An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery

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1 University of North Carolina Asheville Journal of Undergraduate Research Asheville, North Carolina, 2010 An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery Chip Helms Atmospheric Sciences The University of North Carolina at Asheville One University Heights Asheville, North Carolina USA Faculty Advisor: Dr. Christopher Hennon Abstract Tropical cyclones trace their origins to disorganized groups of thunderstorms over tropical waters called "cloud clusters." The transformation from cloud clusters into mature tropical cyclones is not well understood due to the remote location of these clusters (away from the main observation network) and the complexities of the smallerscale physics that occur during genesis. Several studies have used a catalog of cloud clusters to search for distinguishing features between those which develop into storms and those which do not. The creation of these datasets is cumbersome since they must be identified visually through the application of arbitrary and subjective rule. An automated algorithm is developed to identify tropical cloud clusters in geostationary infrared satellite imagery. The algorithm proposed here significantly reduces the time and effort needed to identify and catalog cloud clusters by applying objective search criteria in an automated way. This algorithm will enable the creation of a comprehensive, historical cloud cluster database that will have both research and operational applications. The database will be made available to the scientific community and will be updated in real-time. Keywords: Tropical, Clusters, Infrared Imagery 1. Introduction The influx of satellite data has been particularly beneficial to studies of tropical cyclogenesis (TCG) due to the remoteness of the region and lack of regular in-situ observations. Currently, many of the cloud cluster datasets used in these studies are created by manual examination of satellite images and locating clusters based on either subjective criteria or a blend of subjective and objective criteria. Not only is this process time consuming, it also creates problems when making comparisons between studies with different criteria definitions. In fact, several researchers have mentioned the need for the development of an algorithm to create an objective database of cloud clusters 4,14. A number of obstacles must be overcome to create such an algorithm: (i) defining a set of criteria satisfying the needs of the tropical research community, (ii) translating these criteria into computer code, and (iii) providing useful output which will aid research efforts. The scientific literature contains several possible definitions of cloud clusters. Lee 9 used three criteria to identify non-developing cloud clusters: (i) the cluster is an independent entity unassociated with a cyclone or precyclone system; (ii) the cluster is at least four degrees in diameter and is not elongated in shape; and (iii) the cluster is located between 5 degrees and 17.5 degrees north, 120 degrees and 165 degrees east. In studying cloud clusters with satellite imagery, Martin and Suomi 12 chose clusters based on a minimum area of one square degree, excluding those obviously composed of only low level stratiform clouds. Hennon and Hobgood 4 defined cloud clusters as in Lee 9 with two key changes; the study domain was shifted to the Atlantic basin, south of 40 degrees north, and the associated convection had to persist for a minimum of 24 hours. The definition presented by Perrone and Lowe 15 appears to constitute a lower bound for diameter in the literature, calling for a minimum diameter of one degree latitude. Martin and Sikdar 11 found that cloud clusters progress through two stages. The first stage was composed mainly of small individual cells while the second stage (and of interest to the current study) is populated by fewer, 180

2 larger cells. Several studies define a threshold brightness temperature in infrared satellite imagery for objectively identifying cloud clusters. Sherwood and Wahrlich 17 use threshold values of 235 K for relevant convection, 208 K for active deep convection, and 267 K for associated cloud cover. A value of 208 K is also used by Hall and Vonder Haar 3 to identify cold, deep convection over the west Pacific. Mapes and Houze 10 choose 208 K to locate very cold convection and 235 K to locate moderately cold convection. The current study uses a threshold temperature of 220 K. The inclusion of commonly used parameters in an objective cloud cluster database could prove to be a valuable tool in testing TCG model accuracy. By basing the algorithm on satellite imagery, the existence of preexisting convection, which Riehl 16 found to be a requirement for TCG, is guaranteed. McBride and Zehr 13 found a number of possible prediction parameters based on the 300 mb temperature field, zonal wind and relative vorticity fields at both 200 mb and 900 mb, and mean vertical motion in the area of the cluster. Additionally, McBride and Zehr 13 defined the daily genesis potential (DGP) using the difference in relative vorticity fields at 900 mb and 200 mb and found its value to be correlated to the likelihood of development. Hennon and Hobgood 4 and Hennon et al. 5 used latitude, DGP, maximum potential intensity 6, moisture divergence at 925 mb and 850 mb, precipitable water, 24- hour pressure tendency, and six-hour surface and 700 mb relative vorticity tendencies as predictors in a statistical model. Gray 2 cited parameters such as planetary vorticity, sea surface temperatures, conditional instability, and midtropospheric moisture as influencing TCG. This paper outlines an algorithm for objectively identifying and tracking tropical cloud clusters in the tropical Atlantic basin using geostationary infrared imagery. The study focuses on the Atlantic basin since there is good data coverage, both spatially and temporally, and most systems impacting the U.S. originate here. The following section details the dataset used. The cloud cluster selection process and the algorithm's output parameters are discussed in section 3. Section 4 features an overview of the results from a single season followed by concluding thoughts and future plans for research in section Data and Cluster Requirements This study uses geostationary infrared imagery taken from the Hurricane Satellite (HURSAT) Basin dataset, a subset of the HURSAT database 7. Knapp 8 describes this subset as a full basin, stationary grid in contrast to the stormcentric grid used in other HURSAT subsets. Although recent geostationary satellite data have a 4 km resolution and are available every half hour, the data in this study are gridded at an 8 km spatial resolution and are available at three hour time intervals so that they are comparable throughout the record. The requirements for a cloud cluster used in this study are a blend of those found in the scientific literature. In order to be classified as a tropical cloud cluster, an area of convection, herein referred to as the candidate cloud cluster, must: (i) have sufficiently deep convection, (ii) have an area of deep convection with a minimum diameter of two degrees, (iii) be located over water, (iv) be independent of other systems, and (v) persist for at least 24 hours. The algorithm accomplishes this by defining these requirements as follows: deep convection corresponds to cloud top brightness temperatures below 220 K (herein referred to as the threshold brightness temperature), deep convection must cover 80% of a one degree radius circle centered on a candidate cloud cluster, all candidate positions located over land are ignored, each candidate must be at least 1200 km away from any other candidate, and the initial and final times at which a candidate cluster is identified must be at least 24 hours apart. 3. Algorithm Processes and Output The algorithm locates cloud clusters using a three step process. The first step examines individual satellite images to locate sufficiently large areas of deep convection which could potentially be cloud clusters. To accomplish this, the algorithm removes all pixels with a brightness temperature greater than the threshold brightness temperature, isolating all of the cold cloud tops. The second cloud cluster requirement is applied by calculating, for each remaining pixel, the number of other cold pixels within a one degree radius circle of that pixel. Any pixels having less than 80% coverage of this circle are removed. This removes any cold pixel areas which are significantly smaller than two degrees across. Finally, this step records the location of each of these cold pixel areas and calculates a number of statistics, detailed later in this section. The location is calculated by finding the geometric center of the cold pixel area. A weighted latitude and longitude are also calculated by giving colder pixels more 181

3 weight than those around them, moving the center closer to the location of strongest convection. The second step uses the list of candidate cluster locations found in the previous step to track individual Figure 1. Sample output file from the 2004 Atlantic hurricane season. Figure 1. Lines one through seven describe the parameters used to run the algorithm and notes on the data contained in the file. From left to right, the variables stored in each column are as follows: track number, time stamp, latitude, longitude, weighted latitude, weighted longitude, interpolation flag, size of cluster in pixels, average brightness temperature (Tb), minimum Tb, standard deviation of Tb, 5 th percentile of Tb, 10 th percentile of Tb, maximum radius of cluster, minimum radius, average radius, weighted maximum radius, weighted minimum radius, and weighted average radius. Note that the lower half of the figure is a continuation of the upper half and is separated here due to space constraints. candidate clusters through time. The algorithm goes through the lists of locations and searches for the best match to known independent candidate locations. The independence requirement is applied by ignoring any clusters which are within a 1200 km threshold distance of a larger cluster. In order to find the best match, the algorithm searches outward from the center of each candidate until a nearby location is found. Only locations which are positioned over water are considered for the match. Since convection tends to have diurnal variation, the list of past candidate locations includes candidates whose final time stamp is within 12 hours of the current time being processed. The maximum distance between the candidate and its potential match is dependent on the difference in their times. This ensures that more recent locations are favored over older ones. For simplicity, the algorithm does not allow clusters to split or merge. In the event that a cluster does split or merge, the algorithm will track the larger cluster until the clusters can be considered independent. To provide computer-friendly output, the algorithm interpolates missing data points and smoothes the final cloud cluster tracks. The interpolation scheme is a simple linear interpolation and is applied to both the weighted and unweighted latitude and longitude of the cloud clusters. Interpolated points are denoted by an 'I' as seen in Figure 1. Since a wide range of situations can cause a point to require interpolation, applying an interpolation scheme to the cluster statistics is impractical. Instead, the algorithm marks the statistics as missing. The downside of tracking cloud clusters based on the center of convection is the noise in the track resulting from shifting convection. To remove this noise, a modified three-point running average is applied to each track. Unlike a regular three-point running average, the initial and final points in the track are held constant. 182

June July August September October Figure 2. Tropical cloud cluster tracks from the 2004 Atlantic hurricane season. November Figure 2.

Tracks appearing in the eastern Pacific will be removed during quality control of the database and are not included in any statistics reported in this paper.

The first seven lines of every file give information about the parameters used to run the algorithm, such as the threshold brightness temperature, as well as the column titles for the data.

4 June July August September October Figure 2. Tropical cloud cluster tracks from the 2004 Atlantic hurricane season. November Figure 2. The cloud cluster tracks are sperated based on the initial time stamp listed in the output file. Tracks appearing in the eastern Pacific will be removed during quality control of the database and are not included in any statistics reported in this paper. The output for cloud clusters is recorded in individual text files. Figure 1 contains a sample output file from the 2004 Atlantic hurricane season. The first seven lines of every file give information about the parameters used to run the algorithm, such as the threshold brightness temperature, as well as the column titles for the data. The remainder of the file contains the cloud cluster track information and statistics at three hour intervals during the cluster s lifespan. Track numbers are assigned by the algorithm starting with one at the beginning of each season. The time stamp is written as year, month, day, and UTC hour. The size variable is the total number of cold pixels contained in the cloud cluster. Maximum and minimum radii are calculated as the distances, in kilometers, between the center of the cloud cluster and the farthest and nearest edges, respectively, while the average radius is the average distance from the center to the edge. The weighted radii are calculated using the weighted latitude as the center point. 183

5 4. Results of 2004 Atlantic Hurricane Season The 2004 Atlantic hurricane season was run to demonstrate the algorithm. The 2004 season was almost two and a half times more active than the long term average for the Atlantic 1. The monthly distribution of cluster tracks identified by the algorithm is shown in Figure 2. The tracks tend to be concentrated in areas that are climatologically favorable for development. In June the primary development region is located in the Gulf of Mexico and extreme western Caribbean. This region shifts to include the east coast of the United States in July and expands east of the Caribbean in August. By the peak of the season in September, the main development region reaches from the west coast of Africa across the southern end of the North Atlantic basin and up the western side of the Atlantic Ocean. During October the development region retreats westward as the waters between Africa and South America become less favorable for development. At the end of the season, most development occurs off the east coast of the United States 2. Since Figure 2 only includes a single season, the primary development regions do not stand out as well as they would in maps of tracks from multiple seasons. There were a total number of 67 cloud clusters identified by the algorithm during the 2004 hurricane season. During the same period, there were sixteen systems which reached tropical depression strength, fifteen of which became tropical storms. Nine of these became hurricanes and six hurricanes reached category three strength or greater. A comparison between the number of identified cloud clusters and tropical cyclones during each month is given in Figure 3. It is interesting to note that, even though the peak number of tropical cyclones occurred in August, the peak number of identified cloud clusters corresponds to the climatological peak of the hurricane season in September. This would suggest that August was more favorable for tropical cyclone development than September. With the exception of June and November, the monthly distribution of identified cloud clusters closely resembles the historical distribution of tropical activity. It is unknown whether this is just a coincidence or if this pattern appears in other years where peak activity is different than the climatology. Figure 3. Comparison between cloud cluster and tropical cyclone frequency for the 2004 Atlantic hurricane season. Figure 3. Clusters in the eastern Pacific, see figure 2, are not included in these monthly totals. The tropical cyclone frequency includes all systems which reach at least tropical depression strength. Tropical cyclone data courtesy of The National Hurricane Center

6 5. Conclusions and future work An algorithm is developed to objectively identify and track tropical cloud clusters in the North Atlantic basin. The cloud clusters are identified based on the brightness temperature field from HURSAT basin-wide IR imagery. From this algorithm, a database containing clusters and cluster-centered development parameters from the past 30 years is being built. Since the HURSAT dataset is calibrated to be consistent over the entirety of this time span, any trends appearing in the database reflect trends in the atmosphere and not changes to the observing platform or instruments. Given that the end goal of this algorithm and the resulting database is to aid the study of TCG, it is important that the results are in general agreement with a variety of subjectively and manually identified datasets. In comparison to the cluster dataset created by Hennon and Hobgood 4, which spans the 1998 through 2000 North Atlantic hurricane seasons, both the number of clusters and their general tracks are similar to those identified by the algorithm. More generally, the initial positions are concentrated in the monthly main genesis regions described by Gray 2 and the number of identified clusters is proportional to the number of tropical cyclones developing in the same year as well as developing in the same month. Future research goals for this algorithm include expanding the algorithm to include the other global development regions as well as possible applications to operational forecasting of TCG events. One potential for operational application is in modifying the code to locate current cloud clusters in support of a TCG model. When the database becomes publicly available, the scientific community is encouraged to provide feedback regarding the functionality of the database. 6. Acknowledgements This work was supported by a grant from the Undergraduate Research Program Advisory Council at UNC Asheville. The author would like to thank his advisor, Dr. Chris Hennon, for the work he has done on this project. Additionally, the author would like to thank the handful of peer reviewers who gave their highly beneficial criticism. The author would also like to thank Kenneth Knapp for his help on streamlining the code and data support. 7. References 1. Franklin, James L., and Coauthors, 2006: Atlantic hurricane season of Mon. Wea. Rev., 134, Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, Hall, T. J., and T. H. Vonder Haar, 1999: The diurnal cycle of west Pacific deep convection and its relation to the spatial and temporal variation of tropical MCSs. J. Atmos. Sci., 56, Hennon, C. C., and J. S. Hobgood, 2003: Forecasting tropical cyclogenesis over the Atlantic basin using large-scale data. Mon. Wea. Rev., 131, Hennon, C. C., C. Marzban, and J. S. Hobgood, 2005: Improving tropical cyclogenesis statistical model forecasts through the application of a neural network classifier. Wea. Forecasting, 20, Holland, G. J. 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, Knapp, K. R., and J. P. Kossin, 2007: New global tropical cyclone data from ISCCP B1 geostationary satellite observations. J. of Appl. Remote Sens., 1, , doi: / Knapp, K. R., 2008: Hurricane satellite (HURSAT) data sets: Low-earth orbit infrared and microwave data. Preprints, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., 4B Lee, C. S., 1989: Observational analysis of tropical cyclogenesis in the western North Pacific. Part I: Structural evolution of cloud clusters. J. Atmos. Sci., 46, Mapes, B. E., and R. A. Houze, 1993: Cloud clusters and superclusters over the oceanic warm pool. Mon. Wea. Rev., 121, Martin, D. W., and D. N. Sikdar, 1975: A case study of Atlantic cloud clusters: Part 1. Morphology and thermodynamic structure. Mon. Wea. Rev., 103, Martin, D. W., and V. E. Suomi, 1972: A satellite study of cloud clusters over the tropical North Atlantic 185

7 Ocean. Bull. Amer. Meteor. Soc., 53, McBride, J. L., and R. Zehr, 1981: Observational analysis of tropical cyclone formation. Part II: Comparison of non-developing versus developing systems. J. Atmos. Sci., 38, McTaggart-Cowan, R., G. D. Deane, L. F. Bosart, C. A. Davis, and T. J. Galarneau Jr., 2008: Climatology of tropical cyclogenesis in the North Atlantic ( ). Mon. Wea. Rev., 136, Perrone, T. J., and P. R. Lowe, 1986: A statistically derived prediction procedure for tropical storm formation. Mon. Wea. Rev., 114, Riehl, R. J., 1948: On the formation of typhoons. J. Meteor., 5, Sherwood, S. C., and R. Wahrlich, 1999: Observed evolution of tropical deep convective events and their environment. Mon. Wea. Rev., 127,

An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery. Why?

An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery By Chip Helms Faculty Advisor: Dr. Chris Hennon Why? Create a database for the tropical