Forecasting Tropical Cyclogenesis Over the Atlantic Basin Using Large-Scale Data

DECEMBER 2003 HENNON AND HOBGOOD 2927

Forecasting Tropical Cyclogenesis over the Atlantic Basin Using Large-Scale Data

CHRISTOPHER C. HENNON* AND JAY S. HOBGOOD The Ohio State University, Columbus, Ohio

(Manuscript received 17 September 2002, in ®nal form 13 June 2003)

ABSTRACT A new dataset of tropical cloud clusters, which formed or propagated over the Atlantic basin during the 1998± 2000 hurricane seasons, is used to develop a probabilistic prediction system for tropical cyclogenesis (TCG). Using data from the National Centers for Environmental Prediction (NCEP)±National Center for Atmospheric Research (NCAR) reanalysis (NNR), eight large-scale predictors are calculated at every 6-h interval of a cluster's life cycle. Discriminant analysis is then used to ®nd a linear combination of the predictors that best separates the developing cloud clusters (those that became tropical depressions) and nondeveloping systems. Classi®cation results are analyzed via composite and case study points of view. Despite the linear nature of the classi®cation technique, the forecast system yields useful probabilistic forecasts for the vast majority of the hurricane season. The daily genesis potential (DGP) and latitude predictors are found to be the most signi®cant at nearly all forecast times. Composite results show that if the probability of development P Ͻ 0.7, TCG rarely occurs; if P Ͼ 0.9, genesis occurs about 40% of the time. A case study of Tropical Depression Keith (2000) illustrates the ability of the forecast system to detect the evolution of the large-scale environment from an unfavorable to favorable one. An additional case study of an early-season nondeveloping cluster demonstrates some of the shortcomings of the system and suggests possible ways of mitigating them.

1. Introduction provided researchers and forecasters with tools for testing There is increasing evidence in the recent literature theories and re®ning knowledge of processes that result that the crucial physical processes regarding tropical in TCG. Using one such model, the ®fth-generation Penn- cyclone formation [tropical cyclogenesis (TCG)] in- sylvania State University (PSU)±National Center for At- volve convective and dynamical interactions that occur mospheric Research (NCAR) Mesoscale Model (MM5; on scales that are not currently resolved in operational Grell et al. 1994), Davis and Bosart (2001) were able to forecast models (Emanuel 1989; Chen and Frank 1993; simulate the genesis of Hurricane Diana (1984), begin- Bister and Emanuel 1997; Simpson et al. 1997; Ritchie ning from a weak baroclinic disturbance through hurri- and Holland 1997; Montgomery and Enagonio 1998). cane stage. This type of research was not possible only Consequently, such models have shown virtually no a few years ago. Second, quality atmospheric data are skill at forecasting TCG (e.g., Beven 1999), though now available globally on a regular grid in the form of there has been some improvement as resolution has been reanalysis datasets. These datasets provide surface and increased and model parameterizations improved (Pasch upper-air data derived from a merged ®eld of model- et al. 2002). This leaves forecasters with the dilemma based ®rst-guess estimates and quality controlled in situ of trying to forecast an event without useful objective observations. The National Centers for Environmental guidance and sparse, if any, in situ observational data. Prediction (NCEP)±NCAR reanalysis (NNR) provided Fortunately, several promising developments suggest the atmospheric data for this research. that skillful forecasting of TCG is an attainable goal in Third, and perhaps most relevant to this study, is the the short term. First, advances in computing power have evidence that suggests that skillful TCG forecasts could led to the development of dynamical models with ®ne be made even if the complex mesoscale features and (down to 3-km horizontal) resolution. These models have interactions are neglected. This evidence comes from several sources. McBride and Zehr (1981), in a large global survey of tropical cloud clusters, showed that a * Current af®liation: UCAR Visiting Scientist Program, NOAA/ TPC/National Hurricane Center, Miami, Florida. single parameter computed from observational upper- air data provides valuable information regarding the likelihood of tropical depression development. The daily Corresponding author address: Dr. Christopher C. Hennon, Post- genesis potential (DGP), de®ned simply as the differ- doctoral Fellow, UCAR Visiting Scientist Program, NOAA/TPC/Na- tional Hurricane Center, 11691 SW 17th St., Miami, FL 33165-2149. ence of the large-scale (ϳ4Њ±6Њ of the cluster center) E-mail: [email protected] 900- and 200-mb relative vorticity ®elds, supported a

᭧ 2003 American Meteorological Society 2928 MONTHLY WEATHER REVIEW VOLUME 131 fundamental way of thinking about TCG that was ®rst that analyzes the larger-scale cloud cluster features of proposed by Gray (1968). He argued that in terms of many developing and nondeveloping systems may be large-scale factors important to TCG, the thermodynam- able to provide users with a useful probabilistic forecast ic state of the tropical atmosphere during the hurricane for development. season is nearly always favorable; tropical development The ultimate goal of this research is to provide an depends primarily on dynamical factors. A numerical objective tool for forecasters to assess the likelihood of model sensitivity study performed by Tuleya (1991) a tropical cloud cluster developing into a tropical de- seemed to con®rm that conclusion. The DGP is used as pression (TD) from large-scale data that can be routinely a predictor of TCG in this study. The second source is derived from operational analyses. It is meant to be used the classi®cation work of Perrone and Lowe (1986; in conjunction with other valuable data and information, Lowe and Perrone 1989). Their methodology, which has particularly satellite tools, which have recently been de- numerous similarities to this work, involved identifying veloped or enhanced. Examples of these exciting ad- candidate cloud clusters in the Paci®c basin and strat- vances that are being applied to the TCG problem are ifying them by their eventual development status. Using atmospheric sounders such as the Advanced Microwave linear discriminant analysis, they obtained signi®cant Sounding Unit (AMSU; Kidder et al. 2000), quick scat- skill in separating the developing and nondeveloping terometer (QuikSCAT)-derived wind ®elds (Sharp et al. cloud clusters and concluded that the large-scale envi- 2002), the Tropical Rainfall Measuring Mission (TRMM) ronment may contain more predictive power than has space-borne precipitation radar (e.g., Cecil et al. 2002), been previously thought. A comparison of their work and geostationary satellite data that is being used to detect with this research is discussed in more detail in section the existence of dry Saharan air layers in clusters em- 5. Finally, DeMaria et al. (2001) developed a genesis bedded in easterly waves that move offshore of western parameter for the Atlantic, derived from operational Africa (J. Dunion 2002, personal communication). NCEP analysis ®elds and Geostationary Operational En- The next section presents the data sources employed vironmental Satellite (GOES) water vapor imagery, in this work. Section 3 describes the methodology, in- which was shown to be correlated with active and including the creation of the cloud cluster database, the active genesis periods over the Atlantic basin. They con- selection of the predictors used to classify developing clude by suggesting that a disturbance-centered param- and nondeveloping systems, and the statistical technique eter could be developed to evaluate systems individu- used for classi®cation. Results of the discriminant anal- ally, rather than evaluating the environmental properties ysis classi®cation will be presented in section 4, with of the genesis region. subsections dedicated to a composite analysis and two This research is an attempt to develop such a parameter. individual case studies. Section 5 contains a brief dis- This paper examines the ability to differentiate between cussion of the differences between this study and that cloud clusters that eventually form into tropical depres- of Perrone and Lowe (1986) that may explain dissim- sions [``developing'' (DV)] and those that do not [``non- ilarities in the results. Finally, the conclusions will be developing'' (ND)] based on large-scale reanalysis data. summarized in section 6, followed by suggestions for Building on the studies of Perrone and Lowe (1986), future work in this area. Lowe and Perrone (1989), McBride and Zehr (1981), and DeMaria et al. (2001), we investigate eight predictors either extracted or calculated from the NNR for every 2. Data sources identi®able tropical cloud cluster from the Atlantic hur- a. NCEP±NCAR reanalysis ricane seasons (1 June±30 November) of 1998±2000. The validity of using a large-scale dataset to forecast TCG is The atmospheric data for this study were obtained tested, given that the coarse resolution (2.5Њϫ2.5Њ)of from the 6-hourly global NNR dataset (Kalnay et al. the data renders it unable to resolve any mesoscale phe- 1996). The reanalysis is a global assimilation of obser- nomena, including tropical convection. vations from many different sources, carefully checked To accomplish this task, this study was based on the for quality and merged with a numerical model ®rst- following premise. TCG is not a single event in time, guess ®eld. The resultant dataset includes pressure level but a series of events that result in a surface vortex with variables on a global 2.5Њϫ2.5Њ grid with 17-level an upper-level warm core. Each subsequent event cannot vertical resolution. Direct observations include surface occur without the success of the one that immediately stations, aircraft, and rawindsonde and satellite data. preceded it. As the sequence progresses, the probability Used for numerous applications in meteorological re- of tropical cyclogenesis gradually increases until the search, the NNR provides quality data coverage in areas event is successful (genesis occurs) or a link in the chain that have historically had little or no observational data fails and the cluster becomes stagnant or falls apart. The (i.e., ocean basins). It provides tremendous potential for series of mesoscale and smaller-scale phenomena that application to tropical cyclone research for those who occur near the end of the sequence cannot do so without are interested in large-scale phenomena that has not ex- the establishment of favorable larger-scale conditions isted previously, and is thought to be generally better at the beginning of the sequence. A statistical model in the Tropics than the European Centre for Medium- DECEMBER 2003 HENNON AND HOBGOOD 2929

Range Weather Forecasts (ECMWF) reanalysis (Tren- d. Genesis time and location berth et al. 2001; D. Bromwich 2002, personal com- Determining the time of ``genesis'' is dif®cult and munication). open to interpretation. To simplify the question and re- For this research, the ®rst of our knowledge to exploit main objective regarding the time and location of gen- a reanalysis dataset for the study of TCG, we use the esis, we use the National Hurricane Center (NHC) ``best following data from the NNR: air temperature at 12 track'' database (Jarvinen et al. 1984) with the caution- pressure levels (925, 850, 700, 600, 500, 400, 300, 250, ary note that there are several possible sources of un- 200, 150, 100, and 70 hPa), sea level pressure, precip- certainty and error in the database. For example, there itable water, zonal and meridional winds at the surface is usually little or no direct observational data on hand and at 3 pressure levels (850, 700, and 200 hPa), and to aid analysis in determining the occurrence of genesis speci®c humidity at the same pressure levels. We are and the location of the center of circulation (Bracken con®dent in the quality of the temperature and wind and Bosart 2000). Nevertheless, the best-track ®le is a data. Kalnay et al. (1996) label temperature, wind ®elds, valuable resource and provides the best estimate of the and sea level pressure as class ``A'' variables, meaning time and location of TCG in the Atlantic basin. Tropical that they are almost entirely derived from observations depressions that failed to develop into tropical storms rather than the model. Precipitable water and speci®c do not appear in the databaseÐthose cases were ob- humidity are classi®ed as ``B'' variables, meaning that tained via the NHC Web site. the global model has some in¯uence on the output, but that the direct observations still signi®cantly impact the result. Section 3 documents how the data from the NNR 3. Methodology are used to calculate the predictors for this study. a. Assumptions In our identi®cation and evaluation of cloud clusters b. Sea surface temperature we make the following assumptions. For simplicity, we assume that each system is axisymmetric. In the iden- To provide a lower boundary of temperature for the ti®cation process, the position of the cloud cluster is computation of the maximum potential intensity pre- ®xed at the estimated center of the cloud mass, unless dictor (discussed in section 3), we use Reynolds sea a more precise circulation center is evident from the surface temperature (SST; Reynolds and Smith 1994). satellite imagery. The most accurate position is not cru- Real-time observations are available with a temporal cial to the results, however, because of the coarseness resolution of 1 week, distributed on a 1Њϫ1Њ grid. To of the dataset (2.5Њϫ2.5Њ). We assume that genesis make the dataset spatially consistent with the NNR, the occurs when the system ®rst appears in the best-track grid was devolved into a 2.5Њϫ2.5Њ grid through a database. This is the only objective method for deter- simple bilinear interpolation routine. The data ®elds are mining genesis at this time. Cloud clusters over major derived from a somewhat complex optimum interpo- land areas are not considered in this study; otherwise, lation (OI) technique (Reynolds and Smith 1994) that we neglect any effects that proximity to land may have improves upon the blended technique of Reynolds on the systems. The large majority of the cases thrown (Reynolds 1988). out because of this assumption were African easterly waves that had not left the continent. c. Satellite imagery b. Identi®cation of cloud cluster candidates In order to track the tropical cloud clusters that make Geostationary IR imageries were visually examined up the foundation of this study, GOES-8 infrared (IR) for the 1998±2000 Atlantic hurricane seasons (1 June± satellite imageries were retrieved from the archive at 30 November) to ®nd all of the tropical cloud clusters the Wisconsin Space Science and Engineering Center that formed within the study domain, shown in Fig. 1. (SSEC) at a time resolution of 6 h and a wavelength of To identify a cloud cluster, we had to ®rst decide on a 3.9 ␮m. To provide coverage of the eastern Atlantic, de®nition of a cloud cluster. In general, tropical cloud archived Meteosat-7 IR imageries were obtained from clusters are considered to be mesoscale convective sys- the European Organisation for the Exploitation of Me- tems (MCSs) with length scales of 250±2500 km and teorological Satellites (EUMETSAT) at similar temporal timescales Ն6 h (Maddox 1981). The main idea is that intervals. A mosaic was produced that combined each a cloud cluster should be a system that has the potential satellite's view into one coherent image. If either image to develop into a tropical depression. Hence, several was missing, not available, or not within 1 h of the requirements immediately surface; namely, a cluster corresponding view, the time was labeled as ``missing'' must have suf®cient size, must persist for an extended and not available for cloud cluster tracking. Of the 2196 period of time (i.e., not diurnal in nature), and must potential scenes available for tracking, less than 5% exist in a region where genesis is a genuine possibility were not available. (not in the high latitudes). To quantify these require- 2930 MONTHLY WEATHER REVIEW VOLUME 131

FIG. 1. (top) Location of nondeveloping cases and (bottom) developing clusters 24 h prior to genesis for the 1998±2000 Atlantic hurricane seasons. ments, we use the following criteria initially adopted by objective method for identifying viable candidates. Note Lee (1989) for his work in the Paci®c basin: that the last requirement (iv) is satis®ed if convection persists for any 24-h period of the cluster lifetime. This i) Each cluster must be an independent entity, disas- requirement primarily applies to the ND systems, since sociated with either a cyclone or precyclone system all DV clusters were tracked as long as traceable con- ii) Cluster must be at least 4Њ in diameter and not elon- vection was apparent. We were not able to group a num- gated in shape ber of DV clusters into longer forecast lead times be- iii) Cluster must be located south of 40ЊN iv) Cluster must persist for at least 24 h cause convection ®rst developed at a time close (inside of 48 h) to genesis. Although a robust automated system Essentially, TCG was highly unlikely if any one of these for objectively identifying cloud clusters is highly de- criteria was not met. These criteria provided a somewhat sirable, the clusters in this paper were identi®ed through visual inspection of the satellite brightness temperatures. Table 1 presents a summary of the cloud cluster char- TABLE 1. Summary of documented cloud clusters for the 1998±2000 Atlantic hurricane seasons. acteristics for the three Atlantic seasons in this study. The 2000 season was the most active in terms of both 1998 1999 2000 developing and nondeveloping candidates, though 1998 Total number of clusters 90 91 110 appeared to be a more active year for fertile African Longest in duration (h)* 198 258 294 waves (those easterly waves that exhibited signi®cant Mean duration (h)* 58.9 55.1 54.8 Median duration (h)* 42 36 42 and persistent convection). Full-season summaries for Number of African waves** 42 32 41 all three seasons can be found in the recent literature African wave ratio (%) 46.7 35.2 37.3 (Pasch et al. 2001; Lawrence et al. 2001; Franklin et al. Number of TDs 14 16 18 2001). * Nondeveloping clusters only. We de®ne a ``case'' as one 6-h forecast time within ** Only those easterly waves that exhibited signi®cant and persistent the entire lifetime of a cloud cluster. Therefore, a cloud convection are included. cluster is typically made up of many cases. Once all DECEMBER 2003 HENNON AND HOBGOOD 2931 candidate cloud clusters were identi®ed, their cases were grouped into DV or ND bins. A case is categorized as DV if the cluster developed into a tropical depression within 48 h of the image time. The DV cases were further strati®ed by the number of hours before genesis. For example, Tropical Depression Bonnie (1998) achieved genesis at 1200 UTC 19 August. The pre- Bonnie cloud cluster at 0600 UTC 19 August was categorized as a ``6-h developing case.'' At 2 days prior to genesis, the 1200 UTC 17 August case was labeled a ``48-h developing case.'' If Bonnie's convection was evident before that time, it was categorized as an ND case and included with other cases that never achieved tropical depression status. This ``double counting'' of DV clusters occurred 26 times over the 3-yr period of study. Thus, there were actually 317 clusters over the 3-yr period, but only 291 of them were unique. Figure 1 illustrates the spatial distribution of all of the ND (top) cases and DV (bottom) cloud clusters at 24 h prior to genesis. There were 2265 ND cases from 269 ND clusters, meaning that each cluster existed for approximately 8.5 six-hour periods on average. Of all the cases that made up those clusters, 114 were excluded from the ®nal analysis because of missing satellite data, FIG. 2. Schematic of the areal averaging technique for obtaining 48 because convection became insigni®cant or absent, the predictor dataset. A circle of 2Њ radius from the center of the and 12 because the cluster progressed over a major land- cloud cluster (``L'') delimits the outer boundary for the averaging. mass. Each individual case is assumed to be independent All grid points that fall within the circle (open dots) are averaged of the other cases in the same cloud cluster for the together to yield the value for that cloud cluster and time. discriminant analysis. The two areas of greatest con- centration easily stand out: the easterly wave track along culations presented here were calculated using the larger 8ЊN from the African coast into the central North At- averaging area. lantic and clusters spawned by the monsoon trough in the southern Caribbean. It can be seen that the concen- tration of DV clusters is spatially coherent with the ND d. Selection of predictors cases, although there is a higher development ratio for the region immediately off the eastern U.S. shore and The large-scale factors that must be favorable for in the Gulf of Mexico. This suggests that these areas TCG to occur are well-established and are not neces- are favored TCG regions climatologically. sarily mutually exclusive: 1) There must exist a suf®ciently large area of preexisting convection (Riehl 1948) c. Technique for creation of predictor dataset 2) There must be suf®cient planetary vorticity, usually All of the predictors (except pressure tendency, which requiring the cluster to be at least 5Њ latitude away is dependent on storm location) were computed for each from the equator (Gray 1968) grid point in the domain at 6-h intervals for the entire 3) There must exist near-zero vertical wind shear over Atlantic hurricane seasons of 1998±2000. Figure 2 the center of the system, accompanied by a high- shows an illustration of the averaging technique. Once shear gradient across the system in both the zonal the location of the cloud cluster is identi®ed, a swath and meridional directions (McBride and Zehr 1981) 4) The midtroposphere must be suf®ciently moist (Gray with a radius of 2Њ [6Њ for maximum potential intensity 1968) (MPI)] is envisioned around that point. All NNR grid 5) The ocean must be suf®ciently warm and have a deep points that are located within that radius are averaged mixed-layer depth (Gray 1968) to yield a single value that represents the cloud cluster 6) The atmosphere must be in a state of conditional conditions. Typically, 2±3 grid points fall within the instability (Gray 1968) averaging radius. We had also examined averaging radii of 4Њ,6Њ, and 8Њ, but found much greater classi®cation Note that these are necessary but not suf®cient condi- skill with a 2Њ radius. The lone exception was MPIÐ tions for genesis. The thermodynamic parameters are we found enhanced predictive abilities when we in- nearly always favorable during the Atlantic hurricane cluded MPI averaged at a 6Њ radius. Thus, all MPI cal- season (Gray 1968), and yet TCG is still a rare event. 2932 MONTHLY WEATHER REVIEW VOLUME 131

Nevertheless, we selected eight large-scale predictors values are thought to correspond to more (less) favor- that we believed captured the crucial requirements of able conditions for development. MPI was chosen in day-to-day TCG. Each predictor is brie¯y described be- lieu of SST as a thermodynamic predictor because it low, along with the large-scale requirement (numbers captures conditions in the upper troposphere that are 1±6 above) that it satis®es. The condition of preexisting important for determining the capacity of the system in convection (1) is satis®ed by default since we are only establishing a warm core [see Holland (1997) for a more considering tropical cloud clusters as de®ned in the pre- thorough discussion]. vious section.

4) 925±850-HPA MOISTURE DIVERGENCE 1) LATITUDE Latitude satis®es condition 2. We include latitude in To examine the large-scale low-level moisture con- our set of predictors as a scaled Coriolis parameter vergence into the cluster, the moisture divergence at 925 (COR), computed as: and 850 hPa was calculated, averaged together, and then scaled so that the magnitude was comparable to the COR f ϭ 2␻ sin␾ϫ104 , (1) and DGP predictors. Moisture divergence (MDIV) is where ␻ is the angular rotation of the earth (ϭ7.29 ϫ calculated as 10Ϫ5 sϪ1), and ␾ is latitude in degrees. (١r, (3 ´ V ϩ V´MDIV ϭ r١

2) DAILY GENESIS POTENTIAL where r is the mixing ratio, and V is the total horizontal wind. Lower (higher) values indicate more (less) fa- McBride and Zehr (1981) found that the development vorable conditions for genesis. potential for a cloud cluster could be assimilated into one variable, which they called the ``daily genesis potential.'' It was formulated based on the conclusion of 5) PRECIPITABLE WATER their study that TCG required near-zero vertical wind shear near the storm center and a strong vertical shear The columnar precipitable water (PWAT; taken di- gradient across it. DGP can be calculated as the vertical rectly from the NNR dataset) addresses requirement 4. gradient of relative vorticity, or It is important for the midlevels of the atmosphere to be moist so that the rising saturated air does not evap- DGP ϭ ␨ Ϫ ␨ , (2) 900 200 orate and hence cool the environment. This process will where ␨ 900 and ␨ 200 are the 900-hPa and 200-hPa relative lead to a stabilization of the sounding and the suppres- vorticities. Since the NNR does not contain a 900-hPa sion of the additional convection needed to ultimately pressure level, the 850-hPa level was substituted. A form a warm core. higher (lower) DGP indicates a more (less) favorable development environment. To verify the importance of the 200-hPa level contribution to the DGP, we per- 6) 24-H PRESSURE TENDENCY formed classi®cations using just the 850-hPa relative vorticity as a predictor. We found that the Heidke skill The 24-h pressure tendency (PTEND) predictor does scores (HSSs; Wilks 1995) were 0.1±0.25 higher with not speci®cally address a large-scale requirement for the 200-hPa level included, indicating important infor- genesis; rather, it is included as a diagnostic predictor. mation was gained from the consideration of both levels. Forecasters typically evaluate the pressure changes in cloud clusters when assessing the probability of genesis (R. Pasch 1999, personal communication). In this study 3) MAXIMUM POTENTIAL INTENSITY we use the 24-h pressure tendency to eliminate the MPI is de®ned as the theoretical upper limit on in- strong diurnal signal in the surface pressure ®eld. Be- tensity that a mature tropical cyclone could attain [see cause of the nature of the predictor (information is need- Camp and Montgomery (2001) for a review]. In the ed 24 h prior to the analysis time), there were several Holland (1997) formulation, which is used here, the MPI instances when this predictor was not available for use. can also be used as a proxy for SST and the stability Also, it was discovered that the pressure tendency ex- of the atmospheric column, accounting for requirements hibited very little if any differentiation between DV and 5 and 6 from above. The Holland MPI is highly sensitive ND clusters at lead times greater than 24 h before gen- to changes in SST, especially in the ranges typically esis. For these two reasons, pressure tendency was only seen in the tropical Atlantic during the hurricane season considered for DV clusters 6±24 h prior to genesis. (26Њ±29ЊC). The solution is obtained from a columnar Those cases were then compared to all of the ND cases temperature pro®le, a surface temperature (typically for which pressure tendency data were available in the SST Ϫ 1 K), and surface pressure by means of an it- discriminant analysis. PTEND was computed in a La- erative process. In terms of pressure, lower (higher) MPI grangian frame of reference. DECEMBER 2003 HENNON AND HOBGOOD 2933

7) 6-HSURFACEAND700-HPARELATIVE tively, the 12-h forecasts will be somewhat less skillful VORTICITY TENDENCIES than the 6-h forecasts since the DV cases at 12 h re- semble the ND cases to a greater degree. This effect Two other diagnostic predictors are included in the becomes increasingly prominent at longer forecast same spirit as pressure tendency: the 6-h surface times. (VTENDSFC) and 700-hPa (VTEND700) relative vorticity tendencies. Positive ``vorticity spinup'' indicates a strengthening of the circulation and thus more favorable 4. Results conditions for warm-core development. For simplicity in the calculations, the vorticity tendencies, unlike pres- a. Composite qualitative prediction sure tendency, were computed from an Eulerian frame of reference. This simpli®cation means that the vorticity The DA output gives a probability (P) of a case be- tendency values may be partially due to the movement longing to the DV group given the values of its pre- of the system rather than vorticity spinup or spindown. dictors. To evaluate the performance and usefulness of Consider a worst-case scenario of a fast-moving easterly the predictions, we strati®ed all cases into ®ve bins by wave (10 m sϪ1). The system would still traverse less this probability value. Table 2 lists each bin, the number distance in a 6-h period (about 216 km) than the grid of cases that were grouped in that bin by forecast hour, spacing (ϳ277.5 km) of the data. Given this and the and the number of those cases that developed into a fact that additional forecast skill was harnessed from tropical depression. For example, bin 1 lists all of the the use of the relative vorticity tendency predictors, we cases where the DA procedure predicted DV group believe that this simpli®cation is acceptable. membership with a probability of 0.9 or higher. For the 24-h forecast period, 24 cases met this criterion. Of those 24, 10 developed into tropical depressions 24 h e. Statistical technique later, a development percentage of 41.7. For lower P The sorting of cases into DV and ND groups is ac- forecasts at the same forecast time, the development complished through the use of linear discriminant anal- percentage drops from 21.4 (bin 2) to 1.1 (bin 5). ysis (DA), of which a thorough examination is given in Table 2 reveals an interesting aspect of the classi®- Tabachnick and Fidell (2001). This technique has been cation results. The formation rate of tropical depressions used in several other classi®cation studies in meteorol- is still less than 50% even when the cluster environment ogy with success (e.g., Perrone and Lowe 1986; Els- is especially favorable (corresponding to probabilistic berry and Kirchoffer 1988; DeGaetano et al. 2002). Giv- forecasts P Ͼ 0.9). If one were to rely on these results en a set of normally distributed and independent pre- alone, the false alarm rate (FAR; Wilks 1995) would be dictors and their corresponding group membership, a far too great for a skillful forecast. If the probability of function is derived that maximally separates the groups development drops below 0.7 (as it is with over 90% through a linear combination of the predictor variables. of all cases), TCG almost never occurs, and the fore- The resulting ``discriminant function'' can then be ap- caster could make a rather con®dent forecast given this plied to an independent set of predictors that the user situation. If one uses the value of 0.7 as a ``decision wishes to classify. An alternative method is to classify boundary'' between a 1 and 0 forecast, the probability each case in the dataset by the ``leave one out'' method. of detection (POD; Wilks 1995) ranges from 64% at the That is, the discriminant function is computed with all 6-h forecast period to 37% at the 48-h forecast period. cases except one, and then that case is classi®ed against The FAR ranges from 4% (6 h) up to 8% for other the function. Then the procedure repeats for all of the forecast periods.1 cases. This was the method employed in this study. We Given the development percentage values in Table 2, assumed equal prior probabilities of development. It we can categorize the development likelihood given the should be noted that the DA requirement of multivariate probabilistic prediction of development by the DA pro- normality in this case is not satis®ed. This does not cedure. This is shown in the far-right column of Table invalidate the results but may degrade the classi®cation 2. If the probability of development P Ն 0.9, we label ability of the technique (Tabachnick and Fidell 2001). the cluster as having a ``good'' chance of developing. The group classi®cation was made in the following If 0.8 Յ P Ͻ 0.9, the cluster has a ``fair'' chance of manner. The discriminant analysis was run eight times, developing. If P Ͻ 0.7, development is unlikely to ex- one for each forecast period (6±48 h). For example, all tremely unlikely. These qualitative labels will be used DV cloud clusters that developed into tropical depres- in the case study sections presented later. sions 6 h after the image time were classi®ed with all nondeveloping clusters, regardless of time of year. This produced a set of ``6-h classi®cations.'' Then, all clus- 1 The selection of a decision boundary is usually found by cal- culating a series of forecast skill scores for a range of possible bound- ters that developed into depressions 12 h after the image aries and then choosing the boundary where the maximum scores time were classi®ed with the same set of nondeveloping occur [see Wilks (1995) for more information]. Theoretically, this clusters, producing a ``12-h classi®cation'' set. Intui- would minimize the FAR and maximize the POD further. 2934 MONTHLY WEATHER REVIEW VOLUME 131

TABLE 2. Development rate by probabilistic prediction on average by COR (latitude). This was expected (though and forecast hour. maybe to a lesser degree) and reemphasizes the impor- DV tance of the large-scale dynamics to TCG. The weakest

Forecast No. percentage DV predictors overall were the VTEND700, MPI, and PWAT. hour of cases Developed (%) likelihood The vorticity tendency signal was relatively weak in most Bin 1 (P Ն 0.9) instances and showed little differentiation (as did PWAT) 6 36 16 44.4 Good between DV and ND clusters. This result emphasizes the 12 27 12 44.4 need to consider the vertical change of vorticity (as in 18 30 14 46.7 the DGP) instead of simply the lower-level vorticity when 24 24 10 41.7 30 29 10 34.5 assessing the potential for TCG. In fact, the correlation 36 37 9 24.3 was negative at a couple of the forecast times, indicating 42 26 8 30.8 that little con®dence should be given to them. The sig- 48 18 1 5.6 ni®cance of the MPI was low for nearly all forecast times. Bin 2 (0.8 Յ P Ͻ 0.9) This seems to con®rm, as shown in Gray (1968) and 6 16 6 37.5 Fair McBride and Zehr (1981), that for daily TCG prediction 12 36 7 19.4 during the hurricane season the thermodynamic environ- 18 26 6 23.1 24 28 6 21.4 ment is similar for nearly all candidate cloud clusters. 30 76 7 9.2 Therefore, proxies for the thermodynamic environment 36 57 10 17.5 appear to be of little use within this type of prediction 42 44 3 6.8 system, although predictions did degrade slightly when 48 49 6 12.2 the MPI was removed as a predictor.2 Bin 3 (0.7 Յ P Ͻ 0.8) 6 18 4 22.2 Unlikely 12 43 2 4.7 c. Statistical measures of skill 18 30 1 3.3 24 42 0 0 There are known problems when applying traditional 30 92 6 6.5 measures of probabilistic forecast performance to rare- 36 85 3 3.5 42 124 7 5.6 event situations such as TCG (Marzban 1998). For ex- 48 115 5 4.3 ample, a better Brier score can be obtained by blindly Bin 4 (0.5 Յ P Ͻ 0.7) forecasting nondevelopment for all events. Neverthe- 6 50 1 2.0 Very unlikely less, we calculated the threat and Brier scores for the 12 106 3 2.8 composite dataset for comparison purposes with the Per- 18 93 3 3.2 rone and Lowe (1986) study and to ®nd differences in 24 144 2 1.4 performance across forecast periods. The threat score, 30 326 7 2.1 typically used to evaluate qualitative precipitation fore- 36 284 7 2.5 42 385 7 1.8 casting, is calculated as 48 471 9 1.9 C Bin 5 (P Ͻ 0.5) THREAT ϭ , (4) 6 1108 8 0.7 Extremely unlikely A ϩ B Ϫ C 12 1015 10 1.0 18 1045 7 0.7 where A is the number of DV forecasts made, B is the 24 984 11 1.1 number of DV cases observed, and C is the number of 30 1781 9 0.5 correct DV cases. A threat score of 1 is a perfect score 36 1840 9 0.5 for a suite of forecasts; a score of 0.5 or higher is con- 42 1721 10 0.6 48 1643 11 0.7 sidered a highly skilled forecast. To compute threat scores, we de®ned a DV forecast as one in which the probability of DV was at least 0.7 (as discussed previ- b. Signi®cance of predictors ously and similar to Perrone and Lowe's P Ն 0.65 boundary). Figure 3 shows the threat scores for the 6±48-h Although it is dif®cult to extract signi®cance infor- forecast periods. They range from 0.329 at 6 h to 0.059 mation from DA predictors, it is possible to examine at 48 h. These values are signi®cantly lower than the the strength of the relationship each predictor has with scores computed by Perrone and Lowe (1986). This may the classi®cation output. Table 3 shows the correlation between each predictor and the output of the classi®- cation. Higher values for a predictor indicate a more 2 The 1998±2000 seasons were similar in that each had higher than signi®cant role in determining group membership and normal SSTs. The MPI predictor may be more useful for seasons in which the tropical Atlantic SSTs were cooler than normal. This may hence a greater degree of separation between developing be especially true for early-season systems, when development is and nondeveloping groups. For all forecast hours, the typically limited by the thermodynamic environment (DeMaria et al. DGP was by far the most signi®cant predictor, followed 2001). DECEMBER 2003 HENNON AND HOBGOOD 2935

TABLE 3. Correlations between predictor value and classi®cation prediction for each forecast hour. Rank is given in parentheses for each forecast hour. Avg Hour 6 12 18 24 30 36 42 48 rank DGP 0.864 0.815 0.813 0.781 0.830 0.859 0.860 0.880 1.0 (1) (1) (1) (1) (1) (1) (1) (1) COR 0.279 0.343 0.323 0.260 0.495 0.424 0.432 0.360 2.4 (3) (3) (2) (3) (2) (2) (2) (2) PTEND Ϫ0.211 Ϫ0.356 Ϫ0.286 Ϫ0.197 N/A N/A N/A N/A 3.7 (5) (2) (3) (5) MDIV Ϫ0.232 Ϫ0.130 Ϫ0.281 Ϫ0.415 Ϫ0.201 Ϫ0.94 Ϫ0.100 Ϫ0.128 4.0 (4) (5) (4) (2) (3) (5) (4) (5)

VTENDSFC 0.307 0.129 0.194 0.231 0.030 Ϫ0.056 0.100 Ϫ0.079 5.0 (2) (6) (5) (4) (6) (6) (4) (7)

VTEND700 Ϫ0.012 0.242 0.017 0.189 0.136 0.259 0.057 Ϫ0.194 5.5 (8) (4) (8) (6) (5) (3) (7) (3) MPI Ϫ0.260 Ϫ0.075 Ϫ0.160 Ϫ0.097 Ϫ0.145 Ϫ0.110 Ϫ0.263 Ϫ0.116 5.6 (7) (8) (6) (7) (4) (4) (3) (6) PWAT 0.209 0.111 0.130 0.068 0.002 0.016 Ϫ0.076 Ϫ0.131 6.5 (6) (7) (7) (8) (7) (7) (6) (4) be attributed to differences in the methodologies of the and indicate some value in the probabilistic forecast studies and will be discussed further in section 5. method derived here. The Brier score (Brier 1950) is a measure of skill for a probabilistic forecasting system. It is essentially the sum of the squared probability errors: d. Tropical Depression Keith (2000) case study 1 N We now present two case studies that highlight the 2 BS ϭ ( fiiϪ O ) , (5) strengths and weaknesses of the forecast system. Trop- N ͸ iϭ1 ical Depression Keith (2000) was an easterly wave that where N is the number of forecasts, f i is the forecast initially left the west coast of Africa on 16 September. probability (from the DA) of the occurrence of the ith Figure 4 is a plot of the track of the cloud cluster that event, and Oi is the observed value of the event (1 ϭ eventually developed into Keith. This plot illustrates one DV; 0 ϭ ND). The lower (higher) the Brier score, the of the major limitations of the ``cloud cluster tracking more (less) skillful the probabilistic forecast. A Brier by convection'' method. Convection remained strong score of 0 indicates a perfect suite of forecasts. The across much of the Atlantic but development did not Brier scores for each forecast periods are shown in Fig. occur. When the convection dissipated, the cluster was 3. They are of similar magnitude to Perrone and Lowe declared dead in the database. But the wave remained

FIG. 3. Threat (solid line with diamond points) and Brier (dotted line) scores for each forecast time. These values re¯ect the entire dataset of cloud clusters. 2936 MONTHLY WEATHER REVIEW VOLUME 131

FIG. 4. Track of a tropical cloud cluster that would eventually form into TD 15 (2000), the future Hurricane Keith. intact, unable to be tracked because of the absence of ``jump'' erratically as convection is generated in a dif- any signi®cant convection, and by 27 September the ferent region. wave began showing signs of intensi®cation in the Ca- We ran the classi®cation program for each time plot- ribbean Sea as a new convective phase began. This re- ted on Fig. 4Ðall of the case times in the eastern and generation of convection and subsequent intensi®cation central Atlantic were ND cases and those in the Carib- led to its declaration of tropical depression status at 1800 bean Sea were DV cases. Figure 5 shows the series of UTC 28 September. This system was treated as two eight 6-h forecasts generated for each image time of the separate clusters until the tropical cyclone report was cloud cluster as it progressed across the Atlantic. The consulted on the NHC website. We believe this type of shaded region corresponds to a P(DV) Ͻ 0.7, from situation is a common one, but because of time con- which the development likelihood can be interpreted straints and the subjective nature of the problem we from Table 2 as ``unlikely'' to ``extremely unlikely.'' thought it best to consider these types of situations as Genesis did not occur during this period, despite the separate entities except when alternative sources (such impressive convection at times seen from the IR im- as the NHC tropical cyclone reports) clarify the situa- agery, because the cluster environment was unfavorable. tion. Note also the irregular jumps in the track of the If we examine the overall trends, we see a general im- cloud clusterÐthis is a typical problem with ®xing cen- provement of conditions from 17 September through 20 ters in tropical waves, as the perceived center tends to September, followed by a 2-day deterioration. This was

FIG. 5. Series of 6-h probabilistic development forecasts for the cloud cluster that achieved genesis on 1800 UTC 28 Sep (TD 15). The shaded region, which corresponds to development probabilities of Ͻ0.7, delineates the area that corresponds to an ``unlikely'' to ``extremely unlikely'' chance of development. DECEMBER 2003 HENNON AND HOBGOOD 2937

FIG. 6. Track of tropical cloud cluster ND6 (2000) from 1200 UTC 3 Jun to 0600 UTC 7 Jun. primarily due to unfavorable trends in the vertical wind Figure 7 shows that the forecast system recognized shear structure and marginal MPI. Convection became the favorable environment at this stage and forecast a so disorganized and sparse by 25 September that the high probability for development. The ®rst forecast wave became untraceable via IR imagery despite the made at 1800 UTC 4 June shows a ``good'' chance of improving cluster environment. By late on 26 Septem- development in 24±30 h. The following two forecasts ber, convection was regenerated within a much more were also predicting genesis around 0000 UTC 6 June. favorable dynamic and thermodynamic environment, as But throughout the day on 6 June, convection decreased, evidenced by a large majority of the development prob- and by 7 June the cluster could not be resolved on the abilistic forecasts in the range of 0.7 to 0.99 (``fair'' to IR imagery. The forecasts during 6 June were signi®- ``good'' chance of development). Almost all predictors cantly less favorable, with most probabilities centered at this time were favorable, especially DGP, MPI, and around 0.5, thus showing some indication that the fore- PTEND. Genesis occurred at 1800 UTC 28 September; cast system was sensing a decline in the favorable clus- about 48 h after the convection associated with this long- ter environment that had been in place. lasting wave ®rst reappeared in the Caribbean. Why did the system predict a ``fair'' to ``good'' Keith was a midseason storm that was forecast very chance of development, and yet the cluster did not un- well by the system. We have found that the system in dergo TCG? The probable answer illustrates the com- general does well with the ``prime'' development season plexity of the process. First, the only large-scale param- (August±early October), in which the highest percent- eter that was marginal/unfavorable during the entire life ages of storms form. However, there were several in- cycle of the cluster was COR, with values of 0.75±1.93 stances of cloud clusters, especially those that formed (average COR for DV systems was about 4.5). As men- early (June) or late (October±November) in the season, tioned previously in section 4b, latitude was on average that were not handled with as much skill. The next case the second most important predictor (i.e., second highest study reveals one such storm and leads to a discussion correlation with the predicted result) in the DA. How- in section 6 on strategies that may be employed to im- ever, the correlation magnitude was only on the order prove the forecasting in these situations. of 0.25±0.40 for each forecast time (compared to 0.70± 0.85 for DGP). Thus, if the DGP was high, as it was in this case, it is unlikely the forecast system would e. ND6 (2000) cloud cluster case study predict nondevelopment unless almost all of the other The sixth cloud cluster of the 2000 season was an predictors were unfavorable. We believe this may be a easterly wave that moved off Africa with a somewhat limitation of the linearity of the DA; we suspect that a disorganized convective structure on 3 June. Figure 6 quadratic or nonlinear classi®cation scheme could do a illustrates the west-southwest track of the cluster over better job in these types of situations. Another reason- the next few days. During the day on 4 June, convection able explanation is the neglect of the smaller-scale fea- increased in intensity, and a large (ϳ5Њ diameter) cloud tures and processes within the cloud cluster that cannot shield formed. During these early stages of the cluster be resolved in this type of analysis. Work on incorpo- life cycle, most of the large-scale conditions were fa- rating new satellite tools to resolve mesoscale processes vorable for development. The DGP was in the 1.0±2.0 is an exciting new avenue for research in this area. range (the mean DGP for all developing clusters during the 1998±2000 seasons was 0.8±1.4 for 48- to 6 hour 5. Discussion forecasts), and the moisture convergence and precipitable water were very favorable (signi®cantly above av- The reader is encouraged to digest Perrone and erage) as well. As diagnosed by the MPI, the cluster Lowe's TCG work with Paci®c cloud clusters (Perrone was over SSTs suf®cient for development. and Lowe 1986), as there are many parallels to this 2938 MONTHLY WEATHER REVIEW VOLUME 131

FIG. 7. As in Fig. 5, except for nondeveloping cluster ND6 (2000). research. But there are several crucial differences that lection criteria of 4Њ diameters and persistence for 24 h bring about signi®cantly different results, especially in eliminates smaller, more transient features that have lit- the statistical scores. First, they are predicting tropical tle likelihood of developing into a tropical depression. storm formation. We think this improved their classi- Since DA works best with a high degree of separation ®cation rate since the differences between developing between predictors, we think that the inclusion of small- and nondeveloping clusters are likely to be greater dur- er, transient cloud features might arti®cially in¯ate the ing the time the cluster is a depression. In our study, prediction success rate. TCG is assumed to occur when a cluster makes the Third, they chose some predictors that are important transition to a tropical depressionÐthe transition from from a climatological point of view rather than those a tropical depression to a tropical storm is assumed to with a day-to-day focus. This may have hurt their model be the intensi®cation of a preexisting tropical cyclone. performance, as several of their predictors are not nec- The smaller differences between developing and non- essarily the best ones for daily genesis studies. For ex- developing tropical disturbances make statistical differ- ample, vertical wind shear is highly correlated with trop- entiation more challenging, but they form the core of ical cyclone formation on a seasonal scale. But as has the forecast problem. been shown in several studies (e.g., McBride and Zehr Second, Perrone and Lowe used a more relaxed pro- 1981), tropical cyclones frequently form in high-shear cedure for the selection of their ND cases. They had a environments. That there is near-zero shear over the size requirement of only 1Њ diameter, 4 times smaller cluster itself is the important criterion. The importance than the requirement in this study. In addition, it is not of the DGP in this study clearly validates this statement. clear if their clusters met a persistence requirement nor was there any mention of cloud structures that may have 6. Summary, conclusions, and future work been associated with other systems. If we de®ne a ``cloud cluster'' as a convective entity that satis®es the A prediction system for tropical cyclogenesis is de- condition of ``preexisting convection'' for TCG, it is veloped using large-scale predictors derived from the quite possible that Perrone and Lowe included clusters NCEP±NCAR reanalysis. Using IR satellite imagery for in their dataset that we would not consider as such. the Atlantic tropical cyclone seasons of 1998±2000, can- Although midget typhoons may form from small clus- didate cloud clusters that satisfy predetermined char- ters in the western Paci®c (Lander 1994), TCG normally acteristics are identi®ed and their development status occurs from larger clusters over the Atlantic. The se- determined. Eight predictors are then computed for each DECEMBER 2003 HENNON AND HOBGOOD 2939

6-h period of the cluster's life cycle. The entire dataset James Franklin, and Stacy Stewart at the Tropical Pre- are independently classi®ed using a linear discriminant diction Center and Commander Marge Nordman at the analysis technique. Joint Typhoon Warning Center. Their insights convinced Composite results showed that genesis was a good us that there was a need for this type of research. This possibility if the probabilistic forecast P Ͼ 0.7 and un- paper also bene®ted from discussions with Drs. Jeffrey likely if P Ͻ 0.5. The discriminant function loadings Halverson, John Rayner, Caren Marzban, and Hugh Wil- revealed that the DGP was the most signi®cant predictor loughby. We would also like to thank two anonymous at all forecast times, followed by COR. Statistical mea- reviewers who provided many bene®cial comments and sures of skill were somewhat lower than a previous suggestions that greatly enhanced this paper. study, possibly because of critical differences in the def- inition of genesis and the selection of cloud clusters. A REFERENCES case study of the cloud cluster that eventually formed into Hurricane Keith (2000) showed that the predication Beven, J. L., 1999: The boguscaneÐA serious problem with the system does an excellent job at capturing the evolution NCEP medium range forecast model in the Tropics. Preprints, of the large-scale ®elds important to TCG, despite the 23d Conf. on Hurricanes and Tropical Meteorology, Dallas, TX, Amer. Meteor. Soc., 845±848. limitations of linearity of the analysis method and un- Bister, M., and K. A. Emanuel, 1997: The genesis of Hurricane Guil- certainties in the track of the system. A separate look lermo: TEXMEX analyses and a modeling study. Mon. Wea. at a nondeveloping cluster highlighted those limitations Rev., 125, 2662±2682. and emphasized the need for a more detailed look at Bracken, W. E., and L. F. Bosart, 2000: The role of synoptic-scale ¯ow during tropical cyclogenesis over the North Atlantic Ocean. the smaller-scale features and processes within the clus- Mon. Wea. Rev., 128, 353±376. ters. Brier, G. W., 1950: Veri®cation of forecasts expressed in terms of We believe we have shown that a large degree of probability. Mon. Wea. Rev., 78, 1±3. predictability of genesis can be derived from a simple Camp, J. P., and M. T. Montgomery, 2001: Hurricane maximum in- analysis of the cloud cluster using large-scale data at tensity: Past and present. Mon. Wea. Rev., 129, 1704±1717. Cecil, D. J., E. J. Zipser, and S. W. Nesbitt, 2002: Re¯ectivity, ice the analysis time. We choose predictors that are easy to scattering, and lightning characteristics of hurricane eyewalls calculate and data that are regularly available in hourly and rainbands. Part I: Quantitative description. Mon. Wea. Rev., analysis ®elds. An interesting future study would be to 130, 769±784. evaluate TCG forecasts on numerical model forecast Chen, S. S., and W. M. Frank, 1993: A numerical study of the genesis of extratropical convective mesovortices. Part I: Evolution and ®elds rather than analysis ®elds, similar to the latest dynamics. J. Atmos Sci., 50, 2401±2426. operational version of the Statistical Hurricane Intensity Davis, C. A., and L. F. Bosart, 2001: Numerical simulations of the Prediction Scheme (SHIPS; DeMaria and Kaplan 1999). genesis of Hurricane Diana (1984). Part I: Control simulation. Several concerns would have to be addressed, speci®- Mon. Wea. Rev., 129, 1859±1881. cally, how well classi®cations can be made on pure DeGaetano, A. T., M. E. Hirsch, and S. J. Colucci, 2002: Statistical prediction of seasonal East Coast winter storm frequency. J. model data and whether the model can accurately fore- Climate, 15, 1101±1117. cast the track and intensity of the convection. We believe DeMaria, M., and J. Kaplan, 1999: An updated statistical hurricane one of the biggest de®ciencies with the current model intensity prediction scheme (SHIPS) for the Atlantic and eastern is the representation of moisture in the reanalysis data. North Paci®c basins. Wea. Forecasting, 14, 326±337. ÐÐ, J. A. Knaff, B. H. Connell, 2001: A tropical cyclone genesis Making forecasts from model ®elds rather than analysis parameter for the tropical Atlantic. Wea. Forecasting, 16, 219± ®elds would magnify this problem. 233. We believe using improved reanalysis data supple- Elsberry, R. L., and P. J. Kirchoffer, 1988: Upper-level forcing of mented by improved moisture ®elds from satellite sen- explosive cyclogenesis over the ocean based on operationally sors will make the biggest improvements in this model. analyzed ®elds. Wea. Forecasting, 3, 205±216. Emanuel, K. A., 1989: The ®nite-amplitude nature of tropical cyclo- To ascertain any improvement with a nonlinear classi- genesis. J. Atmos. Sci., 46, 3431±3456. ®er, we have applied the cloud cluster dataset to a prob- Franklin, J. L., L. A. Avila, J. L. Beven, M. B. Lawrence, R. J. Pasch, abilistic neural network. Results were mixed and will and S. R. Stewart, 2001: Atlantic hurricane season of 2000. Mon. be reported at a later time. Ultimately, the application Wea. Rev., 129, 3037±3056. Gray, W. M., 1968: Global view of the origin of tropical disturbances of the system to operational analyses will be the true and storms. Mon. Wea. Rev., 96, 669±700. test of its usefulness. If results prove bene®cial, the Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of development of tools such as a graphical user interface the ®fth-generation Penn State/NCAR Mesoscale Model (MM5). will give forecasters near-real-time objective guidance NCAR Tech. Note 398, 121 pp. to tropical cyclone formation. Holland, G. J. 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 2519±2541. Jarvinen, B. R., C. J. Neumann, and M. A. S. Davis, 1984: A tropical Acknowledgments. The authors wish to thank EU- cyclone data tape for the North Atlantic basin, 1886±1983: Con- METSAT and the Space Science and Engineering Cen- tents, limitations, and uses. NOAA Tech. Memo. NWS NHC- ter at the University of WisconsinÐMadison for pro- 22, 21 pp. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re- viding the satellite imagery used in this research. We analysis Project. Bull. Amer. Meteor. Soc., 77, 437±471. are also grateful for the forecasting perspective of trop- Kidder, S. Q., M. D. Goldberg, R. M. Zehr, M. DeMaria, J. F. W. ical cyclogenesis given to us by Dr. Richard Pasch, Purdom, C. S. Velden, N. C. Grody, and S. J. Kusselson, 2000: 2940 MONTHLY WEATHER REVIEW VOLUME 131

Satellite analysis of tropical cyclones using the Advanced Mi- ÐÐ, J.-G. Jiing, F. M. Horsfall, H.-L. Pan, and N. Surgi, 2002: crowave Sounding Unit (AMSU). Bull. Amer. Meteor. Soc., 81, Forecasting tropical cyclogenesis in the NCEP global model. 1241±1260. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, Lander, M. A., 1994: Description of a monsoon gyre and its effects San Diego, CA, Amer. Meteor. Soc., 178±179. on the tropical cyclones in the western North Paci®c. Wea. Fore- Perrone, T. J., and P. R. Lowe, 1986: A statistically derived prediction casting, 9, 640±654. procedure for tropical storm formation. Mon. Wea. Rev., 114, Lawrence, M. B., L. A. Avila, J. L. Beven, J. L. Franklin, J. L. Guiney, 165±177. and R. J. Pasch, 2001: Atlantic hurricane season of 1999. Mon. Reynolds, R. W., 1988: A real-time global sea surface temperature Wea. Rev., 129, 3057±3084. analysis. J. Climate, 1, 75±86. Lee, C. S., 1989: Observational analysis of tropical cyclogenesis in ÐÐ, and T. M. Smith, 1994: Improved global sea surface temperature the western North Paci®c. Part I: Structural evolution of cloud analysis using optimum interpolation. J. Climate, 7, 929±948. clusters. J. Atmos. Sci., 46, 2580±2598. Riehl, R. J., 1948: On the formation of typhoons. J. Meteor., 5, 247± Lowe, P. R., and T. J. Perrone, 1989: An operational technique for 264. prediction tropical storm formation in the western North Paci®c Ritchie, E. A., and G. J. Holland, 1997: Scale interactions during the Ocean. Natl. Wea. Dig., 14, 5±13. formation of Typhoon Irving. Mon. Wea. Rev., 125, 1377±1396. Maddox, R. A., 1981: The structure and life-cycle of midlatitude Sharp, R. J., M. A. Bourassa, and J. J. O'Brien, 2002: Early detection of tropical cyclones using SeaWinds-derived vorticity. Bull. mesoscale convective complexes. Atmospheric Science Paper Amer. Meteor. Soc., 83, 879±889. 336, Dept. of Atmospheric Science, Colorado State University, Simpson, J., E. Ritchie, G. J. Holland, J. Halverson, and S. Stewart, 311 pp. 1997: Mesoscale interactions in tropical cyclone genesis. Mon. Marzban, C., 1998: Scalar measures of performance in rare-event Wea. Rev., 125, 2643±2661. situations. Wea. Forecasting, 13, 753±763. Tabachnick, B. G., and L. S. Fidell, 2001: Discriminant function McBride, J. L., and R. Zehr, 1981: Observational analysis of tropical analysis. Using Multivariate Statistics, R. Pascal, Ed., Allyn and cyclone formation. Part II: Comparison of non-developing versus Bacon, 456±516. developing systems. J. Atmos. Sci., 38, 1132±1151. Trenberth, K. E., D. P. Stepaniak, J. W. Hurrell, and M. Fiorino, 2001: Montgomery, M. T., and J. Enagonio, 1998: Tropical cyclogenesis Quality of reanalysis in the Tropics. J. Climate, 14, 1499±1510. via convectively forced Rossby waves in a three-dimensional Tuleya, R. E., 1991: Sensitivity studies of tropical storm genesis using quasigeostrophic model. J. Atmos. Sci., 55, 3176±3207. a numerical model. Mon. Wea. Rev., 119, 721±733. Pasch, R. J., L. A. Avila, and J. L. Guiney, 2001: Atlantic hurricane Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. season of 1998. Mon. Wea. Rev., 129, 3085±3123. Academic Press, 467 pp.