/12Z /00Z /12Z /00Z SLP rising (TC weakening). ETC intensifying. A Technique to predict the outcome of extratropical transition E. A. Ritchie 1, J.S. Tyo 1, and O. Demirci 2 1 University of Arizona 2 University 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 61 o long. x 51 o lat. grid of 1 o 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

Training set - 70 cases of ET of 3000–D data at 9 different times from 1997 – Run eigenanalysis at each time  70 EOFs and PCs  each TC has a unique set of PCs  represent 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 pts

3. Find a unit vector, û 0, that maximises the separation (d’) of the two populations in 10-PC space. û 0 = ai + bj + ck + dl + … PC1 PC2 û0û0 PC1 PC2 û0û0

Now we can plot the probability distribution of the training data against the projection distance to û 0 And the corresponding Receiver Operating Characteristic (ROC) curve P D = TP. (TP+FN) P F = 1 - TN. (TN+FP)

End Images What is the technique actually “seeing” to do its prediction? PC1 PC2 û0û0

Height 700 mbWind 200 mbPotential 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 hPa hPa hPa hPa hPa hPa hPa hPa Divergence 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 hPa hPa hPa hPa hPa hPa hPa hPa Divergence 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 intensifiers Early, 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.