On the ability of global Ensemble Prediction Systems to predict tropical cyclone track probabilities Sharanya J. Majumdar and Peter M. Finocchio RSMAS / University of Miami Acknowledgments: James Goerss, Buck Sampson (NRL Monterey), Sim Aberson, Tim Marchok (NOAA), Munehiko Yamaguchi (JMA and RSMAS), NOAA / National Hurricane Center Supported 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 Ike 2.Evaluation of ensemble mean in 2008 season 3.ECMWF / UKMET / EC+UK: mean + circles 4.ECMWF and GPCE: ranges of circle radii 5.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 67 th percentile of all track errors in the previous 5 years to define its circle radius (i.e. STATIC). 1

1 Hurricane Ike: ECMWF EPS

1 Hurricane Ike:

GPCE 1 Hurricane Ike:

NHC 1 Hurricane Ike:

1

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 1

Track errors: ECMWF ensemble mean vs deterministic 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). 2

3 Multi-model ensembles ECMWF UKMET NCEP

3 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 3

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. 3

4 Range of ECMWF 67% Circle Radii

4 Range of GPCE Circle Radii

4 ECMWF Ensemble Mean Errors

4 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. 4

Western North Pacific 2008

5 Ensemble Mean Errors: Atl, WNP

5 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 5

Remarks ECMWF EPS performed well in Atlantic in 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.