On the ability of global Ensemble Prediction Systems to predict

tropical cyclone track probabilitiesSharanya J. Majumdar and

Peter M. FinocchioRSMAS / University of Miami

Acknowledgments: James Goerss, Buck Sampson (NRL Monterey), Sim Aberson, Tim Marchok (NOAA), Munehiko

Yamaguchi (JMA and RSMAS), NOAA / National Hurricane CenterSupported by US Office of Naval Research and NOAA Hurricane

Forecast Improvement Project

Typhoon Workshop, Tokyo, Japan, 11/30/09

Motivation

Seek to provide accurate, situation-dependent, probabilistic track forecasts.

More accurate watches and warnings, timely evacuations, emergency management preparation.

Hope to reduce “overwarning”.

Ensemble Prediction Systems (EPS)

• Many centers emphasize the use of EPS for probabilistic TC track prediction.

• The TIGGE CXML database offers the opportunity for creation and evaluation of new TC track products with multiple EPS.

• Our first step: probability circles.

• Majumdar & Finocchio 2009, Wea. Forecasting, in press.

Outline

1. Case illustration: Hurricane Ike2. Evaluation of ensemble mean in 2008 season3. ECMWF / UKMET / EC+UK: mean + circles4. ECMWF and GPCE: ranges of circle radii5. ECMWF and GPCE: Atlantic and western North

Pacific Basins

EPS vs GPCE vs NHC• For EPS, we only use ensemble spread.• Define X% circle radius as that which encloses X% of the

ensemble members, centered on mean.

• Goerss Predicted Consensus Error (GPCE) uses a combination of predictors: spread of deterministic models (no EPS), initial and forecast TC position and intensity, number of consensus models. Importantly, it includes information on historical errors. Used at JTWC and NHC.

• NHC uses the 67th percentile of all track errors in the previous 5 years to define its circle radius (i.e. STATIC).

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1

Hurricane Ike: 2008090900ECMWF EPS

ECMWF EPS

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Hurricane Ike: 2008090900

GPCE

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Hurricane Ike: 2008090900

NHC

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Hurricane Ike: 2008090900

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Conclusions 1

• EPS may add new information to deterministic model consensus

• ECMWF EPS 67% probability circle appears qualitatively reasonable

• ECMWF EPS circle is smaller than GPCE and NHC in this example

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Track errors: ECMWF ensemble mean vs deterministic

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2008 season average: only TS strength and higher

Conclusions 2

• ECMWF ensemble mean is superior to all models except deterministic ECMWF.

• Comparable to ensemble mean of deterministic models (TVCN).

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3

Multi-model ensembles

ECMWFUKMETNCEP

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Number of Cases: 2008 season

Ensemble mean errors: ECMWF / UKMET / E+U

3

UKMET

ECMWF

% of cases in which best track falls within 67% circle

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Conclusions 3

• ECMWF ensemble mean superior to UKMET ensemble mean.

• Mean of combined ECMWF+UKMET ensemble generally not improved over ECMWF.

• Addition of UKMET ensemble does produce more low-error forecasts.

• Best track falls within circles more often for storms in straight-line motion than recurvers.

• Circle sizes well correlated with error for ECMWF, not so for UKMET.

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4

Range of ECMWF 67% Circle Radii

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Range of GPCE Circle Radii

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ECMWF Ensemble Mean Errors

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ECMWF 67% Circle Evaluation

Conclusions 4

• ECMWF circles are generally smaller than GPCE circles, although the range is wider.

• A 20-member ECMWF ensemble possessed similar results to a 50-member ensemble.

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Wes

tern

Nor

th P

acifi

c 20

08

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Ensemble Mean Errors: Atl, WNP

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ECMWF 67% Circles and GPCE: Atl and WNP

Conclusions 5

• GPCE over-dispersive in Atlantic in 2008• Western North Pacific forecasts were much less

skillful than Atlantic forecasts in 2008• CONW lower skill than ECMWF ensemble mean• ECMWF and GPCE failed to capture the best track

an adequate number of times in W. North Pacific

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Remarks• ECMWF EPS performed well in Atlantic in 2008. • Accurate ensemble mean is necessary.• For ensemble mean and simple circles, 20

members may be enough.• Some EPS tend to be under-dispersive.• Combine with static information to avoid

forecast-to-forecast fluctuations?• Ensemble perturbation technique is important.

Future Work

• Combine more EPS: NOGAPS, JMA, NCEP etc for active 2009 western North Pacific season.

• Detailed investigation of characteristics of ensemble perturbations (Yamaguchi).

• Along- and cross-track errors: “probability ellipses” using EPS.