Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA
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Transcript of Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA
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RUC Convective Probability Forecasts using Ensembles and
Hourly Assimilation
Steve WeygandtStan Benjamin
Forecast Systems LaboratoryNOAA
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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
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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
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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
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RUC convective precipitation forecast
5-h fcst valid 19z 4 Aug 2003
3-h conv.precip. (mm)
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% 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)
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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
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• 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
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.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
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.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
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.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
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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
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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)
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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
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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
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• 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%
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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%
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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)