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

OUTLINE• Deterministic Intensity Forecast Improvements

– Can inner core data from aircraft and satellite improve SHIPS forecasts?

• Automated objective analysis and EOF analysis

– Compare neural network and linear regression models

• Probabilistic Surface Wind Forecast Improvements– Calculate operational track/intensity and wind radii-

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

Sensitivity to the number of realizations

N=500 N=1000

N=2000 N=500000

R34 R50

R64 R100

TyphoonMaemi

9/9/04 06 Z

Vmax=115 kt

R34 130 130 130 130R50 50 50 50 50R100 20 20 20 20

N=2000

Part 2 Project Schedule

• Spring 2004: Investigate variable grid options – Improve efficiency and for NDFD applications

• Spring 2004: Finalize probability model for 2004 season

• Summer/Fall 2004: Run at NHC in real-time for Atlantic and East Pacific cases

• Summer/Fall 2004: Coordinate with JTWC for real-time tests (directly on their ATCF)

• Winter 2004: Evaluate results from 2004 runs

• 2004 “Freebie”: Provide NHC and JTWC with updated Radii-CLIPER models