Transcript Efficient Assimilation of Radar Data for Short

Efficient Assimilation of Radar
Data at High Resolution for
Short-Range Numerical Weather
Prediction
Keith Brewster, Ming Hu,
Ming Xue and Jidong Gao
Center for Analysis and Prediction of Storms
University of Oklahoma USA
WSN05 6 Sep 2005
Toulouse, France
Radar Analysis & Assimilation
Research Topics in CAPS
• Single-Doppler Velocity Retrieval (SDVR)
• Bratseth-type Successive Correction
Analysis (ADAS)
• 3DVAR at Storm Scale
• Cloud & hydrometeor analysis with
latent heating adjustment
• Phase/Position error correction methods
• Ensemble-Kalman Filter at Storm Scale
WSN05 6 Sep 2005
Toulouse, France
Radar Analysis & Assimilation
Research Topics in CAPS
• Single-Doppler Velocity Retrieval (SDVR)
• Bratseth-type Successive Correction
Analysis (ADAS)
• 3DVAR at Storm Scale
• Cloud & hydrometeor analysis with
latent heating adjustment
• Phase/Position error correction methods
• Ensemble-Kalman Filter at Storm Scale
WSN05 6 Sep 2005
Toulouse, France
CAPS 3DVAR Radar Assimilation Flow Chart
Radar 1
Radar 2
External Model
Interpolator
Radar QC &
Remapper
Radar 3
Radar 4
Radar N
Aircraft
Multi-scale
3DVAR
Rawinsondes
AIRS
Soundings
Mesonets
Wind
Profilers
Cloud Analysis
& Latent Heat
Adjustment
ARPS
NWP Model
METAR
Sat IR
Satellite
Remapper
ARPS-to-WRF
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Toulouse, France
Sat Vis
WRF
NWP Model
Radar Quality Control &
Remapping
• Quality Control
– AP & Clutter detection
– Doppler radial velocity unfolding
• Remapping
–
–
–
–
Matches data spacing to model resolution
Eases reflectivity mosaicking
Can be viewed as a form of “superobbing”
Local least-squares interpolation/smoothing
Quadratic in horizontal, Linear in vertical
WSN05 6 Sep 2005
Toulouse, France
Remapping to Dx = 2 km
WSN05 6 Sep 2005
Toulouse, France
CAPS 3DVAR System
• General form
1
1
b T
1
b
o T
J (x)  x  x  B x  x   H x   y  R 1 H x   y o   J c x 
2
2
• Rewritten in incremental form
• Error correlation implemented by means of
a recursive filter.
• Can be applied in multi-grid fashion
• Dynamic constraint:
weak constraint: anelastic mass continuity
1 2 2
J c  c D
2
w
 u v 
D  

 
y 
z
 x
WSN05 6 Sep 2005
Toulouse, France
Radar Ingest- Reflectivity
• Cloud analysis system
– Remapped Satellite Images (Vis and IR)
– Surface observations of cloud bases
– Reflectivity converted to hydrometeors
Rain, hail, dry snow, wet snow
• Cloud water quantity and latent heating
estimated using a lifted-parcel with
entrainment
WSN05 6 Sep 2005
Toulouse, France
3DVAR Applied to
Fort Worth Tornadic Storm
• Fort Worth, Texas area tornadoes of
28 Mar 2000
• 3-km ARPS Forecast 23 UTC-06 UTC
nested in 9-km forecast 18 UTC – 06 UTC
• Six 10-min analysis cycles (1 hour) using
NEXRAD data 22 UTC-23 UTC.
• Experiments:
– Wind and Cloud Assimilated
– Wind Alone
– Cloud Alone
Ming Hu et al. papers submitted to MWR
WSN05 6 Sep 2005
Toulouse, France
00:30 UTC
Radar Reflectivity
1.5 h Forecast
Wind & Cloud Assim
WSN05 6 Sep 2005
Toulouse, France
1.5 h Forecast
Cloud Only Assim
1.5 h Forecast
Wind Only Assim
WSN05 6 Sep 2005
Toulouse, France
00:30 UTC
Radar Reflectivity
1.5 h Forecast
Surface Vorticity
Wind & Cloud Assim
WSN05 6 Sep 2005
Toulouse, France
1.5 h Forecast
Surface Vorticity
Cloud Only Assim
1.5 h Forecast
Surface Vorticity
Wind Only Assim
WSN05 6 Sep 2005
Toulouse, France
Fort Worth Case Summary
• Similar situation observed for second tornado
about 15 min later.
• Good forecast results for this case primarily due
to cloud & diabatic portion of analysis.
• Winds provide improvement to forecasted
vorticity.
• Applicable to on-going convection; other case
studies show utility of radial wind assimilation in
convection-initiation forecast situations.
WSN05 6 Sep 2005
Toulouse, France
1-hour Forecast (1-hr Accum Precip)17-May-2004
01:00
WRF
IC: Eta Interp
WRF
IC: ADAS w/Radar
Radar Precip Obs
WSN05 6 Sep 2005
Toulouse, France
2004 Real-time Use Summary
• Spin-up at 4-km is largely eliminated using
radar and satellite data.
• Good results even with a static analysisinitialization.
WSN05 6 Sep 2005
Toulouse, France
Sample of Ongoing &
Future Work with These Tools
• Testing different lengths of assimilation
cycle and total assimilation window length
• Will also test using 3DVAR output in
Incremental Analysis Updating
• More real-time high-resolution test periods
in collaboration with SPC/NSSL
• Smaller-domain real-time system run daily
http://www.caps.ou.edu/wx
WSN05 6 Sep 2005
Toulouse, France
Credits
• CAPS Research Scientists
– Ming Xue, Jidong Gao, Dan Weber, Kelvin
Droegemeier
• CAPS Model and Real Time System Support
– Kevin Thomas and Yunheng Wang
• CAPS Students
– Ming Hu, Dan Dawson
• WSN05 Conference Travel Support
OU School of Meteorology WeatherNews Chair funds
WSN05 6 Sep 2005
Toulouse, France