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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


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


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


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


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


Remapping to x = 2 km


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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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


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


1.5 h ForecastWind & Cloud Assim

00:30 UTCRadar Reflectivity


1.5 h ForecastCloud Only Assim

1.5 h ForecastWind Only Assim


00:30 UTC Radar Reflectivity

1.5 h Forecast Surface Vorticity

Wind & Cloud Assim


1.5 h Forecast Surface Vorticity

Cloud Only Assim

1.5 h Forecast Surface VorticityWind Only Assim


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.


1-hour Forecast (1-hr Accum Precip)17-May-2004 01:00

Radar Precip Obs

WRFIC: Eta Interp

WRFIC: ADAS w/Radar


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.


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


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