WSN05 6 Sep 2005Toulouse, France
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 StormsUniversity of Oklahoma USA
WSN05 6 Sep 2005Toulouse, 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 2005Toulouse, 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 2005Toulouse, France
CAPS 3DVAR Radar Assimilation Flow Chart
Multi-scale3DVAR
External Model Interpolator
Radar 1Radar 2
Radar 3Radar 4
Radar N
Radar QC &Remapper
METAR
Mesonets
RawinsondesAircraft
Cloud Analysis& Latent Heat
Adjustment
ARPS NWP Model ARPS-to-WRF
WRF NWP Model
Sat IR
Sat VisSatellite
Remapper
WindProfilers
AIRS Soundings
WSN05 6 Sep 2005Toulouse, France
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 2005Toulouse, France
Remapping to x = 2 km
WSN05 6 Sep 2005Toulouse, France
CAPS 3DVAR System• General form
• 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
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2
1DJ cc
z
w
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x
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WSN05 6 Sep 2005Toulouse, 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 2005Toulouse, France
3DVAR Applied to Fort Worth Tornadic Storm
• Fort Worth, Texas area tornadoes of 28 Mar 2000
• 3-km ARPS Forecast 23 UTC-06 UTCnested 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 2005Toulouse, France
1.5 h ForecastWind & Cloud Assim
00:30 UTCRadar Reflectivity
WSN05 6 Sep 2005Toulouse, France
1.5 h ForecastCloud Only Assim
1.5 h ForecastWind Only Assim
WSN05 6 Sep 2005Toulouse, France
00:30 UTC Radar Reflectivity
1.5 h Forecast Surface Vorticity
Wind & Cloud Assim
WSN05 6 Sep 2005Toulouse, France
1.5 h Forecast Surface Vorticity
Cloud Only Assim
1.5 h Forecast Surface VorticityWind Only Assim
WSN05 6 Sep 2005Toulouse, 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 2005Toulouse, France
1-hour Forecast (1-hr Accum Precip)17-May-2004 01:00
Radar Precip Obs
WRFIC: Eta Interp
WRFIC: ADAS w/Radar
WSN05 6 Sep 2005Toulouse, 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 analysis-initialization.
WSN05 6 Sep 2005Toulouse, 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 periodsin collaboration with SPC/NSSL
• Smaller-domain real-time system run dailyhttp://www.caps.ou.edu/wx
WSN05 6 Sep 2005Toulouse, 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
