Journal 01 ~eMeteorological Society 01 Japan, Vol. 78, No.6, pp. 777-788, 2000 777

Typhoon Forecast with the GFDL Hurricane :Model:

Forecast Skill and Comparison of Predictions- using

AVN and NOGAPS Global Analyses

By Chun-Chieh Wu

Department of Atmospheric Sciences, National Taiwan Universitl~, Ta:ipei, Taiwan

Morris A. Bender and Yoshio Kuriharal

Geophysical Fluid Dynamics Laboratory, NOAA, Princeto11, NJ, U.S.A.

(Manuscript received 26 October 1999, in revised form 17 August 2000)

Abstract

A hurricane model developed at GFDL, NOAA, was combined with each of A"N and NO GAPS globalanalyses to construct typhoon prediction systems GFDS and GFDN, respl~ctively. The GFDS systemperformed 125 (178) forecast experiments for 16 (24) storms in the western North Pacific basin during1995 (1996). It exhibited considerable skill in the forecast of tropical cyclone tracks. The average forecastposition errors at 12, 24, 36, 48 and 72 h in 1995 (1996) were 95 (108), 146 (178), 193 (227), 249 (280),and 465 (480) km. The improvement with GFDS in the typhoon position forE~cast over CLIPER wasroughly 30 %. The reduction of position errors in both average and standard deviations indicates superiorforecast accuracy and consistency of GFDS, although there existed systematic; northward bias in the forecastmotion at low latitudes. On the other hand, intensity forecast was not satisfactory, showing a tendency tooverpredict weak storms and underpredict strong storms, similar to the tendency in the Atlantic.

Two sets of forecasts performed in the 1996 season, the one by GFDS ,I.nd the other by GFDN, werecompared with each other. Forecast skills of the storm position with the 1~\1ro systems were comparable.However, the two forecast positions tended to be systematically biased toward different directions. As aresult, when the two forecasts were averaged, the mean error was 10 % smaller than that of each forecast.Also, overall improvement in track forecast was obtained in supplemental experiments in which individualforecasts were corrected for systematic biases. Though systematic bias is not steady, there may be ways toutilize it for improvement of tropical cyclone forecasts.

1. Introduction

Improvement in tropical cyclone forecasting rep-resents one of the greatest challenges in numericalweather prediction. A hurricane forecast system wasdeveloped at NOAA's (National Oceanic and Atmo-spheric Administration) Geophysical Fluid Dynam-ics Laboratory (GFDL) to provide forecast guid-ance on storms in the Atlantic basin. The sys-tem, an official operational hurricane prediction tool

of the U.S. National Weather Service (NWS) since1995, consists of the IGFDL's primitive equation hur-ricane model, a model initialization scheme, theglobal analysis calh~d AVN (Derber et aI. 1998)of the National Center for Environmental Predic-tion (NCEP), and the storm information from theNational Hurricane Center. Its remarkable perfor-mance has been demonstrated in Atlantic hurricaneprediction. For example, the mean track forecast er-rors for 255 cases in 1995 in the Atlantic at 36, 48,and 72 hours were rl~duced by 14, 19, and 25 %, re-spectively, relative to the next best NWS dynamicalprediction model (Kurihara et aI. 1998).

Ideally, an impro\Jed hurricane prediction systemshould demonstrat4~ improvement in forecast skill inall cases in all OC4~an basins. The GFDL systemw;t.<; exnerimentallv ,~un in 1995 for 125 cases in the

1

Corresponding author: Chun-Chieh Wu, Department ofAtmospheric Sciences, National Taiwan University, 61,Ln. 144, Sec. 4, Keelung Rd., Taipei 10772, Taiwan. E-mail: cwu@typhoon.as.ntu.edu.twCurrent affiliation: Frontier Research System for GlobalChange, Sumitomo-Hamamatsucho Bldg. 4F, Minato-ku,Tokyo, 105-0013, Japan.@2000, Meteorological Society of Japan

778 Journal of the Meteorological Society of Japan Vol. 78, No.6

model spans 75° IcLtitude by 75° longitude, witha triply-nested grid sys!lem with resolutions of 1°,1/3°, and 1/6° (TcLble 1 of Bender et al. 1993). Theoutermost domain extends from 15°S to 600N in themeridional direction (for GFDN, the northern andsouthern boundaries may sometimes shift slightlysouthward), and vclfies in the zonal direction de-pending on the storm's I::ondition at initial time.

The time integration for each GFDS (GFDN)forecast was carried out for 72 hours using the lat-eral boundary values which were updated hourly bylinear interpolation of the forecasts from the A VN(NOGAPS) global spec1;ral model (Kurihara et al.1989). In 1995 ea(:h GFDS experiment run withinitial times of 00 or 1~~ UTC, while in 1996 withinitial times of 06 or 18 UTC. The same storm infor-mation was used f(.r the storm vortices initialization(Kurihara et al. 199:» in GFDN and GFDS systems.

Numbers of GFDS foJrecast made in the westernNorth Pacific basin in 1.995 (1996) were 125 (178)for 16 (24) storms that formed during the months ofJuly through early r~ovember (late December). Thelists of GFDS forecclSts made in 1995 and 1996 aresummarized separ~Ltely in Tables 1 and 2.

2.2 Forecast verificationData for verifying the track position and intensity

forecast were obtained from the real-time tropicalcyclone message from the Joint Typhoon WarningCenter (JTWC). (The s~LIlle messages were used forspecifying the initi"l vortex in the model.) Testswere conducted to verify both GFDS and GFDNtrack forecasts ag;~inst either the JTWC positionfixed at real time or the JTWC post.analysis best-track position. Very little differences were found inthe mean position error. Verification of GFDS fore-casts was not made ".hen JTWC ceased its forecastsbecause storms had ma<ie landfall, or had becomeextra-tropical disturban(:es.

To evaluate the pE~rformance of GFDS, various er-ror statistics were (;omputed for all cases in Tables 1and 2. They include the mean forecast position errorand its standard deviation for showing the forecastaccuracy and consistency, distribution of systematicforecast bias, examination of error scatter, and eval-uation of the intensity foJ~ecast. Also, for 72 homoge-neous cases in 1995, GFDS forecasts were comparedagainst the AVN and NOGAPS global model fore-casts, and the official JTWC forecasts. These modelforecasts were also comp~Lred against the CUPER (asimple model based on climatology and persistence)as a means of evalu~Lting forecast skill.

3. Results from forecast experiments in 1995

3.1 Average error and (:omparison with other modelsThe average forec:~t position errors (standard de-

viations) of GFDS in 1995 at 12, 24, 36, 48 and 72 hforecast times were !}5 (63), 146 (87), 193 (131), 249

western North Pacific basin (hereafter, referred to asGFDS), where typhoons evolve in an environment ofhigher sea surface temperature, warmer and morehumid air than the Atlantic and monsoon circula-tion.

In 1996, the U.S. Navy implemented a new op-erational system of typhoon prediction in whichthe GFDL model was combined with the NOGAPS(Navy Operational Global Atmospheric PredictionSystem) global analysis (Goerss and Phoebus 1992;Goerss and Jeffries 1994). This system (hereafter,referred to as GFDN) had been successfully tested inthe preceding season for a limited number of westernNorth Pacific cases with the result indicating thatmean track forecast accuracy was similar to that ofGFDS through 48 h forecast period. The GFDSwas also run in 1996 in a parallel mode with GFDNfor 178 cases, which is 75 % of the total number ofGFDN forecasts. The parallel forecast experimentsfor such a large number of cases could not be re-peated in subsequent years due to limited computeravailability at NCEP.

Rennick (1999) analyzed the GFDN performancein 1996. In this study, typhoon forecasts usingGFDS in 1995 and 1996 are analyzed to make moreextensive evaluation of the GFDL model perfor-mance. Also, forecasts with GFDS in 1996 are com-pared against those with GFDN. Various factors cancontribute to the spread of forecasts, such as dif-ference in the environmental conditions at initialtime, difference in specification of initial storm vor-tex, difference in forecast model, and combinationof these. Differences in the initial environmentalcondition and time-dependent lateral boundary val-ues caused a forecast difference between GFDS andGFDN. In this respect, our comparison study is dif-ferent from others, e.g., Rennick (1999), Zhang andKrishnamurti (1999) and Goerss (2000).

In the present analysis, systematic biases in fore-casts with GFDS and GFDN are emphasized. Anensemble forecast using these two systems is at-tempted as well. Furthermore, supplemental ex-periments are carried out to suggest applicability ofinformation on systematic bias to operational fore-casts.

In Section 2, the forecast experiment and thedata used in the forecast evaluation are described.The results of error analysis for the 1995 and 1996typhoon seasons are respectively presented in Sec-tions 3 and 4. Comparison of forecasts betweenGFDN and GFDS are included in Section 4. Sum-mary and remarks are made in Section 5.

2. Forecast experiments and verification data

2.1 Forecast experimentThe GFDL multiply-nested movable mesh model

(Kurihara et al. 1995) was used in GFDN and GFDSforecasts. The integration domain of the GFDL

December 2000 c.-c. Wu, M.A. Bender and Y. Kurihara 779

Table 1. List of all GFDS forecasts in the western North Pa<:iJic basin in 1995.

I STORM NA~~ I NUMBER OF CASES I DATES OF FORECASTS)

FAYE (WP05) 11 1 JULY 18 -JULY 23GRAY (WP07) 3 I JULY 29 -JULY 30HELEN (WP08) 6 AUGUST 8 -AUGUST 11IRVING (WP09) 3 AUGUST 17 -AUGUST 18JANIS (WPI0) 9 I AUGUST 22 -AUGUST 26KENT (WPI2) 8 AUGUST 27 -AUGUST 30LOIS (WPI3) 4 AUGUST 27 -AUGUST 29OSCAR (WPI7) 9 SEPT. 12 --:,EPT. 16POLLY (WPI8) 5 SEPT. 16 -::,EPT. 20RYAN (WPI9) 12 SEPT. 17 --:,EPT. 23SIBLY (WP20) 7 SEPT. 28 --I:)CTOBER 2VAL (WP25) 1 OCTOBER 10WARD (WP26) 9 OCT. 18 -OCT. 22YVETTE (WP27) 5 OCT. 23 -('CT. ;ZACK (WP28) 13 OCT. 25 --r;;~lANGELA (WP29) 20 OCT. 26 -!';'Q"V:--:-

TOTAL 125 JUL 18 -~IOVEMBER 4 I

Table 2. List of all GFDS forecasts in the western North PacifIc basin in 1996.

L~~- ~~ME I NUMBER OF CASES I DATES OFFORECASTS IDAN (WP06) 3 JULY 06 -JULY 08 rEVE (WP07) 11 JULY 13 -JUL~_~GLORIA (WP09) 5 JULY 22 -JULY 25HERB (WPI0) 9 JULY 24 -JULY 31

(WPll) I 2JOY ~!:_~)_l!

'_~IRK (WPI3) 115 _II NIKI (WP18) 1 5 I

_~qN (WP19) I 17 i(WP23) ~ 6 SEP. 05 -SEt>: 08

TOM (WP25) 8 SEP. 11 -SE:P. 17VIOLET (WP26) 11 SEP. 12 -SEP. zn

[ABEL (WP30)I WP31

22

-'.

13 -OCT. 16---~I

at;.. .." -a(~I aC1"~~=-O(JT. ~

~

OCT:2i=-(ji)T.25NOVEMBER 021I WP35

DALE (WP36) 11 NOV.O4- NOV.12I ERNIE (WP37) 13 NOV. 04 -NOV. 16

FERN (WP42) 8 DEC. 22 -DI-~C. 26I GREG (WP43) 7 DEC. 24 -DEC. 27

-

OTAL 178 JULY 06 -DECEMBER 27

(158) and 465 (330) km, respectively, for 125, 123,115, 103 and 77 cases (Table 3). Large values of thestandard deviation mean that the forecast error canvary significantly from case to case. As indicated byRennick (1999), cases with large errors were gener-ally associated with either the erratic timing of re-curvature, poor tracking performance over elevatedterrain, or poor performance in an environment of

strong vertical shear of the wind. A number of re-searches addressing these problems have been devel-oped at GFDL (Ku:rihara et al. 1998).

Forecasts of stonn position by CLIPER for thesame cases as mentioned above showed average er-rors (standard deviations) of 126 (88), 223 (147),326 (210), 417 (266), and 633 (400) km for 12, 24,36, 48 and 72 h, r'~spectively. The frequencies of

780 Journal of the Meteorological Society of Japan Vol. 78, No.6

Table 3. Average track forecast errors (standard deviations) in km for GFDS forecasts in the westernNorth Pacific basin in 1995.

Table 4. Average track forecast errors (standard deviations) in km for a homogeneous comparison amongvarious model forecasts in the western North Pacific basin in 1995.

Unit: km

CLIPER

I

GFDS 197 (131) 275 (174)

NOGAPS 286 (169) 400 (260)AVN 331 (225) 468 (360)

Case number

superior performance by GFDS over CLIPER were67 %, 72 % and 68 % for 24, 48 and 72 h, respec-tively. The reduction in the average error and stan-dard deviation, as well as the high frequencies of su-perior performance, indicates the superior forecastof storm position by GFDS compared with CUPER.Customarily, the model's forecast skill is expressedby the difference of the average errors between themodel and CLIPER, divided by the average errorof CUPER. The result of calculation indicates re-duction of errors by GFDS relative to CUPER was25 %, 35 %, 41 %, 40 %, and 27 % at 12, 24, 36, 48,and 72 hours, respectively.

Forecasts of storm position from GFDS, AVN,NOGAPS, and the official JTWC forecast for 72 ho-mogeneous cases are compared in Table 4. Over-all, GFDS was the best performer in 1995 withthe smallest average error and standard deviationat all forecast times. The differences in the aver-age position error between GFDS and other fore-casts increased roughly linearly as the forecast timeincreased. Compared with CUPER, JTWC, NO-GAPS and AVN, reduction of the average positionerrors by GFDS at 24 (48) h was 28 (31), 15 (25),25 (31) and 34 (41) %, respectively. Only GFDSshowed improved skill relative to CLIPER at allforecast times. Student t-test indicated that theimprovement with GFDS over NOGAPS and AVNwas statistically significant at all time levels at the95 % confidence level, and the improvement overJTWC was at all forecast time levels except at 72 h.(Note that the GFDS forecasts in 1995 were runin an experimental mode at NCEP, and the fore-casts were not available at JTWC during that pe-riod. JTWC started referring to GFDN forecasts in1996 when the GFDL hurricane system was adoptedin the Navy.) It is also noted here that the statis-

tics u)r all of 212 NOGAPS track forecasts made in1995 had similar errors to those for the 72 case sam-ples used in this study. The NOGAPS performanceshown here was probably representative of its per-formance in the western North Pacific in the 1995seasOrl.

Th,e homogeneous comparison presented abovesuggests that GFDS can produce useful dynami-cal model guidance for tropical cyclone forecastersin the western North Pacific region. A key factorin thE! success of the GFDL hurricane forecast sys-tem is probably the initialization methodology used(Kurihara et al. 1995). Dramatic reduction in trackforec8.st errors occurred in the typhoon forecast sys-tem of the Central Weather Bureau in Taiwan whenthe filtering technique (Kurihara et al. 1995) wasemployed in their forecast model initialization (Chenet al. 1997).

It ~:hould be emphasized that the forecast com-parison between GFDS and other models presentedin thi:s paper is not necessarily a strict model com-pariscln, because each model used different analy-ses to define its initial condition. Also, one shouldnote I;hat the skill of a model is different in differ-ent b:3Sins and in different seasons, indicating thesensitivity of model performance to changes in envi-ronmental conditions. For example, while the over-all performance of NOGAPS in the western NorthPacifi,:: was not higher than GFDS during 1995, itsperformance in the Atlantic basin during that sameyear Vvas comparable with GFDL forecasts.

3.2 S!lstematic biasFor'~cast errors of the tropical cyclone position by

a modlel tend to exhibit position dependent system-atic bias. Figure 1 shows the distribution at two-degreE! resolution of the mean position error, i.e.,systeulatic bias, of GFDS for the 24 and 48 h fore-

December 2000 C.-c. Wu, M.A. Bender and Y. Kurihara 781

~','" ., I ~~' ~I~' ,;t , ,;I:.lob.

1 ~95 4~h!: .~_.:~~~~_l'~!Tl9~IAS

'1(~ ...c-",--'.'."!o

,

,

_:p_-"~--~--~~~<bob~i .6~

~~'V 'o-o1C~(jdo~~ 0:0 0 4) "-" C30nf""" ii~ 6 1~ .VVUU!

j c--ci~-~'~-_o._~- o~ 0'6 (!)~'-:~~g~(§'gg:~0 0 ~ " 0 ~ b b 0 ~ G-~(1'crC1:'ifc:l'C'!' iJ-r'"if'f~~1 600 f!;; 00 Ad-~o.~-& ~c-'"

rid '-' 0 ", \ '"! V io b,: qJ $ 0:'" i

,0 J b; Ccft-JY- ~I ~i'f ~ P .~~1 0.. ~~~~~':

~- -T ~-6-6---:

~r:.~ .-.

.~

C-'rf.,

" t.-".~

~

""",1::"-"1 (? ,~.,

~

Fig. 1. (a) Systematic bias at 24 h of GFDS forecasts in 19~15. The head, tail, and length of each arrowrepresent GFDS forecast position, the verifying position, and the mean position error in degrees,respectively. The radius of each circle represents the standard deviation of all position errors usedin computing average. (b) the magnitude (bold solid line with contour interval of 50 km) of thesystematic bias in (a), and the number of cases (thin solid line with contour interval of 5; regions withthe number of cases equal to or larger than 10 are shaded); (c) and (d) are as for (a) and (b), but for48 h.

cast times and the number of forecast cases averagedto obtain a bias. The vector plots in Figs. la and lcwere based on an ensemble of error vectors in a 10-degree-radius domain, while the standard deviationof errors for the cases used in bias computation wasrepresented by a circle centered at a vector tail. Ingeneral, the radius of each circle was much shorterthan the length of the corresponding error vector, in-dicating that the arrows were representative of the

spatial distribution of the forecast position bias.There exists a general northward bias in the re-

gion south of 30oN. Since most storms moved west-ward or northwestward in the low latitudes, thenorth'\Vard bias might suggest a tendency of GFDSto pr,~dict recurvature of storms too early. Fig-ures ]la and lc also show a general westward biasnorth of 30oN for both the 24 and 48 h forecasttimes.. This resulted from the predicted motion of

782 Journal of tne Meteorological :30ciety of Japan Vol. 78, No.6

Fig. 2. Scatter diagram of the 24, 48 and72 h GFDS cross-track (abscissa) andalong-track (ordinate) forecast positionerrors (units of 100 km) for cases shownin Table 1. The ordinate axis points to-ward the storm's heading direction.

northeastward or eastward moving storms being tooslow in this region, where eastward acceleration ofstorms was often observed.

The forecasts at 24 h tended to have a rela-tively small position error bias in the central partof Fig. lb. Two regions with minimum systematicbias could be found: one near the strait betweenTaiwan and the Philippines, the other in the cen-tral Pacific between 200N and 300N, and 1300E and136°E. In the latter region, the arrows were wellcontained in the circles (Fig. 1a), implying randomscatter of error vectors in an ensemble. For the 48 hforecasts (Figs. 1c and d), minimum systematic biaswas found to the northeast of the Philippines, as wellas in the central Pacific between 25°N and 300N, and1300E and 133°E. One would expect that, as theseminimum biases were calculated in regions with arelatively high number of cases (Figs. 1b and 1d),small systematic bias could mean a small error in in-dividual cases. However, Fig. 1c indicates that thestandard deviation was larger than the systematicbias to the northeast of the Philippines. Therefore,such minimum bias was not representative of errorsin individual cases. A local maximum bias near east-ern China was caused by several bad forecasts of Ty-phoon Janis. Generally, large bias existed near theboundary of the plotted region for both the 24 and48 h forecasts. This was due to a few bad forecastsamong the limited number of cases occurring there(Figs. 1b and 1d). For the 72 h forecast time (figurenot shown), a strong northward bias was also foundin the area south of 32°N.

By decomposing forecast errors of the storm mo-tion in the cross-track/along-track directions (in thepresent analysis, storm's heading direction was es-timated from the observed storm positions 6 h be-fore and 6 h later), one may see the tendency of themodel storm to move to the right or to the left of thestorm's heading direction and, also, to move too fastor too slow [e.g., Fig. 4 in Rennick (1999)]. A scatterplot of forecast position errors relative to the storm'sheading direction is presented in Fig. 2. Each quad-rant of the diagram corresponds to a particular kindof bias. For example, points in the upper right quad-rant represent forecasts that were faster than the ac-tual movement and to the right (taken as positive) ofthe storm's heading direction. In consistent with thenorthward bias shown in Fig. 1 of westward movingstorms in low latitudes, the plot revealed mean right-ward bias relative to the storm's heading directionfor all forecast times. The forecast position errorvectors were also decomposed into zonal/meridionaldirections. As shown by the scatter plot in Fig. 3,general northward bias existed for all forecast times,which was consistent again with rightward bias ofwestward moving storms.

]"ig. 3. Scatter diagram of the 24, 48 and72 h GFDS forecast position errors (unitsof 100 km) for cases shown in Table 1.The ordinate axis points to the north.The abscissa points to the east.

December 2000 c.-c. Wu, M.A. Bender and Y. Kurihara 783

GFDS WIND SPEED BIAS1995 WESTERN PACIFIC SEASON

I ..1, '1 60~4 x x" " " Ax x A

~. x AA §x ~ x xAAlx AI~~ AI:

3 :j ~i : "'i!:8: X .a.t x 'x lAX X .x x" x A " ".44 A t ~ x ~ :" x i A A

! A A A ~ 4 ~

A, 4 .X,

.~ x§ Ax~ X ~ ~ :

., aX

l\ HOUR 12l\ HOUR 24.HOUR 36.HOUR 48.HOUR 72

c 0w>a:wUI'" -200

P

"

I~"6

&

~.i:?;

~

-40 610 iA A

toA

so 75 100 125OBSERVED WIND SPEED (KNOTS)

150

Fig. 4. The GFDS intensity forecast error(knots) as a function of storm intensityfor cases shown in Table 1.

3.3 Intensity forecastIntensity prediction remains quite difficult in the

tropical cyclone forecasting. The plot of forecast in-tensity errors (Fig. 4) showed a tendency of GFDSto predict weak (strong) storms too strong (weak).A similar tendency has been observed in the caseof the GFDL hurricane prediction system in boththe Atlantic and eastern Pacific basins (Kurihara etal. 1998; Bender and Ginis 2000). Probably, thetendency of GFDS to underpredict intense stormsis partly due to the insufficient finest resolutionof the model (i.e., 1/6 degree) which still cannotadequately resolve the eye and eyewall structure.(It should be noted that in the 1995 version ofGFDS, the maximum wind at the 40-m level wastaken for the maximum surface wind rather thanthe conventional10-m level wind.) The tendency tooverpredict weak systems may be partly related tothe scheme currently used in the initialization stepwhich produces a vortex without realistic asymme-tries.

The average errors of the intensity forecast byGFDS were 20.6,21.7, and 23.1 knots, at 24, 48 and72 h, compared with 13.1, 19.8 and 25.2 knots for theJTWC official forecast. Although the errors fromthe GFDS were larger than those from the JTWCforecasts, the error increased by only 2.5 knots from24 h to 72 h in contrast to a 12.1 knots increase forthe same time period in the JTWC forecasts. Whenthe GFDS intensity forecasts were corrected for biasin the early period, the results became more com-petitive with the JTWC forecasts. Thus, GFDS canstill provide useful information for the forecast ofstorm intensity in some instances, particularly forthe later forecast hours.

4. RI~sults from forecast experiments in 1996

In Ithis section, the error statistics of the GFDSforecasts in 1996 are presented and compared withthe results for 1995. Detailed comparison of thetwo SE!tS of forecasts (i.e., GFDN and GFDS) is alsoshown, emphasizing the differences and similaritiesin the distribution of the forecast bias.

4.1 Average error: comparison between GFDN andGFDS

ThE: average forecast position errors for GFDS in1996 for forecast times 12, 24, 36, 48 and 72 h were108, 178,227,280 and 480 km, respectively, for 178,166, 153, 143 and 114 cases (Table 5). Comparedwith ~::;FDS forecasts in 1995, the position errorsin 19~16 increased by 10 % through 48 h, but de-creased slightly at 72 h. The GFDS forecasts in1996 ,vere performed at non-synoptic times (06 and18 UTC). It remains to be investigated whether theincrease in position error in 1996 were partly relatedto the quality of the AVN global analysis at the non-synopltic time. Interestingly, for the same homoge-neous cases, the position error of the CLIPER fore-casts in 1996 increased by 30 % (20 %) at 24 h (48 h)compa.red with those in 1995, suggesting greaterforec~5t difficulty in 1996. As a result, in spite ofthe re,duction of position forecast skill of GFDS in1996, its forecast skill relative to CUPER in 1996was about 5-10 % greater than in 1995. Overall,GFDS forecasts both in 1995 and 1996 showed su-perior forecasts to CUPER for all forecast periods.

The average position errors of GFDN and GFDSfor eac:h forecast time are shown in Table 5, alongwith the percentage of cases in which GFDN exhib-ited a performance superior to GFDS. ComparisonbetweE~n these forecasts, which used the same modelbut based on different global analyses and forecasts,showed about a 3 % smaller average track error forGFDN through 48 h. At 72 h, GFDN forecasts ex-hibited about 11 % reduction in mean track errorover C:FDS. This was the only time level at whichthe difference was found to be statistically signifi-cant at the 95 % confidence level.

The spatial distribution of the forecast error (fig-ure not shown) showed that both models had theirlargest errors over northern Japan and in the SouthChina Sea between the Philippines and Vietnam.The fc,recast error for GFDS was particularly largein the~;e two regions, with 600 km average at 48 hjust east of central Vietnam compared with 450 kmfor GF'DN. Both GFDN and GFDS exhibited small-est errors in the 220 to 340 latitudinal band, whichwas the same with the results from the GFDS fore-casts in 1995.

Experimentally, forecast positions of the GFDNand GFDS were averaged to obtain composite fore-casts (GFDA). It was found that the mean fore-cast error of GFDA at each time level was less than

784 Journal of the Meteorological Society of Japan Vol. 78, No.6

Table 5. Forecast errors (standard deviations) in km for GFDS (AVN analysis), GFDN (NOGAPSanalysis) and % of cases with superior performance of GFDN in 1996.

Table 6. Forecast errors (standard deviations) in km for GFDA (average of GFDS and GFDN) and %of superior performance of GFDA compared to GFDS and GFDN in 1996.

Unit: Km

the error of GFDN as well as of GFDS (Table 6)by about 10 %, while the standard deviation alsoslightly decreased. The improvement of GFDA wasfound to be statistically significant over GFDS at allforecast times shown, and over GFDN at all forecasttimes except 72 h.

;'.2 Systematic bias: further comparison betweenGFDN and GFDS

As shown above, the storm position forecast wasimproved by combination of GFDN and GFDS. Fig-ure 5 shows the distribution of the systematic bias inposition forecast at 48 h for both GFDN and GFDS.For GFDN (Fig. 5a), a southward bias was indicatedin the region south of 3OoN and west of 140oE, ex-cept for a slight northward bias just east of Taiwanand over the South China Sea. North of30ON GFDNhad a strong westward bias. For GFDS (Fig. 5b),similar to the 1995 season, a general northward biaswas indicated in the region south of 30ON. Northof 30oN, the strong westward bias during the 1995season was somewhat reduced in 1996 with more ofa northward bias east of 140oE. Overall, the sys-tematic biases of the two forecasts differed in manyplaces, while similarities were found on the locationof maximum and minimum biases. This means thatthe forecast positions of GFDN and GFDS tendedto spread systematically in position-dependent dif-ferent directions with respect to the best track po-sition. Such a difference in the systematic bias offorecast position between GFDN and GFDS mightpartly explain why the reduction in position erroroccurred in GFDA.

Following Aberson et al. (1998), the relation be-tween the error of an ensemble (forecast positionerror of GFDA) and the error spread within theensemble (the forecast position difference betweenGFDN and GFDS), was investigated. In agreementwith Aberson et al. (1998) and Goerss (2000), noclear correlation between the spread and the GFDAerror at 72 h was found (Fig. 6). Nevertheless, thespread appears to crudely suggest the upper boundof thE: ensemble forecast error.

,/.3 Supplemental experimentsIn order to evaluate the impact of systematic bias

(e.g., Fig. 1 for GFDS in 1995 or Fig. 5b for GFDS in1996) on the tropical cyclone track forecast, supple-ment." experiments were conducted, in which sys-temaj;ic bias associated with each forecast systemwas sllbtracted from each forecast. The average po-sition errors and standard deviations after the sys-tematic bias correction are listed in Tables 7 and 8for GFDS in 1995, and GFDS, GFDN and GFDA in1996, individually. For each experiment, the reduc-tion in both average position errors and standard de-viations as compared to the prediction without thesystematic bias correction is mostly more than 20 %for all forecast time. As we combine the GFDS fore-casts both in 1995 and 1996 to form a larger forecastsample (Table 9) and to perform a new systematicbias calculatioQ and correction, the improvement ofmore than 15 % at all forecast time is still evident.Results obtained from the above experiments clearlysuggest the utility of the systematic bias in the op-erational forecasts, and issues related to this point

December 2000 C.-c. Wu, M.A. Bender and Y. Kurihara 785

Fig. 6. Relation between forecast positionerror of an ensemble (abscissa) and thespread among the ensemble members (or-dinate) at 72 h for cases shown in Table 5in western North Pacific in 1996.

Fig. 5. Systematic bias of (a) GFDN (NO-GAPS analysis) and (b) GFDS (AVNanalysis) forecasts at 48 h for 143 cases(Table 5) in the western North Pacificin 1996. The head, tail, and length ofeach arrow represent the forecast posi-tion, the verifying position, and the ac-tual mean position error in degrees, re-spectively. The radius of each circle rep-resents the standard deviation of all po-sition errors used in computing average.

are further addressed in the summary.

4.4 Intensity forecastThe storm intensity was represented by the max-

imum wind at the conventional 10 m level in 1996,rather than by the 40 m level wind in 1995. As a re-sult, each GFDS forecast showed about a 5-knot re-duction in intensity from the forecasts in 1995. Still,in agreement with Rennick (1999), a tendency ofthe GFDL system to predict the intensity of weakstorms too strong, and predict strong storms tooweak, was indicated (figure not shown).

5. Summary and, remarks

The GFDS system, which combines the model de-veloped at the GFDL with the AVN global analysis,was experimentally run in the prediction of tropicalcyclones in the western North Pacific basin. Thenumber of cases treated by GFDS in 1995 (1996)was 125 (178) for 16 (24) tropical cyclones. The er-ror analysis indicated superior track prediction skillof GFDS compared with CLIPER. The average po-sition error in 1995 was nearly 30 % less than thatof CLIPER.

Intensity forecast with GFDS was not satisfac-tory. A tendency to overpredict the intensity ofweak storms and j;o underpredict the intensity ofstrong storms were indicated, similar to the ten-dency found in the Atlantic and East Pacific basins.Efforts to improve the GFDL hurricane predictionsystem has been under way with emphasis on theimprovement in intensity forecast (e.g., Bender andGinis 2000).

786 Journal 0' the Meteorological Society of Japan Vol. 78, No.6

Table 7. Average position errors (standard deviations) in km for ~::;FDS forecasts with systematic biascorrection for cases in 1995, and % of improvement compared to those without bias correction.

Table 8. Average position errors (standard deviations) in km for GFDS, GFDN and GFDA forecastswith systematic bias correction for cases in 1996, and % of improvement compared to those withoutbias correction.

Table 9. Average track forecast errors (standard deviations) in km for GFDS forecasts for all cases in 1995and 1996 without and with systematic bias correction, and % of improvement with bias correction.

Unit; km24h 36h

ever, distribl11tion of systematic bias changes withtime and, hence, it cannot be available beforehand.Yet, if the te:mporal change of the systematic bias isslow, the bias may show up in the forecast error asthe season pJ:ogresses to increase the forecast cases.Information on the forecast bias may be useful toforecasters, particularly to those acquiring only oneforecast guidance. Of course, research for reducingbias in the storm track forecast is needed using var-ious approaches such as the potential vorticity diag-nostics (Wu ,a.nd Emanuel 1995j Wu et al. 2000).

AcknowledgmentsThe authors thank Jerry Mahlman, Robert

Tuleya and Jeffrey Anderson for their helpful re-marks, and ?llIasashi Nagata (JMA) and the anony-mous review~!rs for valuable comments. The authorsalso thank Steven Lord (NCEP) for kindly providingthe data use([ in the present work. Special acknowl-edgment is given to Mary Alice Rennick of FNMOC

The GFDL model was also combined with theNOGAPS global analysis to establish a predictionsystem, GFDN. Both GFDS and GFDN were runfor 178 homogeneous cases in 1996. It was indi-cated from the comparison of forecasts by two sys-tems that, although average errors in forecast posi-tion were comparable with each other in distance,positions of GFDN and GFDS tended to be system-atically biased toward different directions. WhenGFDN and GFDS forecasts were averaged, the meanerror was 10 % smaller than the error for either in-dividual forecast. This suggests that, depending onthe spread of systematic biases of ensemble mem-bers, the ensemble average of forecasts may make areliable guidance (Goerss 2000).

As demonstrated by the supplemental experi-ments, it is likely that the track forecast error wouldbe reduced when the forecast could be corrected bybias of the prediction system used. In practice, how-

December 2000 C.-c. Wu, M.A. Bender and Y. Kurihara 787

for her valuable suggestions, for providing data nec-essary for this study, and for helping in the imple-mentation of an automated system to provide theGFDN forecasts and storm information in near-realtime. The computational support provided by theNCEP computer facility for this study is appreci-ated. Credit is also given to Yu- Tseng Cho (N a-tional Taiwan University), Kang-Ning Huang (Cen-tral Weather Bureau) and Jeffrey Varanyak (GFDL)for drafting assistance. The first author is par-tially supported through the National Science Coun-cil of Taiwan by Grants NSC87-2621-P-OO2-053 andNSC88-2325-Z-002-028.

References

Aberson, S.D., M.A. Bender and R.E. Thleya, 1998:Ensemble forecasting of tropical cyclone tracks.Preprints, 12'"h Con! on Numerical Weather Predic-tion, Phoenix, AZ, Ameri. Meteor. Soc., 290--292.

Bender, M., A., R.J. Ross, R.E. Thleya and Y. Kurihara,1993: Improvements in tropical cyclone track andintensity forecasts using GFDL initialization system.Mon. Wea. Rev., 121, 2046-2061.

and I. Ginis, 2000: Real case simulations ofhurricane-ocean interaction using a high resolutioncoupled model:Effects on hurricane intensity. Mon.Woo. Rev., 128, 917-946.

Chen, D.-S., T.-C. Yeh, K.-N. Huang, M.-S. Peng andS. W. Chang, 1997: Performance of the typhoon-track forecast system in the Central Weather Bureauin Taiwan. Proc., 22 Con! on Hurricanes and n-op-ical Meteorology. American Meteorological Society,Boston MA., 248-249.

Derber, J., H. Pan, J. Alpert, P. Caplan, G. White,M. Iredell, Y. Hou, K. Campana and S. Moor-thi, 1998: Changes to the 1998 NCEP opera-tional MRF model analysis/forecast system. Tech.Procedures Bull. 449. [Available from National

Centers for Environmental Prediction, W /NP23,World Weather Building, Washington DC, 20233; or

http://wwlli.nws.noM.gov/om/tpb/index.htm.)Goerss, J.S. B:lld P. Phoebus, 1992: The navy's oper-

ational atmosphric analysis. Woo. Forecasting, 7,232-249.

and R.. Jeffries, 1994: Assimilation of synthetictropical cyclone observations into the Navy Opera-tional Global Atmospheric Prediction System. Woo.Forecasting, 9, 557-576.

, 2000: Thopical cyclone trock forecasts using anensemble of dynamical models. Mon. Wea. Rev.,128,1187-1193.

Kurihara, Y., C.L. Kerr and M.A. Bender, 1989: Animproved numerical scheme to treat the open lateralboundary of a regional model. Mon. Woo. Rev., 117,2714-2722.

, --, R.E. Tuleya and R.J. Ross, 1995: Im-provement in the GFDL hurricane prediction system.Mon. Woo. Rev., 123,2791-2801.

, R.E. Tuleya and M.A. Bender, 1998: TheGFDL hun'icane prediction system and its perfor-mance in the 1995 hurricane season. Mon. Woo.Rev., 126, 1306-1322.

Rennick, M.C., 1999: Performance of the Navy's trop-ical cyclone prediction model in the western NorthPocific basill during 1996. Weather and Forecasting,14, 297-30~i.

Wu, C.-C. and K.A. Emanuel, 1995: Potential vorticitydiagnostics of hurricane movement. Part I: A casestudy of Hlllrricane Bob (1991). Mon. Woo. Rev.,123, 69-92.

-, T.-S. Huang, K.-N. Huang and T.-C. Yeh, 2000:PV diagnostics of the impoct of model initializationon the performance of a typhoon prediction system.Proc., 24th Conf. on Hurr. And Tropical. Meteor.,Ameri. Meteor. Soc., Boston, MA. 423-424.

Zhang, Z. and T.N. Krishnamurti, 1999: A perturbationmethod for hurricane ensemble predictions. Mon.Wea. Rev., 127, 447-469.

788 Journal of the Meteorological Society of Japan Vol. 78, No.6

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