Examination of the Dominant Spatial Examination of the Dominant Spatial Patterns of thePatterns of the

Extratropical Transition of Tropical Cyclones Extratropical Transition of Tropical Cyclones from the 2004 Atlantic andfrom the 2004 Atlantic andNorthwest Pacific SeasonsNorthwest Pacific Seasons

By David KofronBy David Kofron

ATMO529ATMO529

OutlineOutline• Brief description of ET

• Forecasting challenges

• Data description and preparation

• Results of SVD

• Discussion of results

• Future Work

What is ET?What is ET?• Extratropical Transition (ET) = the evolution of a decaying tropical cyclone (TC) into

an extratropical cyclone

• Characterized by poleward propagation, acceleration by mid-latitude westerlies (in

general, a change from tropical to mid-latitude environment), and an interaction with

trough or preexisting baroclinic cyclone

• Environmental changes include …

• Increased baroclinicity and vertical wind shear

• Decreased SST’s with an increase in the gradient of SST

• Landfall can cause an increase in surface drag, decrease in surface fluxes of latent and sensible heat,

and orographic effects

• Structural changes of the TC include …

• Decrease in maximum wind speed accompanied by expansion of the radius of maximum wind speed

(typically resembling the size of extratropical cyclone)

• Increased translation speed

• Increase in wave swell due to increased radius of winds and translational speedFrom Jones et. al. 2003

Defining the ET Life CycleDefining the ET Life Cycle

• Consists of two stages: Transformation and Reintensification

• ET period from onset of transformation stage (some use the end of this

stage as the beginning) to the end of the reintensification stage

• Transformation complete when storm resembles baroclinic cyclone on

satellite imagery and analysis plots

• The completion of the transformation stage known as “ET-time”

• “ET-time” can be found using different methods

From Klein et. al. 2000

Why is ET important?Why is ET important?

• ET forecasts are generally poor because …

• Tropical-extratropical interactions not well resolved by numerical models leads to poor forecasting of

storm track (speed and location) and intensity (maximum wind and storm size)

• Heavy precipitation in storms cause major localized flooding that is not well forecast

• Oceanic response (e.g. wave size and swell)

• Because the storms are no longer tropical, there are no responsibilities for the

tropical offices to issue warnings and watches, so storms are often overlooked by local

forecast offices

• Most issues have been better resolved due to the increase in research and

operations, however ET is still not well understood and there is no common definition

(in other words, there are still many improvements that can be made)

• An examination of the dominant spatial patterns may reveal what features are most

important to ET

From Jones et. al. 2003

The DataThe Data• Navy Operational Global Atmospheric Prediction System (NOGAPS)

reanalyses at 1° resolution for 12 hour time intervals for 500 hPa geopotential

heights

• Interpolated the data down to 0.5° resolution

• Found the exact center of the storm using the mean sea level pressure

analyses

• Extracted a 50.5° x 60.5° storm centered grid box (spatial dimension n = 12221)

• Used data for 2004 storms in North Atlantic and Northwest Pacific basins (26

storms = time dimension m)

• Time centered the data around “ET-time” for ± 72 hours (13 times); ET-time in

this case is when the TC first becomes an open wave in 500 hPa analyses using

20 m contour interval

• Ran SVD routine 13 times for a 26 x 12221 matrix of 500 hPa anomalies

-24 hr

+12 hr

-12 hr

+24 hr

ET-time

Example

Eigenvalue SpectraEigenvalue SpectraExplained Variance and Error Bars for ET-Time - 48 hr

0

0.1

0.2

0.3

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0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue No.

Expl

aine

d Va

rianc

e

Explained Variance and Error Bars of ET-Time -24 hr

0

0.1

0.2

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0 1 2 3 4 5 6 7 8 9 10

Eigenvalue No.

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Explained Variance and Error Bars of ET-Time -00 hr

0

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Eigenvalue No.

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Explained Variance and Error Bars of ET-Time +24 hr

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0.2

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Eigenvalue No.

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The Results – 1The Results – 1stst EOF EOF

-24 hr 68.7%-36 hr 66.5%-48 hr 46.7%

-12 hr 66.3% ET-time 61.8% +12 hr 67.6%

The Results – 2The Results – 2ndnd EOF EOF

-24 hr 9.5%-36 hr 11.3%-48 hr 20.6%

-12 hr 11.1% ET-time 13.9% +12 hr 11.6%

The Results – 3The Results – 3rdrd EOF EOF

-24 hr 7.0%-36 hr 6.2%-48 hr 9.7%

-12 hr 7.9% ET-time 8.7% +12 hr 6.0%

DiscussionDiscussion• The 1st and 2nd are significant by the North Test (possibly the 3rd, as well)

• The first 3 EOF’s explain approximately 80% of the variance

• There is a large jump in the variance explained by the 1st EOF after 48 hours prior to ET-time

• The dominant spatial pattern is most likely the mean height gradient between the tropics and mid-

latitudes

• The jump in variance explained could be due to a general change in the pattern about 2 days prior to

ET

• There is a large decrease in the variance explained by the 2nd EOF after 48 hours prior to ET-

time

• The dominant spatial pattern is most likely associated with the mid-level divergence in the exit region

of the trough that is located to the northwest of the TC [Note: the anomaly propagates to the south and

west indicating that the TC generally moves into the low to the east]

• The decrease in variance explained could mean that the divergence in the trough is more important

earlier with the TC-extratropical interaction

• The values of variance explained by the 3rd EOF generally is between 5% and 10%

• The dominant spatial pattern is most likely represents the troughs to the northwest and the northeast;

the TC can be accelerated into either trough

Future ResearchFuture Research

• Analyze the existing definitions of ET in order to determine a better way to define

“ET-time” that can be used for research or operational purposes

• Perform the same SVD analysis to potential vorticity (PV), which is a variable that

can explain the dynamics and thermodynamics of the atmosphere using one field

• Include a larger data set (e.g. 30+ years worth) in order to reduce “noise”

• Apply some statistical analysis in order to include only significant storms

• Create a forecast model to more accurately predict the storm track and intensity

• Demirci, O., “A project pursuit application to predict extratropical transition of tropical cyclones,” Ph.D.

dissertation, Univ. New Mexico, Albuquerque, 2006.

• Jones, S., P. Harr, J. Abraham, L. Bosart, P. Bowyer, J. Evans, D. Hanley, B. Hanstrum, R. Hart, F.

Lalaurette, M. Sinclair, R. Smith, and C. Thorncroft, 2003: The Extratropical Transition of Tropical Cyclones:

Forecast Challenges, Current Understanding, and Future Directions. Wea. Forecasting, 18, 1052-1092.

• Klein, P., P. Harr, and R. Elsberry, 2000: Extratropical Transition of Western North Pacific Tropical

Cyclones: An Overview and Conceptual Model of the Transformation Stage. Wea. Forecasting, 15, 373-395.

• Sinclair, M.R., 2002: Extratropical Transition of Southwest Pacific Tropical Cyclones Part I: Climatology and

Mean Structure Changes. Mon. Wea. Rev., 130, 590-609.

ReferencesReferences

Questions??Questions??