an improved wind probability program: a year 2 joint hurricane testbed project update mark demaria...
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An Improved Wind Probability Program:A Year 2 Joint Hurricane Testbed Project Update
Mark DeMaria and John Knaff, NOAA/NESDIS, Fort Collins, COStan Kidder, CIRA/CSU, Fort Collins, CO
Buck Sampson, NRL, Monterey, CAChris Lauer and Chris Sisko, NCEP/TPC, Miami, FL
Presented at the Interdepartmental Hurricane Conference
March 5, 2009
Monte Carlo Wind Probability Model
• Estimates probability of 34, 50 and 64 kt wind to 5 days• Implemented at NHC for 2006 hurricane season• Replaced Hurricane Strike Probabilities• 1000 track realizations from random sampling NHC
track error distributions• Intensity of realizations from random sampling NHC
intensity error distributions– Special treatment near land
• Wind radii of realizations from radii CLIPER model and its radii error distributions
• Serial correlation of errors included • Probability at a point from counting number of
realizations passing within the wind radii of interest
1000 Track Realizations 64 kt 0-120 h Cumulative Probabilities
MC Probability ExampleHurricane Ike 7 Sept 2008 12 UTC
Project Tasks
1. Improved Monte Carlo wind probability program by using situation-depending track error distributions
• Track error depends on Goerss Predicted Consensus Error (GPCE)
2. Improve timeliness by optimization of MC code
3. Update NHC wind speed probability product • Extend from 3 to 5 days• Update probability distributions (was based on
1988-1997)
Tasks 2 and 3 Completed
• Optimized code implemented for 2007 season– Factor of 6 speed up
• Wind Speed Probability Table – Calculated directly from MC model intensity
realizations– Implemented for 2008 season
Task 1: Forecast Dependent Track Errors
• Use GPCE input as a measure of track uncertainty
• Divide NHC track errors into three groups based on GPCE values– Low, Medium and High
• For real time runs, use probability distribution for real time GPCE value tercile– Different forecast times can use different distributions
• Relies on relationship between NHC track errors and GPCE value
Goerss Predicted Consensus Error (GPCE)
• Predicts error of CONU track forecast– Consensus of GFDI, AVNI, NGPI, UKMI, GFNI
• GPCE Input– Spread of CONU member track forecasts– Initial latitude– Initial and forecasted intensity
• Explains 15-50% of CONU track error variance • GPCE estimates radius that contains ~70% of
CONU verifying positions at each time• In 2008, GPCE predicts TVCN error
– GFS, UKMET, NOGAPS, GFDL, HWRF, GFDN, ECMWF
72 hr Atlantic NHC Along Track Error Distributions Stratified by GPCE
0
5
10
15
20
25
30
35
40
-600 -400 -200 0 200 400 600 800
Along Track Error (nmi)
Fre
qu
en
cy
(%
)
Lower GPCE Tercile
Upper GPCE Tercile
2008 Evaluation Procedure
• GPCE version not ready for 2008 real time parallel runs
• Re-run operational and GPCE versions for 169 Atlantic cases within 1000 km of land at t=0
• Qualitative Evaluation: Post 34, 50, 64 kt probabilities on web page for NHC– Operational, GPCE and difference plots
• Quantitative Evaluation: Calculate probabilistic forecast metrics from output on NHC breakpoints
GPCE MC Model Evaluation Web Page
http://rammb.cira.colostate.edu/research/tropical_cyclones/tc_wind_prob/gpce.asp
Individual Forecast Case Page
Tropical Storm Hanna 5 Sept 2008 12 UTC
34 kt 0-120 h cumulative probability difference field (GPCE-Operational)All GPCE values in “High” tercile
Hurricane Gustav 30 Aug 2008 18 UTC
64 kt 0-120 h cumulative probability difference field (GPCE-Operational)All GPCE values in “Low” tercile
Quantitative Evaluation
• Calculate probabilities at NHC breakpoints– Operational and GPCE versions
• 34, 50 and 64 kt• 12 hr cumulative and incremental to 120 h
– 169 forecasts X 257 breakpoints = 43,433 data points at each forecast time
• Two evaluation metrics– Brier Score– Optimal Threat Score
Operational and GPCE Probabilities Calculated at 257 NHC Breakpoints
West Coast of Mexicoand Hawaii breakpointsexcluded to eliminate zero or very low probability points
Brier Score (BS)
• Common metric for probabilistic forecasts
• Pi = MC model probability at a grid point (0 to 1)
• Oi = “Observed probability” (=1 if yes, =0 if no)
• Perfect BS =0, Worst =1• Calculate BS for GPCE and operational versions• Skill of GPCE is percent improvement of BS
2
1
][1
ii OPN
BSN
i
Brier Score Improvements2008 GPCE MC Model Test
0
1
2
3
4
5
6
7
8
9
10
12 24 36 48 60 72 84 96 108 120
Forecast Period
Bri
er
Sc
ore
Imp
rov
em
en
t (%
)
64 kt Cumulative
50 kt Cumulative
34 kt Cumulative
0
1
2
3
4
5
6
7
8
9
10
12 24 36 48 60 72 84 96 108 120
Forecast Period
Bri
er S
core
Imp
rove
men
t (%
)
64 kt Incremental
50 kt Incremental
34 kt Incremental
Cumulative Incremental
Threat Score (TS)
• Choose a probability threshold to divide between yes or no forecast
• Calculate Threat Score (TS)• Repeat for wide range of thresholds
– Every 0.5% from 0 to 100%• Find maximum TS possible• Compare best TS for GPCE and operational model runs
ab cForecast Area Observed Area
)( cba
aTS
Threat Score Improvements2008 GPCE MC Model Test
-6
-4
-2
0
2
4
6
8
10
12
12 24 36 48 60 72 84 96 108 120
Forecast Period (hr)
TS
Ove
rlap
Are
a In
crea
se (
%)
64 kt Incremental
50 kt Incremental
34 kt Incremental
Cumulative Incremental
-6
-4
-2
0
2
4
6
8
10
12
12 24 36 48 60 72 84 96 108 120
Forecast Period (hr)
TS
Ove
rlap
Are
a In
crea
se (
%)
64 kt Cumulative
50 kt Cumulative
34 kt Cumulative
Potential Impact of GPCE on Hurricane Warnings
• Automated hurricane warning guidance from MC probabilities under development– Schumacher et al. (2009 IHC)
• Warning algorithm run for Hurricane Gustav (2008)– Operational and GPCE
versions
Summary
• Code optimization and wind speed table product are complete– Implemented before 2007 and 2008 seasons
• GPCE-dependent MC model – Tested on 169 Atlantic cases from 2008 – Results are qualitatively reasonable– Improves Brier Score at all time periods relative to
operational MC model– Improves Threat Score at most time periods
• Not tested in the eastern and western Pacific