Clustering Indian Ocean Tropical Cyclone Tracks by the Standard Deviational Ellipse

Abstract

Abstract:The standard deviational ellipse is useful to analyze the shape and the length of a tropical cyclone (TC) track. Cyclone intensity at each six-hour position is used as the weight at that location. Only named cyclones in the Indian Ocean since 1981 are considered for this study. The K-means clustering algorithm is used to cluster Indian Ocean cyclones based on the five parameters: x-y coordinates of the mean center, variances along zonal and meridional directions, and covariance between zonal and meridional locations of the cyclone track. Four clusters are identified across the Indian Ocean; among them, only one cluster is in the North Indian Ocean (NIO) and the rest of them are in the South Indian Ocean (SIO). Other characteristics associated with each cluster, such as wind speed, lifespan, track length, track orientation, seasonality, landfall, category during landfall, total accumulated cyclone energy (ACE), and cyclone trend, are analyzed and discussed. Cyclone frequency and energy of Cluster 4 (in the NIO) have been following a linear increasing trend. Cluster 4 also has a higher number of landfall cyclones compared to other clusters. Cluster 2, located in the middle of the SIO, is characterized by the long track, high intensity, long lifespan, and high accumulated energy. Sea surface temperature (SST) and outgoing longwave radiation (OLR) associated with genesis of TCs are also examined in each cluster. Cyclone genesis is co-located with the negative OLR anomaly and the positive SST anomaly. Localized SST anomalies are associated with clusters in the SIO; however, TC geneses of Cluster 4 are associated with SSTA all over the Indian Ocean (IO).

Southern hemisphere tropical cyclones: A critical analysis of regional characteristics

“…In the western North Pacific and North Atlantic, greater storm intensity is related to longer lifetimes (Kang & Elsner, 2015). Using cluster analysis, Rahman et al (2018) found that the storms with the highest LMI tend to occur over the warmer ocean regions that have the highest amount of potential energy available. This is opposite to global‐based observations by Frank & Young (2007), where, the high intensity storms from category 3 to 5 systems persist for the shortest duration (mean of 1.9–2.3 days).…”

“…The characteristics involving duration to attain LMI have not been investigated for the NH. In the SH, a possible reason for the different durations to achieve LMI observed (Figures 4 and 6), points to higher SSTs and a warm moist environment, which promote greater potential intensity and energy for sustaining a storm (Rahman et al, 2018; Schenkel et al, 2018). This is based on the statistically significant relationships found (in this study; Figure 4) between intensity, duration and SST, which is well known as a thermodynamic requirement for high intensity tropical cyclones (Bhaskaran et al, 2018; Emanuel et al, 2004; Wing et al, 2015).…”

Assessing the Effects of Land Cover Land Use Change on Precipitation Dynamics in Guangdong–Hong Kong–Macao Greater Bay Area from 2001 to 2019

“…The detailed processing process is shown in Figure 2. The standard deviation ellipse [36,37] is useful to analyze the spatial and temporal variation of precipitation centers and the trend of spatial distribution of precipitation regions. The long axis and the short axis of the ellipse surface were output, as well as the direction of the ellipse.…”

A Methodology for the Spatiotemporal Identification of Compound Hazards: Wind and Precipitation Extremes in Great Britain (1979–2019)

“…Trajectory clustering contrasts with the moving cluster approach, where the moving object is of interest (vs. the movement itself). Examples of trajectory clustering include cyclone track clustering in different world regions (Ramsay et al, 2012;Rahman et al, 2018). Among other factors, the characteristics of the data used influences the choice of the spatiotemporal clustering method.…”