Fanyou Kong1 and Kelvin Droegemeier1,2

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Presentation transcript:

Storm-Scale Ensemble Forecast Experiment - Fort Worth Tornadic Storm Case Fanyou Kong1 and Kelvin Droegemeier1,2 1Center for Analysis and Prediction of Storms, 2School of Meteorology, The University of Oklahoma

Domain Setting 24km (238x150) 6km (180x180) 3km (180x180)

Ensemble perturbation method Four SLAF (scaled-lagged average forecast) members: s1, s2; s3, s4 perturbations between previous ARPS forecasts (P1,P2) and current analysis are ± to the analysis One control member (regular ARPS run): cntl

24 km ensemble cntl P1 s1 s2 P2 s3 s4 6 km ensemble 3 km ensemble 3/28/2000 3/29/2000 00Z 06Z 12Z 18Z 00Z 06Z 24 km ensemble 18-hr cntl 24-hr P1 s1 s2 30-hr P2 s3 s4 6 km ensemble 12-hr rad, sat (P1 – cntl) (P2 – cntl) 3 km ensemble 22Z 8-hr rad, sat

12hr accumulate rainfall ARPS ETA

3-hr rainfall from 24 km ensemble cntl mean spread

Domain average spread from 24 km ensemble

Domain average spread from 24 km ensemble

3-hr rainfall from 24 km ensemble cntl s1 s2 s4 s3

3-hr rainfall probability from 24 km ensemble

500 hPa Height from 24 km ensemble mean spread

Sea Level Pressure from 24 km ensemble mean spread

Surface Temperature from 24 km ensemble mean spread

6 km ensemble

24 km ensemble P1 P2 6 km ensemble cntl s1 s2 s3 s4 3/28/2000 3/29/2000 00Z 06Z 12Z 18Z 00Z 06Z 24 km ensemble 18-hr 24-hr P1 30-hr P2 6 km ensemble 12-hr cntl rad, sat s1 (P1 – cntl) s2 s3 (P2 – cntl) s4

1-hr rainfall from 6 km ensemble cntl mean spread

Domain average spread from 6 km ensemble

Domain average spread from 6 km ensemble

1-hr rainfall from 6 km ARPS ensemble cntl s3 s4 mean

1-hr rainfall probability from 6 km ensemble

1-hr rainfall probability from 6 km ensemble

Hourly accumulate rainfall (mean vs obs)

Hourly accumulate rainfall (probability vs obs) Prob ≥ 0.1 in

3 km ensembles Test different ways to form IC/BC for individual members Evaluate ensemble analyses and products suitable for storm-scale EF Assess value of storm-scale EF

24 km ensemble P1 P2 6 km ensemble 3 km ensemble cntl s1 s2 s3 s4 3/28/2000 3/29/2000 00Z 06Z 12Z 18Z 00Z 06Z 24 km ensemble 18-hr 24-hr P1 30-hr P2 6 km ensemble 12-hr 3 km ensemble 8-hr cntl (method one & two) rad, sat 22Z s1 (P1 – cntl) s2 s3 (P2 – cntl) s4

3 km Ensembles – Method One Initiate at 22Z March 28 Control run from 6km cntl s1/s2 using perturbation between 24km P1 and control, s3/s4 using perturbation between 24km P2 and control Run ADAS only once (control run), with NIDS and sat data Explicit microphysics

Composite reflectivity from 3km cntl (initiate at 22Z)

radar cntl

Method One s1 s3 s2 s4

Method One s1 s3 s2 s4

3 km Ensembles – Method Two Initiate at 22Z March 28 Control run from 6km cntl s1/s2 using perturbation between 24km P1 and control, s3/s4 using perturbation between 24km P2 and control Run ADAS for each member, with NIDS and sat data Explicit microphysics

Method Two s1 s3 s2 s4

Method Two s1 s3 s2 s4

Surface reflectivity (method two) mean spread

Surface reflectivity probability (method two) ≥ 35 dBZ ≥ 45 dBZ

Hourly rainfall (method two) s1 s3 obs s2 s4

Hourly rainfall probability from 3km ensemble (method two)

3 km Ensembles – Method Three Initiate at 23Z March 28 Control run from 6km cntl s1/s2 using perturbation between 24km P1 and control, s3/s4 using perturbation between 24km P2 and control Run ADAS for each member, with NIDS and sat data Explicit microphysics

Composite reflectivity from 3km cntl (method three)

Model reflectivity vs radar cntl radar

Model reflectivity vs radar

Model reflectivity vs radar

Model reflectivity vs radar

Model reflectivity vs radar

Model reflectivity vs radar mean radar

Surface reflectivity from 3km ensemble (method three) cntl s3 s4 mean

Surface reflectivity probability (method three) ≥ 35 dBZ ≥ 45 dBZ

Surface reflectivity probability (method three) radar ≥ 35 dBZ

Hourly rainfall from 3km ensemble (method three) cntl s3 s4 mean

Hourly rainfall probability from 3km ensemble (method three)

Hourly rainfall probability from 3km ensemble (method three)

3 km Ensembles – Method Four Initiate at 23Z March 28 Control run from 6km cntl s1/s2 using perturbation between 6km 12Z and control, s3/s4 using perturbation between 6km 06Z and control Run ADAS for each member, with NIDS and sat data Explicit microphysics

Surface reflectivity probability (method four) radar ≥ 35 dBZ

Hourly rainfall probability from 3km ensemble (method four)