conservation of variables in flux form
• Prognostic equations for conserved quantities
• Pressure and temperature diagnosed from
• thermodynamics
• High 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

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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

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Gravity Current Simulations
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2-D Mountain Wave Simulation
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2D Squall Lines
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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 )
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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 US
3 km Regional FSL 10 km
Northeastern US AFWA 15 km
Continental US U. Of Illinois 25 km,
Midwestern US (http//WRF-model.org
)
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36 h Forecast Valid 12Z 27 Sept 02 24 h
Precipitation Verification
(mm)
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3-6 h Accumulated Precip Forecasts Valid 18Z 4
June 2002
(From Mike Baldwin and Matt Wandishin, NOAA/NSSL)
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3-6 h Accumulated Precip Forecasts Valid 18Z 4
June 2002
(From Mike Baldwin and Matt Wandishin, NOAA/NSSL)
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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)
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Model Physics in High Resolution NWP
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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?

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Realtime 4 km BAMEX Forecast
24-25 May 2003
Radar reflectivity 00Z 24 May initialization 36
h forecast
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Reflectivity, 12 Z 24 May 2003
Observed
WRF 12 h 4 km forecast
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Reflectivity, 06 Z 25 May 2003
Observed
WRF 30 h 4 km forecast
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Realtime 4 km BAMEX Forecasts Valid 6/8/03 12Z
Composite NEXRAD Radar
4 km BAMEX forecast 36 h Reflectivity
4 km BAMEX forecast 12 h Reflectivity
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Realtime 4 km BAMEX Forecasts Valid 6/10/03 12Z
Composite NEXRAD Radar
4 km BAMEX forecast 36 h Reflectivity
4 km BAMEX forecast 12 h Reflectivity
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Realtime 4 km BAMEX Forecasts Valid 6/10/03 12Z
Composite NEXRAD Radar
10 km BAMEX forecast 36 h Reflectivity
10 km BAMEX forecast 12 h Reflectivity
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Realtime 4 km BAMEX Forecasts Valid 6/10/03 12Z
Composite NEXRAD Radar
22 km CONUS forecast 36 h Reflectivity
22 km CONUS forecast 12 h Reflectivity
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Problems with Traditional Verification Schemes
forecast 1
forecast 2
truth
Issue the obviously poorer forecast has better
skill scores From Mike Baldwin NOAA/NSSL
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Ensemble Forecasting

• 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

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Coupled Systems
(Source Rick Allard, NRL)
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Model Coupling

• 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)

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WRF-Chem Based on EPA CMAQ Model

• Development of a WRF-Chem model based on EPAs
  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)
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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)
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Comparison with NOAA Aircraft Obs.
Model shows higher background ozone plume
locations are well matched with observations.
(Daewon W. Byun and Seung-Bum Kim, University of
Houston)
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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)

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Model Forecasts

• Surface O3 forecast
• Similar results!
• Wind direction
• Front location
• Peak O3
• Other AQ models are similar
• Figure Stu McKeen
• (NOAA/AL)