# Presentation: 663880

## A Technique to predict the outcome of 

**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**

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

**- 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 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, û0, that maximises the separation (d’) of the two populations in 10-PC space.

*û**0**  *= a***i*** + b***j*** + c***k*** + d***l*** + ...

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

- 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? **

## Dissipating Cases

**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 

## Divergence

|  | 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 |

**Divergence**

**Results for Temperature and Divergence **
**using EEOF**

## Divergence

|  | 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 |

**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.