19970628/12Z19970627/00Z 19970627/12Z 19970629/00Z

SLP rising (TC weakening). ETC intensifying.

A Technique to predict the outcome of extratropical transition

E. A. Ritchie1, J.S. Tyo1, and O. Demirci2

1University of Arizona2University of New Mexico

Acknowledgments: Office of Naval Research Marine Meteorology Program

Objective – develop a simple technique that adds value to the NWP forecasts during ET

Method:- ET is a very “visual” problem - use statistical pattern-recognition techniques

Initial attempt:- objectively distinguish ahead of time those TCs that will intensify from those that will dissipate during ET.

During S1 End of S1 36 h into S2

Peter 1997 (+)

Ivan 1997 (-)

ET + 00

- Data

- NOGAPS analyses interpolated to a 61o long. x 51o lat. grid of 1o resolution centered on the TC location

- TC location from JTWC best track data or from minimum sea-level pressure determined from NOGAPS analyses.

- Training Data - 70 ET Storms from 1997 – 2003 western N-Pacific

- Test Data – 27 ET Storms from 2004 - 2005

Training set - 70 cases of ET of 3000–D data at 9 different times from 1997 – 2003

1. Run eigenanalysis at each time70 EOFs and PCs each TC has a unique set of

PCsrepresent TCs by their PCs

2. The higher-order EOFs contain “noise” not relevant to our problem -> results in over-fitting of the data

a) retain largest 20 PCs (~98% of variance)

b) optimize over highest 20 PCs to get “most important” 10 PCs of these 20.

-> removes high-order information (over-fitting)

-> improves the robustness of the system.

61 pts

51 p

ts

3. Find a unit vector, û0, that maximises the separation (d’) of the two populations in 10-PC space.

û0 = ai + bj + ck + dl + …

PC1

PC2

û0

PC1

PC2

û0

Now we can plot the probability distribution of the training data against the projection distance to û0

And the corresponding Receiver Operating Characteristic (ROC) curvePD = TP . (TP+FN)

PF = 1 - TN . (TN+FP)

End Images

What is the technique actually “seeing” to do its prediction?

PC1

PC2

û0

Height 700 mb Wind 200 mb Potential Temp 850 mb

Dissipating Cases

Intensifying Cases

Multivariable – incorporate two variables at a single into the training set using EEOF, SVD analysis or a technique we call “3D-space” to replace the EOF analysis step

20% FA

Temperature (K)

100 hPa

200 hPa

300 hPa

500 hPa

700 hPa

850 hPa

925 hPa

1000 hPa

Alone 55.6 64.0 44.4 59.3 70.4 66.7 55.6 51.9

100 hPa 51.9 63.0 84.0 51.9 55.6 66.7 66.7 59.3 51.9

200 hPa 44.0 60.0 68.0 56.0 48.0 48.0 72.0 56.0 52.0

300 hPa 74.0 44.4 72.0 48.1 55.6 55.6 70.4 44.4 48.1

500 hPa 81.5 59.3 72.0 48.1 59.3 66.7 48.1 63.0 55.6

700 hPa 59.3 51.9 64.0 44.4 66.7 63.0 63.0 59.3 48.1

850 hPa 63.0 55.6 80.0 55.6 59.3 74.1 63.0 66.7 44.4

925 hPa 51.9 59.3 80.0 48.1 70.4 63.0 70.4 59.3 55.6

1000hPa 59.3 51.9 84.0 44.4 66.7 70.4 66.7 63.0 51.9

Div

erg

ence

Results for Temperature and Divergence using EEOF

20% FA

Temperature (K)

100 hPa

200 hPa

300 hPa

500 hPa

700 hPa

850 hPa

925 hPa

1000 hPa

Alone 55.6 64.0 44.4 59.3 70.4 66.7 55.6 51.9

100 hPa 51.9 63.0 80.0 44.4 44.4 66.7 66.7 59.3 59.3

200 hPa 44.0 60.0 64.0 44.0 72.0 56.0 64.0 52.0 48.0

300 hPa 74.0 66.7 80.0 40.7 55.6 66.7 63.0 51.9 33.3

500 hPa 81.5 59.3 68.0 44.4 55.6 70.4 70.4 59.3 48.1

700 hPa 59.3 63.0 76.0 51.9 55.6 74.1 66.7 63.0 51.9

850 hPa 63.0 55.6 72.0 40.7 63.0 81.5 63.0 70.4 55.6

925 hPa 51.9 59.3 68.0 59.3 59.3 63.0 70.4 55.6 59.3

1000hPa 59.3 55.6 72.0 44.4 63.0 66.7 66.7 55.6 48.1

Div

erg

ence

Results for Temperature and Divergence using SVD

Conclusions and Future Work

Incorporating multiple variables generally improves performance (measured by increased detection for same false-alarm rate).

Increase the Training Set substantially- improve utility by increasing the number of classes

discriminated:- Strong, Moderate, Weak intensifiers, dissipators

Fast, Moderate, Slow intensifiersEarly, Delayed intensifiers

- better representation of any individual storm - better representation of seasonal and interannual cycles in

the training set(Reanalysis data or use model to “create” training set Move away from model dependence by using remote-sensed

data that discriminate the two classes – e.g., surface winds, precipitation estimates.

System is “simple” – provides a yes or no decision – adds confidence to a NWP forecast.