Cloud Clusters and Tropical Cyclogenesis: Developing and Nondeveloping Systems and Their Large-Scale Environment

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1 192 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 Cloud Clusters and Tropical Cyclogenesis: Developing and Nondeveloping Systems and Their Large-Scale Environment BRANDON W. KERNS AND SHUYI S. CHEN Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida (Manuscript received 19 September 2011, in final form 13 July 2012) ABSTRACT Tropical cyclone (TC) genesis occurs only when there is persistent, organized convection. The question of why some cloud clusters develop into a TC and others do not remains unresolved. This question cannot be addressed adequately without studying nondeveloping systems in a consistent manner together with developing systems. This study presents a systematic approach in classifying developing and nondeveloping cloud clusters based on their large-scale environments. Eight years of hourly satellite IR data and global model analysis over the western North Pacific are used. A cloud cluster is defined as an area of #208-K cloud-top temperature, generally mesoscale in size. Based on the overlapping area between successive hourly images, they are then tracked in time as time clusters. The initial formations of nearly all TCs during July October were associated with time clusters lasting at least 8 h (8-h clusters). The occurrence of an 8-h cluster is considered to indicate the minimum degree of convective organization needed for TC genesis. A nondeveloping system is defined as an 8-h cluster that is considered to be a viable candidate for TC genesis, but was not associated with the TC genesis. The large-scale environmental conditions of cyclonic low-level vorticity, low vertical wind shear, low-level convergence, and elevated tropospheric water vapor are statistically more favorable for developing systems. Generally, the environment became more (less) favorable with time for the developing (nondeveloping) systems. Nevertheless, many developing (nondeveloping) systems formed (dissipated) in seemingly unfavorable (favorable) environments within a lead time of,24 h. 1. Introduction The dominant mode of organization of convection in the tropics is mesoscale convective systems (MCSs) with convective cores and stratiform precipitation (Houze 1977; Zipser 1977). Tropical cyclogenesis [(TC) genesis] the initial development of a self-sustaining, warm-core vortex does not occur without persistent convection and low-level convergence (Gray 1968). Much of our knowledge on TC genesis is based on developing systems that often occur in environments with enhanced low-level convergence, vorticity, and atmospheric moisture that are favorable for TC genesis. However, these favorable environmental conditions occur much more often than TC genesis events. What are the sufficient conditions for TC genesis, and what is the role of mesoscale Corresponding author address: Dr. Brandon Kerns, Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL bkerns@rsmas.miami.edu convective systems leading up to TC genesis? To answer these questions, it is necessary to study both developing and nondeveloping systems in an objective, consistent manner. a. Definition of TC genesis Part of the ambiguity in studying developing and nondeveloping systems is the definition of TC genesis. Frank (1987) defined TC genesis as the transition from a tropical disturbance to a tropical depression (TD), with a closed surface wind circulation. This is similar to the definition used by the National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC). The subsequent transformations of the TD to a tropical storm (TS) and the TS to a hurricane are referred as development and intensification, respectively. Others have proposed a two-stage genesis process: 1) large-scale development to describe the development of the environment favorable for TC genesis and 2) core formation (e.g., McBride 1995). Note that convective cloud systems can modulate their environment through moistening and DOI: /MWR-D Ó 2013 American Meteorological Society

2 JANUARY 2013 K E R N S A N D C H E N 193 heating. The interaction between deep convection and its environment is complex. The two stages of TC genesis may be impossible to separate in some cases. Because of the ambiguous nature of defining TC genesis and the general lack of detailed, high-resolution in situ data over tropical oceans, it is impossible to determine the exact time of TC genesis for most storms, even at the 6-h temporal resolution of best-track datasets. Indeed, different operational centers frequently have different TD initiation times in their best tracks for the same storm. In this study, the initial appearance of a TD or TS in the JTWC best track ( best-track genesis ) is used as the reference point for TC genesis. b. Large-scale environment and TC genesis The large-scale environment, including area-averaged vorticity, vertical wind shear, low-level convergence, and moisture content, strongly influences TC genesis (Gray 1968; DeMaria et al. 2001; Schumacher et al. 2009). However, the actual genesis process depends critically on convective systems of various scales and their interaction with the large-scale disturbance (Simpson et al. 1997, 1998; Houze 2010). Tory and Frank (2010) provide a detailed summary on various hypotheses on TC genesis from both large-scale and mesoscale perspectives. Tropical cyclone genesis is most favored in regions with elevated low-level relative vorticity and low vertical wind shear (Gray 1968; McBride and Zehr 1981; DeMaria et al. 2001). The Saharan air layer with dry air and high dust concentration may play a significant role in modulating TC formation in the Atlantic (Dunion and Velden 2004), though this is still a topic of active research (Braun 2010). Camargo et al. (2007) suggested that moisture is most likely to be a limiting factor in the Atlantic during El Niño years. While there are statistical differences between the environment of the developing and nondeveloping disturbances, they cannot be entirely distinguished based on the large-scale conditions, using the current generation of atmospheric analysis products (Perrone and Lowe 1986; Hennon and Hobgood 2003; Hennon et al. 2005; Kerns and Zipser 2009; Fu et al. 2012; Peng et al. 2012). The within-sample variance is comparable to or larger than the difference between the developing and nondeveloping sample means. Note that this appears to be the case regardless of the tracking method. These results can be interpreted to imply that the large-scale environment determines the likelihood of TC genesis, but other factors at smaller scales determine whether and when genesis occurs. That is, TC genesis is probabilistic to some extent (Simpson et al. 1998). It is also possible that a more favorable environment may simply indicate that the large-scale development component of TC genesis (McBride 1995) is under way, and therefore the inner core is more likely to form. Recently several studies have shown that a deep (up to ; hpa) meso-a-scale ( km) Lagrangian closed circulation in the wave-relative flow is favorable for TC genesis, especially in easterly waves (Dunkerton et al. 2009; Wang et al. 2009, 2012). The socalled Kelvin cat s eye configuration (referred to as a wave pouch ) is favorable because it affords a degree of protection from the harsh environment and provides a focus point for sustained deep convection and vorticity accumulation. c. Convective processes during TC genesis Several mechanisms have been identified for organization of convective systems into the core of a TC. Chen and Frank (1993) showed that long-lived MCSs can create inertially stable areas in saturated, stratiform rain regions. This is favorable for the development of the initial warm core and hydrostatic surface pressure falls because a greater fraction of the latent heat released contributes to column warming (Schubert and Hack 1982; Rogers and Fritsch 2001). Houze et al. (2009) found that large areas of saturated, mesoscale ascent led to the genesis of Hurricane Ophelia (2005). On the other hand, Bister and Emanuel (1997) proposed that a long-lived mesoscale rain area ( showerhead ) and associated evaporative cooling would lead to a saturated, cold-cored midlevel vortex. However, it is unclear how the cold core evolves into a warm-core system for TC genesis to occur. Ritchie and Holland (1993, 1997) emphasized the merger of multiple mesoscale convective vortices. Vortex merger results in increasingly deeper, stronger vortices, eventually leading to the development of a TD or TS. Simpson et al. (1997) present a case in which the interaction of multiple MCSs leads to development of a nascent eye (e.g., the initial warm-core vortex) in the subsidence regions between the convective systems. Dolling and Barnes (2012) also related the nascent eye of a TS to mesoscale subsidence. Harr et al. (1996) found that the initial round of MCSs modifies the subsynoptic environment such that the new MCSs organize in bands, leading to the development of some western North Pacific TCs. Hendricks et al. (2004) and Montgomery et al. (2006) emphasized the role of vortical hot towers associated with strong updrafts in convective cores, which is important for concentrating vorticity near the surface. In all the above studies as well as operational practice, persistent and organized convection is vital for TC genesis. However, the meaning of persistent and organized can be difficult to define and is qualitative in many of these studies.

3 194 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 d. Tracking developing and nondeveloping systems Two types of tracking methods have been used in previous studies: ones based primarily on dynamic fields and others based on infrared satellite data. Thorncroft and Hodges (2001) used an automated tracking method using filtered 850-hPa relative vorticity to develop a 20- yr climatology of Atlantic African easterly wave (AEW) activity. Hopsch et al. (2007, 2010) have also used this vorticity tracking technique. Kerns et al. (2008), Fu et al. (2012), and Peng et al. (2012) use variations of tracking filtered relative vorticity. Dunkerton et al. (2009) focus on the intersection of the wave trough and the critical layer, which is viewed as the focal point of TC genesis. The use of clouds as tracers for developing and nondeveloping disturbances in the tropics goes back to the first meteorological satellite images. Gray (1968) considered tropical disturbances to be distinctly organizedcloudandwindpatternsinthewidthrangeof km, which possess a conservatism in time of at least a day or more. This concept was apparently motivated by observations that midlatitude cloud patterns generally correspond with circulation features of similar scale (Hayden 1970). This viewpoint crystallized into the concept of the synoptic disturbance cloud cluster (Gray 1973; Ruprecht and Gray 1976; McBride 1981). Ruprecht and Gray (1976) described the cloud cluster quite qualitatively: it is a bright, solid white blob in satellite imagery. McBride (1981) described the synoptic disturbance cloud cluster as a loosely organized collection of deep convective clouds and covered...by a thick cirrus shield. Note that they used the same dataset as Ruprecht and Gray (1976), which had considerable limitations in terms of temporal resolution. Modern satellite data allows for a much more detailed analysis of convective systems through their life cycles. More recent studies using synoptic cloud cluster concept as a means to identify synoptic-scale disturbances include Perrone and Lowe (1986), Hennon and Hobgood (2003), Hennon et al. 2005, and Hennon et al. (2011). Hennon et al. (2011) described an objective method for tracking these synoptic-scale cloud systems globally. Cloud clusters may disappear for up to 12 h and still be tracked as the same system, as long as redevelopment occurs within a reasonable radius. Note that redevelopment of convection does not always imply coherent, continuous propagation of a synoptic weather system. In fact, large areas of cloudiness can occur without an easily identifiable synoptic-scale wind disturbance (Simpson et al. 1967, 1968). An alternative approach to objectively define and track cloud clusters is to focus on mesoscale organization. Williams and Houze (1987) defined cloud clusters using an infrared brightness temperature threshold of 208 K, tracking them in time as time clusters to study winter monsoon convection in the vicinity of Borneo. This definition of cloud clusters/time clusters emphasized coherent mesoscale convective systems, in contrast to the synoptic-scale cloud clusters discussed above. In this framework, the multiple convective burst events leading up to TC genesis observed by Zehr (1992) are related to the development and decay of individual mesoscale cloud clusters. The cloud cluster/time cluster tracking framework allows for quantifying multiscale variability from the mesoscale up to intraseasonal and interannual time scales (e.g., Mapes and Houze 1993; Chen et al. 1996; Chen and Houze 1997a,b). The cloud cluster identification and tracking used in these studies are completely automated and objective, in contrast to previous studies which relied on manual disturbance identification and tracking (McBride and Zehr 1981; Perrone and Lowe 1986; Hennon and Hobgood 2003; Kerns et al. 2008; Dunkerton et al. 2009; Fu et al. 2012; Peng et al. 2012). Defining developing and nondeveloping systems based on the cloud cluster tracking method using hourly satellite data is less ambiguous and easy to reproduce in both operational and research environments. The organization of this manuscript is as follows. The data used are described in section 2. The tracking of cloud clusters/time clusters and vorticity maxima are described in sections 3 and 4. Classification between developing and nondeveloping clusters is described in section 5. Section 6 presents an illustrative example of a developing and nondeveloping case. The results of the large-scale environment of developing and nondeveloping systems as well as their evolution in time are presented in sections 7 and 8. Finally, conclusions are given in section Data a. TC tracks The JTWC best track contains the 6-hourly coordinates and intensity of named TCs and unnamed depressions and subtropical cyclones. The best-track data are based on postseason reanalysis of the available data for each storm. Because of the need to infer storm initiation using satellite estimates (e.g., Dvorak 1975), the initial appearance of a TD or TS in the best-track data is considered to be a reference point for defining a developing system in this study, which may not necessarily be the precise time of TC genesis. The storms considered in this study occurred in N, E during July October, (Fig. 1). This includes: 84 typhoons, 39 tropical storms, and 17 tropical depressions. Of these, 4 typhoons and 1 tropical storm entered the study region from the east, and 3

4 JANUARY 2013 K E R N S A N D C H E N 195 FIG. 1. The number of TC genesis events within a 2.58 radius of each point (contours) and genesis locations (plus signs) for July October Contours are for every 1 event, starting from 3, and bold for 5 and above. typhoons formed in late June then tracked through the study region in July. Hurricane Ioke (2006) also entered the study region from the east. For these storms the precursors to TC genesis are not identified in this study. b. Satellite data The geostationary Multifunctional Transport Satellites (MTSAT) infrared (IR) channel ( mm) data from July October are used for identification of cloud clusters. The data are at (approximately 5 km near the equator) resolution. Hourly satellite data are used to track the evolution of cloud clusters through their life cycle. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) retrieved column-integrated water vapor [or total precipitable water (TPW)] and optimum interpolated sea surface temperature (SST) data are used for examining cloud cluster environmental moisture and SST. The retrieved data are provided by Remote Sensing Systems (RSS). For SST, daily fields are used. The daily fields are optimum interpolation composites of TRMM TMI data and AMSRE data at spatial resolution (Gentemann et al. 2004). They are corrected for the diurnal cycle to ;0800 local time using an empirical function of solar insolation, wind speed, and local time of observation (Gentemann et al. 2003). SST data are averaged over an area with a radius of 18, and valid observations must have at least 50% coverage. This criterion is effective as a land mask to eliminate cases near or over land. For the water vapor fields, the version 4 average of the past 3 days of TMI data (bmaps_v04 d3d data) is used. Because the tropical ocean is relatively moist in the boundary layer, most of the variance in TPW is due to variations in the midlevel water vapor content. The disadvantage of the polar-orbiting satellite TMI and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) data is the limited temporal sampling and gaps between swaths, as well as rain contamination. This is alleviated by using 3-dayaveraged data, at the expense of some temporal variability. Because of the 48 averaging radius used and the relatively small TPW gradients within the west Pacific (WPAC) warm pool, the loss of information from the temporal averaging does not significantly affect the results. For TPW, cases are included only if there are TPW data for at least half of the 48 radius. Cases without sufficient coverage (,50%), usually due to land coverage, are not considered in the statistical analysis of developing and nondeveloping cases. c. Global analysis data The National Centers for Environmental Protection (NCEP) final analysis (FNL) is used to identify and track TC-related vorticity maxima as well as vorticity maxima that did not develop into TCs. It is also used to determine the large-scale environment of the clusters such as the vertical wind shear and near-surface convergence. The FNL analysis incorporates additional observations that were not available for inclusion in the real-time NCEP Global Forecast System (GFS) analysis. However, because of the sparse observational network over most of the WPAC, the NCEP FNL relies heavily on the GFS model first guess. In particular, it is sensitive to the model convective parameterization. An upgrade to the GFS model physics, including convective parameterization, was implemented on 28 July 2010, which could potentially affect the results for the last 3 months of the 8-yr study. Nevertheless, the upgrade is not expected to affect the main conclusions of this study. The data are 6-hourly and at 18 resolution. Area-averaged fields are calculated from the analysis by averaging all grid points within a 68 radius from the center. The most recent available analysis data are used for each case (e.g., a case at 0500 UTC uses data from 0000 UTC). 3. Tracking cloud clusters a. Cloud clusters Following Williams and Houze (1987), cloud clusters are identified as contiguous areas of IR brightness temperatures below a threshold value encompassing a contiguous area of at least 5000 km 2 (equivalent diameter of 80 km), but in some cases over km 2 (350 km), including meso-b- and small meso-a systems (Orlanski 1975). The minimum area threshold is arbitrary; however, the subset of long-duration convective systems we focus on attained areas of well above this threshold. The IR

5 196 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 FIG. 2. Enhanced MTSAT-1R infrared satellite image at 0000 UTC 8 Sep Blue regions are where cloud-top temperature is lower than 208 K, representing the extent of the cloud clusters. Cyan circles indicate the geometric centers of cloud clusters with the circle area proportional to the cluster area. threshold is 208 K based on previous studies over the western Pacific during the Tropical Ocean and Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE; Mapes and Houze 1993; Chen et al. 1996). There is generally a close correspondence between 208-K cloud area and contiguous mesoscale areas of rainfall observed by radar. The choice of 208 K filters out many nonprecipitating anvil clouds. While the correspondence between cold clouds and precipitation is not one-to-one (Arkin and Ardanuy 1989), most, if not all, of the 208-K cloud clusters represent convective systems that at one point in time had a significant precipitating area. Note that in many previous studies synoptic-scale cloud clusters were tracked, using warmer brightness temperature thresholds and/or subjective manual tracking methods. This study focuses on objectively tracked mesoscale convective systems defined by 208-K cold cloud tops roughly corresponding to rain areas. After the cloud clusters are identified, the center of the cloud cluster is defined as the geometric center of the,208-k area. The size of the cloud cluster is defined as the diameter that the cloud cluster would have if it were a circle with the same area. Figure 2 shows a map of objectively identified cloud clusters (dark blue shading) at 0000 UTC 8 September The largest cloud cluster with an area of km 2 (size of 340 km) near 168N, 1268E is associated with Typhoon Sinlaku. The rainbands surrounding Sinlaku also appear as cloud clusters. Several other relatively large cloud clusters, the largest of which is ; km 2 (size ; 310 km), are not associated with TCs, but with a monsoon disturbance in the South China Sea. Another area with several large cloud clusters is the intertropical convergence zone (ITCZ; roughly N) east of 1458E. b. Time clusters Time clusters are cloud clusters that can be tracked in time with various lifetimes and sizes. Cloud clusters that overlap by at least 50% or km 2 between consecutive hourly satellite images are considered to be part of the same time cluster. Time clusters may consist of cloud clusters that merge and/or split between consecutive frames. Note that the term time cluster refers only to convective systems with continuity in time between at least two consecutive images. 1 1 Time cluster tracking resets at the beginning of each month. This results in some long-lasting time clusters occurring at the end of the month not being continuously tracked into the next month. Because of the limited number of 8-h clusters lasting from one month into the next month, this does not affect significantly affect the results of this study.

6 JANUARY 2013 K E R N S A N D C H E N 197 At any given time, a time cluster may consist of more than one cloud clusters that merged at a future time or split at a previous time, and therefore belong to the same time cluster. The center and area of the time cluster at a given time are the geographical center and the combined area of all the constituent cloud clusters, respectively. The maximum area (size) associated with the lifetime of the time cluster is the maximum area (size) among the time cluster snapshots. As in TOGA COARE, time clusters with longer duration generally attained larger sizes (Fig. 4a and Chen and Houze 1997a,b). The results presented in rest of this study will use time clusters exclusively to represent convective cloud systems. The terms cluster, and time cluster are used interchangeably from here on. Similar to the near-equatorial region during TOGA COARE (Chen et al. 1996), most time clusters move westward for a few hours and remain relatively small (Fig. 3) Only a small minority of time clusters are associated with TCs. The development and life cycles of Supertyphoons Sinlaku, Hagupit, and Jangmi and Tropical Storms Mekkhala and Higos in September 2008 are clearly associated with long-lived time clusters (time clusters lasting at least 24 consecutive images are highlighted). For the case of Sinlaku, the highlighted cluster lasted 159 h and could be tracked starting 9 h prior to best-track genesis. Hagupit and Higos also have westward-moving groups of time clusters that are easily identified as precursors to the storm formation (e.g., developing clusters). In addition to the developing systems, there are long-lived time clusters that move westward, often in distinct envelopes. c. Initial tropical cyclone time clusters As a first step to determine the properties of time clusters that develop into TCs, the initial TC time cluster for each TC that formed within the study area in July October (e.g., Fig. 1) is identified objectively as follows. It is the longest-lasting time cluster among either 1) the largest time cluster present at the time of initial TD appearance in best track or 2) the first time cluster that formed within 24 h after the initial TD appeared in the JTWC best-track data. For a few weak, disorganized storms (e.g., unnamed TDs and minimal tropical storms with intermittent convection), a dominant time cluster could not be tracked until up to 24 h after best-track genesis. For all TDs and TCs considered in this study from July October , the initial TC time clusters lasted at least 8 h, while the time clusters at later stages of the TC had a range of sizes and durations similar to the set of all time clusters (Fig. 4). A time cluster that persists for at least 8 h (e.g., nine consecutive images) is referred to as an 8-h cluster. There are time clusters tracked in the study region during July October A total of 4379 of them (e.g., 9%) lasted at least 8 h. Physically, 8-h clusters are convective systems with long durations, which are likely to have large stratiform rain areas at some time in their lifetime and are likely to persist beyond the time of dominant diurnal surface forcing (e.g., Chen and Houze 1997a). 4. Tracking vorticity maxima To help identify the 8-h clusters leading up to a TC genesis (i.e., the precursors to TC genesis), an objective vorticity maximum tracking algorithm is used to track the system in time prior to best-track genesis. A Gaussian smoothing function was applied to the NCEP FNL relative vorticity at 850 hpa. The weighting function is a Gaussian bell curve with standard deviation of three grid points (38, ;330 km) extending out nine grid points (;990 km) in each direction. This smoothing filters out small-scale features that are not fully resolved by the analysis. Kerns et al. (2008) showed that coherent vorticity maxima could be tracked back several days prior to initial formation of some TCs. Vorticity maxima are first identified and then tracked in time by matching them with the closest one within 38 radius in the next time frame using the 6-hourly data. Tracks end when a vorticity maximum can no longer be found within 38 at the next analysis time. The minimum threshold value of the vorticity tracking is s 21. It is a low threshold chosen to be as inclusive as possible. Vorticity maxima can be seen moving generally westward, often together with coherent envelopes of cloud clusters (Fig. 5). Some are evidently precursors leading to TC formations and others are not associated with TC genesis. For example, Typhoons Hagupit and Higos were associated with well-defined vorticity features and pre-tc vorticity centers that could be tracked from east of 1608E (outside of the cluster tracking domain). The vorticity maxima associated with each TC are found by searching within 38 of the best-track data. Most vorticity maxima that are associated with besttrack TDs are within 18 distance of the best track (not shown). The vorticity maxima tracks extend up to several days prior to TC formation and can cross a significant part of the basin during that time (Fig. 6a). 5. Classification of developing and nondeveloping systems Because the initial developments of all TCs during July October are associated with an 8-h cluster, the occurrence of an 8-h cluster is considered to indicate

7 198 MONTHLY WEATHER REVIEW VOLUME 141 FIG. 3. Time longitude diagram of cloud clusters and time clusters within the domain shown in Fig. 2, for September Black circles represent cloud cluster locations. The size of the circles is proportional to the size of the cloud clusters. Time clusters are connected with red lines. Time clusters lasting at least 24 h are highlighted with yellow circles and dark blue lines. TC best tracks (green lines) are labeled by storm names (unnamed tropical systems are labeled by storm number). The initial appearance of each storm as a TD or TS in the JTWC best track is indicated with a thick, black circle. a minimum duration and level of mesoscale organization that is necessary, but not sufficient, for TC genesis to occur. Therefore, only 8-h clusters over the ocean, which are not already TCs, are included as candidates for further identification of developing and nondeveloping systems. The 8-h minimum duration threshold greatly reduces the number of time clusters to only those that have a potential to develop into a TC and, therefore, are considered a viable candidate to TC genesis. a. Developing systems A developing system is defined as a time cluster or a group of time clusters that last at least 8 h and are associated with a tractable vorticity maximum that provides a way to identify the precursors leading up to TC formation. Developing systems include pre-tc clusters that are backtracked in time using the vorticity maxima tracks. For many storms the initial TC time cluster that

8 JANUARY 2013 K E R N S A N D C H E N 199 FIG. 4. Time cluster duration and maximum size (e.g., equivalent diameter) for (a) all time clusters and (b) time clusters passing within 38 of JTWC best track (e.g., TC time clusters). In (b), dominant clusters associated with the initial formation of the TC are indicated as solid black dots, and other TC clusters at a later stage in the storm evolution are drawn as open circles. The vertical dashed line indicates the 8-h duration threshold, and the arrow indicates time clusters lasting at least 8 h (8-h clusters). existed at the time of initial appearance of a TD in the best track (e.g., initial TC clusters) could be tracked several hours back before genesis as a time cluster and in some cases more than a day (Fig. 6b). Additionally, any 8-h cluster passing within 58 of a pre-tc vorticity track is also considered to be a developing system (Fig. 7). The developing systems have a similar range of lead times as the pre-tc vorticity maxima (Figs. 6a,b). b. Nondeveloping systems A nondeveloping system is defined as an 8-h cluster that is not a TC cluster and is not previously identified as a developing cluster (Fig. 7). As illustrated by Fig. 5, many nondeveloping systems are associated with westward-moving vorticity features and trackable vorticity centers. Among the time clusters tracked over the ocean, 3144 were 8-h clusters (Fig. 7). A total of 398 of the h clusters were associated with active TCs and are excluded as candidates for TC genesis. Among the other h clusters, 435 (15.8%) were developing systems and 2311 (84.2%) were nondeveloping systems. 6. Examples of developing and nondeveloping systems a. Precursors to Typhoon Hagupit (2008) As shown in Figs. 3 and 5, Typhoon Hagupit was a case with a well-defined sequence of precursor developing clusters going back several days prior to TC formation. Further details on the evolution of vorticity and time clusters leading up to the development of Hagupit are shown in Fig. 8. An envelope of latitude-averaged vorticity with an embedded vorticity center can be traced back to east of 1608E at least as far back as 12 September, the 8-h clusters associated with the vorticity maximum began on 14 September, and a 4-day series of 8-h clusters occurred during September, leading up to the initial appearance of the TD in best track at 1800 UTC 18 September. The 8-h clusters occurred preferentially to the south of the center of vorticity, and remained skewed to the south even after the initial TD had formed. This is probably due to the effect of large-scale vertical shear on the system (Black et al. 2002; Corbosiero and Molinari 2002; Lonfat et al. 2004). Also note that the vorticity maximum weakened somewhat on 16 September, but then it intensified after ;1 day of sustained 8-h cluster activity. As shown in the relative vorticity swath map (e.g., the maximum vorticity along the track of the vorticity center), the relative vorticity gradually increased as the system moved westward on September. It is likely that the persistent, organized convection on September played a strong role in modifying the mesoscale environment. That is, the time clusters can be considered distinct precursors to the TC genesis. The convection also likely contributed to the preconditioning of the large-scale vorticity and moisture leading up to the formation of Hagupit. b. Nondeveloping system A sequence of nondeveloping systems during September 2010 was associated with a westward-moving envelope of vorticity maxima, which is shown as an

9 200 MONTHLY WEATHER REVIEW VOLUME 141 FIG. 5. Time longitude diagram of 8-h clusters and NCEP FNL relative vorticity for September The background color shading is 850-hPa relative vorticity averaged from N along each longitude grid. The 8-h cluster tracks are denoted as magenta circles with the circle area proportional to the time cluster area. Vorticity maxima lasting at least 24 h are drawn as triangles. JTWC TC best tracks are drawn as thick, black lines. example in Fig. 9. In this case, a vorticity center could be tracked over 3 days moving from ;1408E to the Philippines, along ;128N. The envelope of enhanced vorticity can be traced back farther to the east, but not the vorticity center. Several 8-h clusters occurred along and to the south of the vorticity center track. Based on the vorticity swath map, the vorticity center attained an amplitude similar to pre-hagupit on 15 September 2008, but

10 JANUARY 2013 K E R N S A N D C H E N 201 FIG. 6. Scatter diagram of lead time vs distance from the initial tropical depression (TD) formation based on JTWC best track: (a) pre-tc vorticity maxima and (b) developing 8-h clusters. In (b), the triangle markers are for the initial TC time clusters, which are trackable directly to the TC. despite of the collocated 8-h clusters and the vorticity maxima over a period of 3 days before moving into the vicinity of the Philippine islands, the system did not intensify further into a TC. This nondeveloping system eventually moved over the Philippines. Note that TC genesis does occur in close proximity to the Philippines (Fig. 1), which means the islands were not necessarily a factor in this case, especially since it failed to develop over a 3-day period prior to approaching the islands. Clearly, the collocation of relative vorticity and 8-h clusters is not sufficient to determine whether TC genesis will occur, and it is necessary to consider other factors. separation in the environments of the developing versus nondeveloping systems can be used to obtain a skillful discrimination between the two subsets of 8-h clusters. Additionally, the information provided by the time sequence of cloud clusters allows the time history of the developing and nondeveloping systems to be quantified. a. Environmental conditions The strongest statistical predictor of TC genesis is low-level relative vorticity (Fig. 10a). Consistent with McBride and Zehr (1981), the median relative vorticity for the developing cases ( s 21 ) is nearly twice 7. Large-scale environment of developing and nondeveloping systems The developing and nondeveloping systems are associated with a wide range of environment conditions, quantified by low-level vorticity, low-level convergence, vertical wind shear, moisture content, and SST. In the subsequent analysis, the large-scale environmental conditions are computed at each hourly snapshot throughout the 8-h clusters lifetimes. In addition to developing and nondeveloping systems, the environment predictors are also calculated for the first 24 h of the TCs for comparison purpose. There is a significant overlap in these parameters between developing and nondeveloping systems (Fig. 10). Nevertheless, there are significant statistical differences (above 95% confidence in the Student s t test) in all the parameters, except SST. This FIG. 7. Schematic of the classification of 8-h clusters into developing and nondeveloping systems. The number of time clusters represented is in parentheses for each category.

11 202 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 FIG. 8. Evolution of vorticity maxima and cloud clusters associated with the genesis of Typhoon Hagupit (2008). The best track is shown as the thick black line. The vorticity maxima associated with the particular storm is drawn as triangles. The 8-h cloud clusters associated with the particular storm (e.g., TC clusters and developing clusters) are drawn as magenta circles, proportional to the cloud cluster size. (left) Time longitude Hovmöller plot. The background color shading is relative vorticity at 850 hpa averaged from N at each longitude. (right) Map of the tracks of the features shown in (left). The color shading is the maximum relative vorticity within 108 of the vorticity maximum center. that of the nondevelopers 2 ( s 21 ). This is much greater discrimination than Fu et al. (2012) who found only a 20% difference using filtered vorticity fields. For reference, the relative vorticity distribution of the first 24 h of the TCs is also shown. As expected, the TCs have somewhat higher relative vorticity than the developing systems. This suggests that most of the large-scale relative vorticity was generated prior to TC genesis. Note that the system vorticity and the large-scale environment vorticity cannot be entirely distinguished. In addition to low-level vorticity, the median low-level convergence is ;30% greater for the developing systems versus nondeveloping systems (Fig. 10b). As with relative vorticity, the difference between developing systems and the first 24 h of TCs is less significant. 2 A sensitivity test was performed including only the nondeveloping systems tracking within 58 of a vorticity center lasting over 24 h. The median value of relative vorticity obtained was ; s 21 higher. Vertical wind shear is significantly lower on average for the developing systems (Fig. 10c). The median vertical wind shear of the developing systems is ;9 ms 21 (17 kt). This median value of shear is similar to the threshold below which rapid intensification of TCs generally occurs (Kaplan and DeMaria 2003). Half of the developing systems occurred in an environment with shear.9 ms 21. Also, about 30% of the nondeveloping cases were associated with shear values,9 ms 21. Satellite-derived TPW was somewhat higher in developing cases compared with nondeveloping cases, but it is not a strong discriminating factor (Fig. 10d). Hennon and Hobgood (2003) also found TPW to be a poor predictor in the WPAC. This is probably due to a generally moist environment compared with the Atlantic. A similar calculation was done using only the subset of cases with individual TMI swaths in close proximity in space and time, and similar results were obtained (not shown). Satellite-derived SST did not suggest a significant difference between developing and nondeveloping systems

12 JANUARY 2013 K E R N S A N D C H E N 203 FIG. 9. As in Fig. 8, but for the nondeveloping cases associated with a moderately strong vorticity maximum from Sep (Fig. 10e). This is probably because of the generally warm SSTs in the WPAC during July October. b. Genesis productivity The preferred areas of TC genesis (Fig. 1) generally correspond with the preferred areas of 8-h clusters (Fig. 11a). Nevertheless, the maximum of 8-h cluster occurrence is in the South China Sea just west of the Philippine islands, whereas the TC genesis is most frequent east of the Philippines near 1308E. The South China Sea has a relatively larger number of nondeveloping clusters than the warm pool region east of the Philippines. The relative frequency of developing and nondeveloping clusters is summarized using a genesis productivity similar to Kerns et al. (2008) and Hennon et al. (2013). The genesis productivity of 8-h clusters was calculated as the number of developing time clusters divided by the total number of candidate time clusters (developing and nondeveloping clusters, excluding TCs). The genesis productivity is greatest over the west Pacific warm pool near N, 1308E, which is collocated with the relatively high occurrence of 8-h clusters (Fig. 12). In contrast, the South China Sea has relatively low genesis productivity. The peak in genesis productivity east of the Philippines is farther north than the maximum track density of both vorticity maxima and 8-h clusters (Figs. 11a,b). Similar to the Atlantic, the maximum genesis productivity is located to the north of the maximum track density of disturbance centers (Kerns et al. 2008). Note that the latitude of maximum genesis productivity corresponds to the maximum latitude and highest planetary vorticity within the geographical region where 8-h cluster occurrences are relatively frequent. Motivated by the maximum of genesis productivity at ;178N, the following is considered to be an additional predictor of TC genesis in the WPAC: ALAT17 5 jlatitude 2 17j, where the vertical bars indicate the absolute value. c. Linear discriminant analysis The developing systems statistically form in more favorable environmental conditions (e.g., lower shear, higher humidity, stronger low-level vorticity, and convergence) than the nondevelopers. Linear discriminant analysis (LDA) was performed using the five environmental parameters shown in Figs. 10a e as well as ALAT17 (section 7b). LDA can determine the overall

13 204 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 FIG. 10. Cumulative distributions of the large-scale environment conditions associated with 8-h cluster snapshots: (a) 850-hPa relative vorticity, (b) 925-hPa divergence, (c) hpa vertical wind shear, (d) TMI 3-day mean TPW, and (e) TMI/AMSRE SST. Nondeveloping clusters are indicated by the dashed curves. Developing clusters are indicated by the solid curves. The first 24 h of TCs are indicated by the light, dotted curves. The dynamic fields from the FNL analysis are based on averages in (a) (c), TPW is based on averages in (d), and SST is based on 18 averages in (e). discrimination between developing and nondeveloping systems using an optimal linear combination of multiple predictors (Perrone and Lowe 1986; Hennon and Hobgood 2003; Kerns and Zipser 2009). The probability of development from LDA (or posterior probability) is considered to be an indication of how favorable the largescale environment is for TC genesis. A climatological probability of development is defined as the fraction of the developing systems in all 8-h cluster instances, which is ;20%. When LDA probability is significantly greater

14 JANUARY 2013 K E R N S A N D C H E N 205 FIG. 11. The number of tracks passing through each during July October : (a) time clusters lasting at least 8 h (8-h clusters) and (b) vorticity maxima lasting at least 24 h. (less) than the climatological background probability of development, the environment can be considered to be favorable (unfavorable) for TC genesis. An environment with LDA probability to develop close the climatology is considered to be marginally favorable for TC genesis. To determine how effective the LDA is for distinguishing between developing and nondeveloping systems, the leave-one-out ( jackknife ) cross validation is used. A single sample is left out, and the LDA is recalculated using the remaining samples. This process is repeated for each hourly instance of the 8-h cluster, to obtain the probability of that case developing. The probability of detection is the fraction of developing systems that were correctly predicted to be developing systems. The false-alarm rate is the fraction of nondeveloping systems that were erroneously predicted to be developing systems. The probability of detection and false-alarm rate depend on the threshold posterior probability used to decide when to predict development, which is also referred to as the decision threshold. The Heidke skill score (HSS) is used to determine the predictive skill relative to making random guesses (Marzban 1998). HSS ranges from below 0 to 1. HSS. 0.3 is considered to indicate a benchmark for a useful level of skill. Skill scores above the benchmark are seen for posterior probabilities between 0.15 and 0.50 (Fig. 13). Using the climatological likelihood (20% LDA probability to develop), a 70% detection rate with a 28% falsealarm rate is obtained (Fig. 13). At the threshold with FIG. 12. The TC genesis productivity of 8-h clusters. Genesis productivity is defined as the percentage of candidate (e.g., developing plus nondeveloping) 8-h clusters passing through each 58 box that are identified as developing systems. FIG. 13. The probability of detection (POD, dashed), false-alarm rate (FAR, dash dot), and Heidke skill score (HSS, solid) as a function of the threshold LDA probability of development. The climatological likelihood of development, which is the fraction of candidate time cluster snapshots that are developing systems, is indicated by the vertical gray line (;0.2). The POD, FAR, and HSS are determined using leave-one-out ( jackknife ) cross validation. See text for details.

15 206 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 FIG. 14. (a) The probability density function (PDF) of posterior probabilities from linear discriminant analysis. (b) The number of instances (e.g., hourly snapshots) of nondeveloping and developing systems in each bin (0.05) of LDA posterior probability. The gray vertical line represents the climatological likelihood of an 8-h cluster being a developing cluster (;0.2). peak skill as indicated by the HSS (;30% LDA probability to develop), there is roughly a 50% probability of detection and a 12% false-alarm rate. Cloud cluster area, growth rate, and aspect ratio were considered as potential predictors, but they did not improve the skill, probably because individual convective systems each go through their individual life cycles with significant changes to their morphology over periods of a few hours. The HSS obtained in this study compares favorably with previous studies using various tracking methods and predictors. Hennon and Hobgood (2003) obtained an HSS of ;0.4 for the 12- and 24-h lead-time periods. Note that the lead time in this study is significantly longer (median of 39 h). Hennon et al. (2005) expanded the range of posterior probability above the benchmark of HSS using a neural network classifier, but did not significantly increase the peak skill score. The range of posterior probability with HSS. 0.3 in this study is somewhat greater than their LDA classification, but less than their neural network classifier. The skill in this study is comparable to Kerns and Zipser (2009) for the Atlantic, but significantly lower than their results in the eastern North Pacific. Peng et al. (2012) and Fu et al. (2012) did not present skill scores, but their filtered relative vorticity appears to provide somewhat less discrimination than the spatial averaged vorticity used in this study (their Fig. 9). Because relative vorticity is the strongest predictor, it is likely that the skill of their studies would be somewhat lower than the current study. Note that these skill score comparisons are limited because of the unique mesoscale classification method used here compared with the synoptic-scale classification used in previous studies. Nevertheless, the skill scores obtained are not fundamentally different than previous studies using synoptic classification. Despite the statistically significant differences between the developing and nondeveloping systems, there are many nondeveloping systems occur in environments that are apparently favorable for TC formation (e.g., LDA probability to develop.20%); 20% of the nondeveloping systems have at least the same chance to develop as the median of the developing systems. Recall that the sample size of nondeveloping systems is 2311, while there are only 436 developing (Fig. 7). For cases with the LDA probability to develop greater than the climatological likelihood of development, the fraction of developing systems is larger than the fraction of nondeveloping systems (Fig. 14a); however, the number of nondeveloping cases with a probability to develop larger than the climatological background is considerably larger than the number of developing systems with the same probability, up to posterior probability of 0.4 (Fig. 14b). 8. Evolution of developing and nondeveloping systems To better understand and further distinguish the characteristics of the developing and nondeveloping systems, evolutions of both groups are examined in context of their large-scale environmental conditions in terms of the LDA probability to develop. The developing systems are backtracked in time so that their evolution leading up to TC genesis can be examined in an evolving large-scale environment (Fig. 15a). A small group of the developing systems can be tracked back as long as 8 days prior to TC

16 JANUARY 2013 K E R N S A N D C H E N 207 FIG. 15. The time history of LDA probability to develop for (a) developing systems and (b) nondeveloping systems. Each of the light gray lines represents the entire time history of an individual 8-h cluster. In (a), time is with respect to the time of TD/TS formation in the JTWC best track (gray lines). The initiation points of the time clusters are indicated with black dots. In (b), time is with respect to the initial formation of the 8-h cluster, and the dissipation points are drawn as triangles. Contours are drawn for the number of snapshots (e.g., hourly points along the gray lines) in each bin: (a) 24-h and (b) 6-h bins. Both panels use 0.1 LDA probability bins. Contours are for 10, 20, 50, 100, 200, 300, 400, 500, 1000, and 2000 snapshots for both (a) and (b). The climatological probability to develop (0.2) is indicated by the black horizontal line in both (a) and (b). genesis. Not surprisingly, the environment is not particularly conducive to TC genesis at that early stage. As the genesis time approaches, cloud clusters are statistically more likely to occur in increasingly favorable environments. On the other hand, many other developing systems occurred in moderate to less favorable environments even with short lead times,48 h. In fact, the peak of the distribution at,48-h pregenesis (e.g., contours in Fig. 15a) is in a marginally favorable environment near the climatological likelihood of TC genesis (;20%). The evolution of nondeveloping systems is examined from the formation of a 8-h cluster to its dissipation (Fig. 15b). In contrast to the developing systems, the nondeveloping systems statistically occurred in less favorable environments as time progressed. The majority of false alarms occurred with clusters lasting,36 h, which represents the vast majority of nondeveloping systems. A minority of very long-lasting (.24 h) clusters remained in a favorable environment for.24 h prior to their dissipation. However, 806 (274) of the 2311 nondeveloping systems had LDA probability to develop above 0.2 (0.3) within the first 24 h after cluster initiation. The distinct evolution of the relatively lasting developing and nondeveloping systems may indicate that their large-scale environmental conditions are better resolved by the global FNL analysis fields, whereas the majority of developing and nondeveloping systems formed in the environments that are less distinguishable and/or or unresolved by the global analysis. The latter presents a major challenge for TC genesis forecasting using the large-scale environmental conditions, which can lead to many false alarms. The interaction of long-lasting cloud clusters with their environment may modulate the conditions leading up to TC genesis, which is unresolved in the current observational data and global analysis fields. This could

17 208 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 be due to a single dominant cloud cluster lasting extremely long (.24 h) or the repeated occurrence of shorter duration clusters. In developing systems, the persistent convection can feed back into the large-scale environment, making it increasingly more favorable. This interaction is more likely to occur in an environment with higher relative vorticity, which is more likely to be inertially stable with a smaller Rossby radius of deformation in those cases (Schubert and Hack1982). In nondeveloping systems this positive interaction does not take place, and instead the environment of the clusters instead becomes more hostile with time. Determining the mechanism that allows feedback to the environment is an ongoing research topic. It is hypothesized that the mesoscale environment in which convection occurs is a critical factor in determining to what extent the convection can feed back to the environment (e.g., Chen and Frank 1993). Another possible interpretation of the increasingly favorable large-scale environment of developing systems with time is that the large-scale environment is partly a symptom of the outer-core component of TC genesis. This process may occur concurrently with innercore formation and may be inseparable from it. 9. Conclusions Tropical convective cloud systems (cloud clusters) are a key ingredient for TC genesis. The characteristics of the cloud clusters and their environmental conditions are examined in terms of developing and nondeveloping systems. An objective cloud cluster tracking method is used to track and classify developing and nondeveloping systems. The key findings are summarized as follows: d d d d A total of time clusters were tracked from July October over the western North Pacific Ocean. Only h clusters were considered as viable candidates for TC genesis, among which 435 (15.8%) were developing systems and 2311 (84.2%) were nondeveloping systems (Fig. 7). The developing systems generally have greater lowlevel vorticity, greater low-level convergence, lower vertical wind shear, and higher water vapor content than the nondeveloping systems (Fig. 10). There is no significant difference in SST. Linear discriminant analysis (LDA) can skillfully distinguish between developing and nondeveloping systems. The Heidke skill score obtained is ;0.4 (Fig. 13), among the highest skill scores presented in the literature. A probability of detection of 70% and falsealarm rate of 27% is obtained. Generally, the environment became more favorable with time for developing systems with long (1 7 days) d lead time to TC genesis (Fig. 15a), whereas nondeveloping systems tend to evolve into unfavorable environments with time (Fig. 15b). Many developing (nondeveloping) systems formed (dissipated) in seemingly unfavorable (favorable) environments within a lead time (duration) of,24 h, which presents a major challenge for TC genesis forecasting. Incorporating the 8-h cluster duration can improve statistical prediction of TC genesis by eliminating many disturbances with weaker, short-lived convective systems and providing a more stringent criterion to determine systems that have the potential to develop. In an operational environment, the cloud cluster tracking method and LDA classification could be used to provide an objective, statistical guidance on the potential for operational invest systems to develop into TCs. The lack of a more robust separation of the environmental conditions for developing and nondeveloping systems may be in part due to the relative low-resolution of FNL analysis fields. It is possible that the skill could be improved by using higher-quality analyses. Although observations from targeted field programs can provide insights about TC genesis on mesoscale and subsynoptic scales, the lack of continuous temporal and spatial coverage makes it difficult to address the convective environmental interactions. A combination of in situ observations and high-resolution, cloud-resolving modeling over a large region allowing both convective systems and their environment to interact freely will be a key to better understand and predict the interaction of the convection (e.g., long-lasting cloud clusters) with the environment during TC genesis. Acknowledgments. The authors wish to thank Mr. Ed Ryan for his assistance on processing the satellite data and cloud cluster tracking analysis. 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