Joe Klemp

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Convection Resolving NWP using WRF
Joe Klemp National Center for Atmospheric
Research Boulder, Colorado
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Weather Research and Forecasting Model
Goals Develop an advanced mesoscale forecast and
assimilation system, and accelerate research
advances into operations

• Collaborative partnership, principally among
  NCAR, NOAA,
• DoD, OU/CAPS, FAA, and university community
• Multi-agency WRF governance 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 1,400 registered community users, annual
• workshops and tutorials for research
  community
• Daily experimental real-time forecasting at
  NCAR ,
• NCEP, NSSL, FSL, AFWA, U. of Illinois
• Operational implementation at NCEP and AFWA in
  FY04

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Model Physics in High Resolution NWP
Physics No Mans Land
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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 detailed
  aspects of convection may be inherently
  unpredictable at forecast
• times of O(day)?
• If the forecasts are more valuable, are they
  worth the cost?

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WRF Mass Coordinate Core

• Terrain-following hydrostatic pressure vertical
  coordinate
• Arakawa C-grid, two-way interacting nested grids
  (soon)
• 3rd order Runge-Kutta split-explicit time
  differencing
• Conserves mass, momentum, dry entropy, and
  scalars
• using flux form prognostic equations
• 5th order upwind or 6th order centered
  differencing
• for advection
• Physics for CR applications Lin microphysics,
  YSU PBL,
• OSU/MM5 LSM, Dudhia shortwave/RRTM longwave
• radiation, no cumulus parameterization

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Model Configuration for 4 km Grid

• Domain
• 2000 x 2000 km, 501 x 501 grid
• 50 mb top, 35 levels
• 24 s time step
• Initialization
• Interpolated from gridded analyses
• BAMEX 40 km Eta CONUS analysis
• Isabel 1o GFS global analysis (110 km)
• Computing requirements
• 128 Processors on IBM SP Power 4 Regatta
• Run time 106 min/24h of forecast

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Bow Echo and Mesoscale Convective Vortex
Experiment (BAMEX)
Goal Study the lifecycles of mesoscale
convective vortices and bow echoes in and around
the St. Louis MO area
Field program conducted 20 May 6 July 2003
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Real-time WRF 4 km BAMEX Forecast
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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Real-time WRF 4 km BAMEX Forecast
Initialized 00 UTC 9 June 03
Composite NEXRAD Radar
Reflectivity forecast
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Real-time WRF 4 km BAMEX Forecast
Initialized 00 UTC 10 June 03
Reflectivity forecast
Composite NEXRAD Radar
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Real-time 12 h WRF Reflectivity Forecast
Valid 6/10/03 12Z
Composite NEXRAD Radar
4 km BAMEX forecast
10 km BAMEX forecast
22 km CONUS forecast
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Real-time WRF 4 km BAMEX Forecast
Valid 5/30/03 23Z
Composite NEXRAD Radar
23 h Reflectivity Forecast
Line of Supercells
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Realtime WRF 4 km BAMEX Forecast
Valid 6/23/03 06Z
Composite NEXRAD Radar
30 h Reflectivity Forecast
6 hail 00Z
Squall line
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Realtime WRF 4 km BAMEX Forecast
Valid 6/12/03 06Z
Composite NEXRAD Radar
30 h Reflectivity Forecast
Missed
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Realtime WRF 4 km BAMEX Forecast
Initialized 5/24/03 00Z
Composite NEXRAD Radar
Reflectivity Forecast
12 h
Squall line
24 h
Persists
Dissipates
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Preliminary BAMEX Forecast Verification
Number of MCSs for each 36 h forecast initialized
at 00 UTC.
(Done, Davis, and Weisman)
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Hovmoller Depiction of Hourly Precip
Observed
Model
Data have been averaged in the latitudinal
direction
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Preliminary BAMEX Forecast Verification
Subjective analysis of organized convection
Criteria for successful forecast forecast system
within 400 km and 3 h of those observed.
Yes
No
Probability of detection (POD) 58 False alarm
rate (FAR) 28
(Done, Davis, and Weisman)
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Preliminary Findings for BAMEX Forecasts

• Rapid spinup of storm-scale structure from
  large-scale IC
• Forecasts were helpful to field operations
  planning, particularly
• on the number of systems, their mode and
  location
• 4 km WRF replicates overall MCS structure and
  character
• better than 10 km WRF with cumulus
  parameterization
• More detailed representation of convective mode
• No improvement in precipitation threat scores
• Skill in forecasting systems as high after 21 h
  as during the
• first 6-12 h, suggesting mesoscale control of
  initiation
• Convective trigger function wasnt needed

Convection resolving forecasts should be a useful
tool for predicting significant convective
outbreaks and severe weather
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Hurricane Isabel
NOAA 17 AVHRR 13 Sep 03 1448 GMT
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Hurricane Isabel Track
18/1700Z
4 km WRF Initialized 17/0000Z
10 km WRF Initialized 15/1200Z
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Hurricane Isabel 3 h Precip Forecast
WRF Model 10 km grid 5 day forecast
Initialized 12 UTC 15 Sep 03
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48 h Hurricane Isabel Reflectivity Forecast
Initialized 00 UTC 17 Sep 03
4 km WRF forecast
Radar Composite
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Hurricane Isabel Reflectivity at Landfall
18 Sep 2003 1700 Z
41 h forecast from 4 km WRF
Radar Composite
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Hurricane Isabel Surface-Wind Forecast
WRF Model 4 km grid 2 day forecast
Initialized 00 UTC 17 Sep 03
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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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Scientific Questions for Storm-Scale NWP

• What is the predictability of storm-scale
  events, and will resolution of
• fine-scale details enhance or reduce their
  prediction?
• What observations are most critical, and can
  high-resolution data
• from national networks be used to initialize
  NWP models in real time?
• What physics is required, and do we understand
  it well enough
• for practical application?
• How can ensembles be utilized for storm-scale
  prediction?
• What are the most useful verification
  techniques for storm and
• mesoscale forecasts?
• What networking and computational
  infrastructures are needed
• to support high-resolution NWP?
• How can useful decision making information be
  generated from
• forecast model output?