Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed Project Status Report Mark DeMaria NOAA/NESDIS/ORA, Fort Collins, CO ohn A. Knaff, Jack Dostalek and Kimberly J. Mueller CSU/CIRA, Fort Collins, CO Collaborators : Jim Gross (TPC), Charles Anderson (CSU), Buck Sampson (NRL),Miles Lawrence(TPC), Chris Sisko (TPC) Presented at The Inter-Departmental Hurricane Conference March 3, 2004 Charleston, SC
Transcript
Improvements in Deterministic and Probabilistic
Tropical Cyclone Surface Wind Predictions
Joint Hurricane Testbed Project Status Report
Mark DeMariaNOAA/NESDIS/ORA, Fort Collins, CO
John A. Knaff, Jack Dostalek and Kimberly J. Mueller
CSU/CIRA, Fort Collins, CO
Collaborators: Jim Gross (TPC), Charles Anderson (CSU),Buck Sampson (NRL),Miles Lawrence(TPC), Chris Sisko (TPC)
Presented at The Inter-Departmental Hurricane ConferenceMarch 3, 2004 Charleston, SC
CLIPER error distributions– Randomly sample errors using Monte Carlo method
• Generate probabilities of 34, 50, 64 and 100 kt winds
Decay-SHIPS and NHC Intensity Forecast Skill 2001-2003
-30
-25
-20
-15
-10
-5
0
5
10
15
20
12 24 36 48 60 72 84 96 108 120
Forecast Interval (hr)
Err
or
Rel
ativ
e to
SH
IFO
R (
%)
Decay SHIPS
NHC Official
5 Basic Radial Profiles (Samsury and Rappaport 1991)
1
2
3
4
5
• Develop objective method for extracting similar information• Supplement with inner-core GOES data
Variational Wind Analysis for Aircraft Data
• Combine 12 hours of recon data in storm-relative coordinates
• Perform automated quality control– Analyze data to determine if coverage is sufficient
• Designed to measure at least azimuthal wavenumber 0 and 1 – Compare data to “pre-analysis” to eliminate bad points
• Perform “variational” analysis to provide u,v on radial, azimuthal grid – azimuthal smoothing >> radial smoothing– Based on Thacker and Long (1988)
• Preliminary prediction based upon azimuthal average tangential wind
AF Recon Flight Level Winds for Hurricane LiliEarth-Relative 10/02/02 0000-1200 UTC
AF Recon Flight Level Winds for Hurricane LiliStorm-Relative 10/02/02 0000-1200 UTC
Variational Wind Analysis for Lili10/02/02 0000-1200 UTC
Isotachs (kt) from Variational Wind Analysis for Lili10/02/02 0000-1200 UTC
Isotach Analyses for Hurricane Lili10/01 0000 UTC – 10/03 1200 UTC
0
10
20
30
40
50
60
0 20 40 60 80 100
120
140
160
180
200
Radius (km)
Tan
gen
tial
Win
d (
m/s
)
10/01 00
10/01 12
10/02 00
10/02 12
10/03 00
10/03 12
Azimuthally Averaged Tangential Wind (r=0 to 200 km)Hurricane Lili 10/01 00 UTC to 10/03 12 UTC
Comparison of Best Track and Variational Analysis Maximum Wind
(1995-2002 Cases)
y = 0.9743x - 0.1111
R2 = 0.903
0
20
40
60
80
100
120
140
160
180
0 20 40 60 80 100 120 140 160 180
Analysis Max Wind (kt)
Bes
t T
rack
Max
Win
d (
kt)
EOF Analysis
• ~400 cases with recon and IR data (95-03)• 51 radial grid points, r = 4 km• How to relate 102 IR and wind values to intensity
change?– Empirical Orthogonal Function (EOF) Analysis
– Mathematical technique for extracting common patterns from datasets
– Apply to tangential wind and IR radial profiles
– Work with small set of patterns instead of the entire profiles
Variance Explained by each EOF
0
10
20
30
40
50
60
70
80
90
1 6 11 16 21 26 31 36 41 46 51
Eigenvalue Number
Var
ian
ce E
xpla
ined
(%
)
Tangential Wind
IR Brightness TTang. Wind:99% w\ 6 EOF
IR Brightness T:99% w\ 4 EOF
Tangential Wind and IR EOFs
-0.2
0
0.2
0.4
0.6
0.8
2 22 42 62 82 102 122 142 162 182 202
Radius (km)
No
rmal
ized
Val
ue
EOF 1
EOF 2
EOF 3
-0.4
-0.2
0
0.2
0.4
0.6
2 22 42 62 82 102 122 142 162 182 202
Radius (km)
No
rmal
ized
Val
ue
EOF 4
EOF 5
EOF 6
-0.4
-0.2
0
0.2
0.4
0.6
2 22 42 62 82 102 122 142 162 182 202
Radius (km)
No
rmal
ized
Val
ue
EOF 1
EOF 2
EOF 3
EOF 4
Tang. Wind 1-3
IR 1-4
Tang. Wind 4-6
Part 1 Project Schedule
• Spring 2004: Develop statistical intensity model using EOF amplitudes – Provide adjusted SHIPS forecast based upon inner core
information
• Spring 2004: Compare neural network and regression techniques– Collaboration with Dr. Charles Anderson, CSU Computer Science
Department (Expert in Machine Learning Techniques)
• Summer 2004: Implement variational aircraft analysis at NHC/JHT
• Summer/Fall 2004: Test results on real-time forecasts
Preliminary Neural Network ResultsDependent data test with 1989-2002 Sample
Monte Carlo Model for Tropical Cyclone Surface Wind Probabilities
(Initial support from Insurance Friends of the National Hurricane Center)
• Calculate NHC track and intensity errors (along track and cross track) from multi-year sample
• Determine large set of tracks and intensities (realizations) centered around official forecast by randomly sampling from error distributions
• Estimate wind radii distributions from errors of radii-CLIPER model
• Calculate probabilities by number of times specified point comes with radii of specified wind speed relative to total number of realizations
• Run in real-time in 2003 season (starting August)
Monte CarloWind ProbabilityModel
Example:
Hurricane Fabian
Aug 31 2003 18Z
Vmax=115 kt
R34 100 75 75 100R50 30 30 30 30R64 20 20 20 20
Modifications based on 2003 Results
• Model Changes– Improved portable random number generator– Complete error field sampling (instead of 1-99th percentiles)– Modified for use in the Atlantic, East/Central Pacific, and western North
Pacific basins (i.e., Longitude … 0-360)– Option for 100 kt radii added for JTWC
• Error Components– Improved radii-CLIPER model
• Inclusion of initial wind radii asymmetries – radii match observed at t=0 hr
• R34 bias correction
– Intensity errors account forecast intensity and distance to land– Distributions being updated with 2003 cases
Old New
Impact of Model Changes(Fabian 2003 Example)
3.34
1.62
0.600.33
0.18
0.06
0.02
15.759.53
4.452.96 2.45
1.09
0.48
0.01
0.1
1
10
100
10 100 1000 10000 100000
Number of Realizations
Pro
ba
bil
ity
Err
or
(%) Average Maximum
Effect of Number of Realizations on Probability Estimate
