Understanding spatiotemporal patterns of typhoon storm surge disasters based on their tropical cyclone track clusters in China

Abstract

Typhoon storm surge disasters have garnered much attention because of their catastrophic damages. We investigated spatiotemporal patterns of typhoon storm surge disasters based on their tropical cyclone track clusters to support disaster mitigation in China. We aggregated 172 typhoon storm surge disasters in the entire cluster. Then, we used the extended Finite-Mixture-Model to categorize these 172 disasters into three clusters according to their track clusters (westward, northward, and westward shift at the coastline). In general, not all temporal distributions of the frequency and damage showed significant trends in the entire cluster and three clusters from 1983 . Between 1983-2000 and 2001, the average annual frequency increased, and average annual direct economic loss and average annual fatalities decreased in the entire cluster. Although most temporal patterns in the three clusters were similar to those in the entire cluster, a positive growth ratio in the average annual direct economic loss was apparent between 1983-2000 and 2001-2018 in Cluster 3. For spatial patterns, southern and eastern regions were more affected by typhoon storm surge disasters than northern regions. In northern regions, Cluster 2 recorded the most disaster occurrences, direct economic losses, and fatalities. Track characteristics and mitigation measures were introduced to help understand disaster spatiotemporal patterns in the entire cluster and three clusters.

Changes in Tropical Cyclone Disasters Over China During 2001–2020

“…TCs with magnitude beyond that of a tropical depression (maximum wind ≥ 10.8 m/s) were considered. Here, the best-track data set was decomposed into three clusters using a probabilistic clustering technique (Gaffney, 2004;Gaffney et al, 2007), which has been widely conducted in TC-related research (K. Wang et al, 2021;Q. Wu et al, 2020;J.…”

“…TCs with magnitude beyond that of a tropical depression (maximum wind ≥ 10.8 m/s) were considered. Here, the best‐track data set was decomposed into three clusters using a probabilistic clustering technique (Gaffney, 2004; Gaffney et al., 2007), which has been widely conducted in TC‐related research (K. Wang et al., 2021; Q. Wu et al., 2020; J. Zhao et al., 2018). The method is a probabilistic curve clustering that uses a linear regression mixture model fitted to a second‐order polynomial to group tracks with similar characteristics by means of the TCs' spatial and temporal information of their locations versus time (Camargo et al., 2021).…”

Hazard Assessment of Typhoon on the Southeast Coast of China Using Grid Fixed-Point Method

“…Generalized Extreme Value (GEV) theory is often used to quantify the relationship between the intensity and frequency of extreme natural events [10]. The Gumbel distribution is usually chosen [11] to estimate wind speed, and its cumulative distribution function (CDF) can be described as:…”

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

“…The detection of separation relies on the existence of patterns, splitting the batch of predicted tracks into a definite number of clusters. Various numerical clustering methods have been applied to characterize TCs tracks, including finite mixture models (Camargo et al, 2007a(Camargo et al, , 2007b(Camargo et al, , 2008Gaffney et al, 2007;Kossin et al, 2010;Ramsay et al, 2012;Wang et al, 2021), k-means (Blender et al, 1997;Corporal-Lodangco et al, 2014;Elsner, 2003;Elsner & Liu, 2003), fuzzy clustering (Harr & Elsberry, 1995;H.-S. Kim et al, 2011), preferred direction (Lander, 1996), recurving process (Hodanish & Gray, 1993), self-organizing map (H.-K. Kim & Seo, 2016), mass moments (Miller et al, 2023;Nakamura et al, 2009) or standard deviational ellipse (Rahman et al, 2018). Although those studies were focusing on clustering best track data from the International Best Track Archive for Climate Stewardship project, the Joint Typhoon Warning Center or the Regional Specialized Meteorological Center (RSMC) Tokyo-Typhoon Center, that is, a unique path per TCs, the present study has a different aim: clustering tracks forecasted by an ENWP, to detect separation scenarios, at each initialization time.…”

“…The detection of separation relies on the existence of patterns, splitting the batch of predicted tracks into a definite number of clusters. Various numerical clustering methods have been applied to characterize TCs tracks, including finite mixture models (Camargo et al., 2007a, 2007b, 2008; Gaffney et al., 2007; Kossin et al., 2010; Ramsay et al., 2012; Wang et al., 2021), k ‐means (Blender et al., 1997; Corporal‐Lodangco et al., 2014; Elsner, 2003; Elsner & Liu, 2003), fuzzy clustering (Harr & Elsberry, 1995; H.‐S. Kim et al., 2011), preferred direction (Lander, 1996), recurving process (Hodanish & Gray, 1993), self‐organizing map (H.‐K.…”