Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

Hourly RUC Convective Probability Forecasts using

Ensembles and Radar Assimilation

Steve WeygandtStan Benjamin

Forecast Systems LaboratoryNOAA

AUTOMATED CONVECTIVE WEATHER GUIDANCE

PRESENT

• 0-2 h forecasts from radar extrapolation with growth and decay (nowcasting techniques)

• Beyond 2 h guidance from model output helpful

FUTURE

A seamless convective guidance product utilizing a variety of inputs including nowcasts and model ensemble information to provide guidance to humans and automated decision support systems

Model-based Probability Forecasts for Convective Weather

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

Procedure:Aggregate model convective information to larger time/space scales (~1-2 h, 80-100 km)

• Scales should increase with increasing lead time• Scales will decrease as models get better

Ensembles provide technique for aggregating forecast information

Types of ensembles

• Multi-model ensembles

• Initial/boundary condition ensembles

• Model physics ensembles

• Time-lagged model ensembles (2004)

• Model gridpoint ensembles (2003)

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 60 km

RUC convective probability forecast


Threshold > 2 mm/3hLength Scale = 60 kmBox size = 7 GPs

7 pt, 2 mm

(2003 --gridpointensemble)

• Relative Operating Characteristic (ROC) curves

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

• “Left and high” curve best

Does probability beat model precip?

PO

D

POFD

----- probability----- conv precip

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%

Gridpoint Ensembles

Adjustable parameters

• Length scale

• Precipitation Threshold

Inherent weaknesses

• Constrained to single model run

• Non-zero probability can only extend out as far as the characteristic distance

More ensemble information

better probabilities

5 pt, 1 mm 7 pt, 2 mm

9 pt, 2 mm9 pt, 4 mm

% 10 20 30 40 50 60 70 80 90

Different box sizes and convectiveprecip.thresholdsgive differentprobabilityfields

Need to calculate statisticalreliabilityto calibrateprobabilities

25%

40%

Optimal threshold and length scale?


• Automated convective probability forecast

• Gridded fields derived from model ensembles

• Real-time forecasts started 2003 (RCPFv2003)

• Testing/improvements during 2004 (RCPFv2004)

• 2-, 4-, 6-h forecasts every 2 hours (CCFP guidance)

• Verification of forecasts by RTVS

• AWC evaluation of product during 2005

• Merge with short-range techniques (NCAR/MIT)

RUC Convective Probabilistic Forecast (RCPF) evolution


RUC Convective Probability Forecast

POD=0.55Bias = 1.4CSI = 0.305 pt, 1 mm / 3h, 40% thresh

Sample 2003 RUC product

Verification displayfrom RTVS

Threshold probability forecast to get a categorical forecast

• RCPF most useful for initial convectivedevelopment

2003 verification of RCPFv2003

Forecast length

RCPFv2003

6h Fcst

• RCPF bias too large all timesexcept evening

GMTEDT

Forecast Valid Time

Diurnal cycle of convection

Threshold probability forecast at 40% to get categorical forecast

Improvements to RCPF for 2004GOALS (maximize skill)

• Reduce large bias (diurnal effects, western differences)

• Improve spatial coherency, temporal consistency• Improve robustness• Reduce latency

ALGORITHM CHANGES• Increase filter size (9 GP east, 7 GP west)

• Time-lagged ensemble (multiple hourly projections from multiple RUC forecast cycles)

• Diurnal cycle for precip. thresh. (maximum daytime, minimum nightime; smaller value in the west)

• Increase forecast lead time one hour (eg: 6-h fcst from 13z valid 19z available at 1245z instead of 1345z)

Threshold adjusted to optimize the forecast bias

Diurnal variation of Precipitation Threshold Rate

ForecastValid Time

GMT

EDT

Higher threshold to reducecoverage

Lower threshold to increase coverage

West of 104 deg. longitude, multiply threshold by 0.6

- Threshold likely too low at night (bias still too large)

• Verification for 26 day period (6-31 Aug. 2004)

• RCPFv2004 fcst is a 1-h older than RCPFv2003

RCPFv2004 has similar CSI, much improved bias

Comparison of RCPFv2003 and RCPFv2004

Forecast length

ForecastValid Time

GMT

EDT

6h Forecast


.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

GMT

EDT


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) Fcst

Lead

Tim

e

Bias by lead-time, time of day

GMT

EDT


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

Fcst

Lead

Tim

e

40%

40%

CSI vs. bias for 2003 vs. 2004(6-h forecasts valid 19z)

• RCPFv2004 fcst is a 1-h older than RCPFv2003

RCPFv2004 has better CSI for given bias value

Points at 5% intervals

Low Probabilities

High Probabilities

13z convectionAt fcst

Time...

19z verif

RCPFv2004

Sample RCPFv2004 product

25 – 49%50 – 74%75 – 100%

Verification

19z NCWD

10 Aug 2004

13z + 6h Forecast

15z convectionAt fcst

Time...

RCPFv2004

15z + 6h Forecast

21z verif

Sample RCPFv2004 product

25 – 49%50 – 74%75 – 100%

Verification

21z NCWD

23 July 2004

RELIABILITY For all 60% fcsts, eventoccurs 60% of time (45 deg line)

RESOLUTION Strong change in obsfreq for given changein fcst probability(vertical line)

SHARPNESS Tendency for forecast probabilities to be nearextreme values (0%, 100%)(not hedging)

Tradeoffs between reliability, resolution, sharpness

FORECAST probability (/100)

OB

SE

RV

ED

fre

qu

en

cy

(/1

00)

Under forecast

Over forecast

Climatology

perfect r

eliabilit

y

Actualreliability

Interpreting Reliability Plots

RELIABILITY • Better reliability for 2004 vs. 2003

• Underfcst low prob., overfcst high prob.

• 2004 has many fewer 0% prob. pts that have convection

Fractional Coverage • 2004 has more low prob. pts, fewer high prob. pts

• 2004 has fewer 0% prob. pts (not shown)


OB

SE

RV

ED

fre

qu

en

cy

(/1

00)

Climatology

perfect r

eliabilit

y

RUC-NCWF 6-h fcsts valid 19z

Under

Over

0.100.080.060.040.020.00F

CS

T f

rac

t.a

real

co

ve

r.


6-31 Aug. 2004

ACTIVITIES FOR 2005

• Dissemination and evaluationRealtime use and evaluation by AWCHourly output and update frequencyNCAR password protected web-site

(model and radar extrapolation)

• Ongoing product developmentEnsemble-based potential echo top informationUse of ensemble cumulus closure informationUpgrade from 20-km RUC to 13-km RUCUse of other RUC fields

• Merge RCPF with NCWF2 (E-NCWF)

2005RCPF

16z + 8h Forecast

Sample RCPF 2005 product

25 – 49%50 – 74%75 – 100%

Verification

00z NCWD

8 Mar 2005CCFP

18z + 6h Forecast

Sample Probability/Echo Top Display

Probabilities shown with color shading

Potentialecho topheight shown with black Lines (kft)

-- Echo top from parcel overshoot level

-- Contour echo top height at desired interval(3kft or 6kft?)

Grell-Devenyi Cumulus Parameterization

• Uses ensemble of closures:- Cape removal - Moisture convergence- Low-level vertical mass flux- Stability equilibrium

• Includes multiple values for parameters:- Cloud radius (entrainment) - Detrainment (function of stability)- Precipitation efficiency (function of shear)- Convective inhibition threshold

PRESENT: Mean from ensembles fed back to modelFUTURE: Optimally weight ensembles closures,Use ensemble information to inprove probabilities

2 hr Nowcast(scale - 60 km)

Fo

rec

as

tP

erf

orm

an

ce

Closures groups in RUC Grell-Devenyi ensemble

cumulus scheme

Radar2100 UTC

10 July, 20029-h

fcst valid 21z 10 Jul 2002

STRENGTHS OF MODEL GUIDANCE

• Capturing initial convective development• Long lead-time and early morning forecasts

Improvements to the model and assimilation system lead directly toimprovements in probability forecasts

For RUC model:

• Assimilate surface obs throughout PBL• 13-km horizontal resolution (June 2005)• Radar data assimilation• Full North American coverage (2007)

ISSUES FOR MODEL GUIDANCE

• Short-range forecasts (spin-up problem)Poor performance for short-range forecastdoes not invalidate longer-range forecasts

• Propagation of convective systems

• Robustness (spurious convection, complete misses)

• Model bias issues

Differences for parameterized vs.explicit treatments of convection

Reflectivity: mosaic data

• NSSL pre-processing code transferred to NCEP • Integrate mosaic data into RUC cloud analysis• Couple to ensemble cumulus parameterization• Couple to model velocity fields

Radial Velocity: level II data • Generalized 3DVAR solver from lidar OSSE • Use horizontal projection of 3D radial velocity

Outstanding Issues

- Data thinning/superobbing - Quality Control (AP, 2nd trip, unfolding, birds,) - Optimal uses (clear-air, stratiform precip., t-storms)

RUC Radar Data Assimilation Plans

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)

Thoughts and questionsPredictability very limited for small-scale convective precipitation features

• Smoothing improves many scores• Smoothing alters spectra, probability information

Many “radar” approaches applicableto model forecast precipitation fields

• Probabilities from spatial variability of model precip.• Model depicts “displacement”, and “temporal evolution”• Apply “tracking” algorithms to model precipitation fields?

Many opportunities for blending model- andradar-based techniques

• Need extensive comparison to find “break even” points• Assess ability of radar and model for different tasks• Merge radar structure with model favored regions?

CONVECTIVE STORM TYPE

Squall-line

• Discernible from probability shape30%

50%

70%Not as clear for other shapes

• Scattered storms (high likelihood, 20% coverage)

• MCS (20% likelihood, significant coverage)

30%Storm-type affects correlation of adjacent probabilities,cumulative probability forflight track

How is the RCPF created?1. Gridpoint ensemble (for each model GP) - Fraction of 20-km model gridpoints within 9 x 9 box

with 1-h convective precipitation exceeding threshold(use 7 x 7 km box west of 104 deg. Longitude)

- Diurnal variation to 1-h convective precipitation threshold(smaller value for threshold west of 104 deg. longitude)

2. Time-lagged ensemble - Use up to six forecasts

bracketing valid time

- 9-h RUC forecast everyhour with hourly output

- 2-h latency to RUC modelforecast output

4-h RCPF inputs

M0+4 M1+5 M2+6M0+5 M1+6 M2+7

6-h RCPF inputs

M0+6 M1+7M0+7 M1+8

8-h RCPF inputs

M0+8 M1+9M0+9

M# = # hoursback to model initial time

Time-lagged ensemble inputs

Forecast Valid Time (UTC)

12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z 00z

RUC modelforecasts(HHz+F)

Init

ial

Tim

e 1

4z

15

z 1

6z

17

z 1

8z

19

z 2

0z

21

z 2

2z

23

z

12

z 1

3z

14

z 1

5z

16

z 1

7z

18

z 1

9z

20

z 2

1z

Availab

le

RCPF has 2h latency

2 4 6 8

14z+2,313z+3,412z+4,5

14z+4,513z+5,612z+6,7

14z+6,713z+7,8

14z+8,913z+9

2 3 4 5 6 7 8 9

15+2,314+3,413+4,5

15+3,414+4,513+5,6

15+4,514+5,613+6,7

15+5,614+6,713+7,8

15+6,714+7,813+8,9

15+7,814+8,913+9,10

15+8,914+9,1013+10,11

15+9,1014+10,1113+11,12

HHz = model intial time F = forecast length (h)

14z RCPF(16z

CCFP)

15z RCPF(17z

CCFP)

Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

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