Post on 12-Jan-2016
description
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)