RUC Convective Probability Forecasts using Ensembles and

Hourly Assimilation

Steve WeygandtStan Benjamin

Forecast Systems LaboratoryNOAA

Background on Rapid-Update Cycle

Background

Fields

1-hrfcst

1-hrfcst

1-hrfcst

11 12 13Time (UTC)

AnalysisFields

3DVAR

Obs

3DVAR

Obs

• U.S. operational model, short-range applications,situational awareness model

• Used by aviation, severe weather and general forecast communities

• 1-h update cycle, many obs types including:ACARS, profiler, METAR, radar

• Full cycling cloud/precip

• Grell/Devenyi ensemble cumulus parameterization

Benjamin, Thurs. 9:30 talk

Research BackgroundProblem:Thunderstorm likelihood information needed by aviation traffic community for strategic planning (Collaborative Convective Forecast Product)

Goals:Utilize outputs from RUC hourly output cycle to provide guidance for aviation forecasters.

Blend model-based probabilities with observation-based probabilities (Pinto, next talk)

Collaboration:NCAR Research Applications Lab (Mueller, Poster 5.21)National Weather Service Aviation Weather Center

Model-based Convective Probability Forecasts

Principle:Convective forecasts at specific model grid points from a single deterministic model run less likely to be correct than ensembles of model outputs.

Ensemble Approaches:• Adjacent model gridpoints (2003)• Time-lagged ensembles (2004)• Cumulus parameterization closures

Procedure:• Use model 1-h parameterized precipitation• Specify length-scale and precipitation threshold • Bracketing 1-h model outputs from successive cycles

RUC convective precipitation forecast

5-h fcst valid 19z 4 Aug 2003

3-h conv.precip. (mm)

% 10 20 30 40 50 60 70 80 90

Prob. ofconvectionwithin 120 km

RUC convective probability forecast

5-h fcst valid 19z 4 Aug 2003

Threshold > 2 mm/3hLength Scale = 120 kmBox size = 7 GPs

7 pt, 2 mm

(gridpointensemble)

Time-lagged ensemble

Model InitTime Eg: 15z + 2, 4, 6 hour RCPF

forecast

Forecast Valid Time (UTC)

11z 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z

13z+4,512z+5,611z+6,7

13z+6,712z+7,8

13z+8,912z+9

RCPF2 4 6

18z

17z

16z

15z

14z

13z

12z

11z 6 7

5 6 7 8 9 10

4 5 6 7 8 9

Model runs used

model has 2h latency

• Precipitation threshold adjusted diurnally and regionally to optimize the forecast bias

• Use smaller filter length-scale in Western U.S.

ForecastValid Time

GMT

EDT

Higher threshold to reducecoverage

Lower threshold to increase coverage

Multiply threshold by 0.6 over Western U.S.

Bias corrections

.24, .25

.22, .23

.20, .21

.18, .19

.16, .17

.14, .15

.12, .13

.10, .11

CSI by lead-time, time of day

ForecastValid Time

Diurnal cycle of convection

6-h

4-h

2-h

6-h

4-h

2-h

6-h

4-h

2-h

RC

PF

v200

4R

CP

Fv2

003

CC

FP

(Verifiation 6-31 Aug. 2004)

FcstLeadTime

GMT

.24, .25

.22, .23

.20, .21

.18, .19

.16, .17

.14, .15

.12, .13

.10, .11

CSI by lead-time, time of day

ForecastValid Time

Diurnal cycle of convection

6-h

4-h

2-h

6-h

4-h

2-h

6-h

4-h

2-h

RC

PF

v200

4R

CP

Fv2

003

CC

FP

(Verifiation 6-31 Aug. 2004)

FcstLeadTime

GMT

Quick

spi

n-up

1

8z in

it

.24, .25

.22, .23

.20, .21

.18, .19

.16, .17

.14, .15

.12, .13

.10, .11

CSI by lead-time, time of day

ForecastValid Time

Diurnal cycle of convection

6-h

4-h

2-h

6-h

4-h

2-h

6-h

4-h

2-h

RC

PF

v200

4R

CP

Fv2

003

CC

FP

(Verifiation 6-31 Aug. 2004)

FcstLeadTime

GMT

Quick

spi

n-up

1

8z in

it

Bias by lead-time, time of day

6-h

4-h

2-h

6-h

4-h

2-h

6-h

4-h

2-h

2.75-3.0

2.5-2.75

2.25-2.5

2.0-2.25

1.75-2.0

1.5-1.75

1.25-1.5

1.0-1.25

0.75-1.0

0.5-0.75

v200

4v2

003

CC

FP

(Verifiation 6-31 Aug. 2004)

ForecastValid Time

Diurnal cycle of convection

FcstLeadTime

GMT

2005 Sample RCPF and CCFP

25 – 49%50 – 74%

75 – 100%

Verification

00z 8 Mar 2005

NCWD

CCFP

18z + 6h Forecast

RCPF

Verification from FSL Real-Time Verification System (Kay, Thurs. 12:48 talk)

Height (ft x 1000)

RUC 4-h Forecast Potential Echo Top

ObservedComposite

RadarReflectivity/

EchoTops

38

26

37

22

36 25

5343

4345

37

3855 57

44

50

5139 33

2733

2734

57

56

3635

45

45

52

A-SM-ConCAPEGrell

Use of Ensemble Cumulus Closure Information

Normalized 1-h avg. rainratesFrom different closure groups

VERIFICATION

2100 UTC26 Aug 2005

RCPF 8-h fcst

• Relative Operating Characteristic (ROC) curves

• Show tradeoff: “detection” vs. “false-alarm”

• “Left and high” curve best

Does gridpoint ensemble add skill?

PO

D

POFD

----- gridpoint ensemble----- deterministic forecast

Sample: 5-h fcst from

14z 04 Aug 2003

Low prob

Low precip

High precip

High prob

det

ecti

on

false detection

9 pt, 4 mm

25%

CSI = 0.22Bias = 0.99

RCPF – 20 AUG ’05 11z+8h

Scores for 40% Prob.

NCWD valid 19z 20 AUG 05

RCPF20

RCPF13

CSI = 0.15Bias = 1.19

25 – 49%50 – 74%

75 – 100%

Sample 3DVAR analysis with radial velocity

500 mb Height/Vorticity

*Amarillo, TX

DodgeCity, KS

*

*

AnalysisWITHradial

velocity

**

Cint =2 m/s

**

Cint =1 m/s

K = 15wind

Vectors

and speed

0800 UTC 10 Nov 2004

Dodge City, KS

Vr

Amarillo, TX

Vr

*

*

Analysisdifference

(WITH radial

velocity minus

without)