Cluster Analysis of North Atlantic Tropical Cyclones

Corporal-Lodangco, Irenea L.; Richman, Michael B.; Leslie, Lance M.; Lamb, Peter

doi:10.1016/j.procs.2014.09.096

Cited by 23 publications

(13 citation statements)

References 15 publications

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“…Multivariate statistical analysis is a branch developed from classical statistics and is a comprehensive analysis method [ 11 , 12 ]. It can analyze the statistical laws of multiple objects and multiple indicators when they are related to each other, including cluster analysis (CA) [ 13 ], discriminant analysis (DA) [ 14 ], principal component analysis (PCA) [ 15 ] and factor analysis (FA) [ 16 ]. Multivariate statistical analysis is a suitable tool for multi-component chemical and physical measurements for meaningful data reduction and interpretation [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

et al. 2021

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Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

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Section: Introductionmentioning

confidence: 99%

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

et al. 2021

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“…A few studies have used a clustering approach to study Atlantic tropical cyclones [Kossin et al, 2010;Kozar et al, 2012;Corporal-Lodangco et al, 2014;Daloz et al, 2015]. In particular, Kossin et al [2010], and later on Kozar et al [2012], have used Poisson regressions to link the variability of four clusters of tropical cyclones to different climate features.…”

Section: Introductionmentioning

confidence: 99%

Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis

2017

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We analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin‐wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane database through estimates of missing storms. The analysis confirms that tropical cyclones forming in different regions of the Atlantic are susceptible to different climate influences. We also investigate the presence of trends in these various time series, both at the basin‐wide and cluster levels, and show that, even after accounting for possible missing storms, there remains an upward trend in the eastern part of the basin and a downward trend in the western part. Using model selection algorithms, we show that the best model of Atlantic tropical cyclone activity for the recent past is constructed using Atlantic sea surface temperature and upper tropospheric temperature, while for the 1878–2015 period, the chosen covariates are Atlantic sea surface temperature and El Niño–Southern Oscillation. We also note that the presence of these artificial trends can impact the selection of the best covariates. If the underlying series shows an upward trend, then the mean Atlantic sea surface temperature captures both interannual variability and the upward trend, artificial or not. The relative sea surface temperature is chosen instead for stationary counts. Finally, we show that the predictive capability of the statistical models investigated is low for U.S. landfalling hurricanes but can be considerably improved when forecasting combinations of clusters whose hurricanes are most likely to make landfall.

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“…Additionally, to check the correct choice of k, the Silhouette coefficient was used; it returns the cohesion of a cluster and its separation with respect to the other clusters. As can be seen, it is notable that for two and three clusters, the maximum values are reached (see Figure 12e,f), being higher than 0.40 (fair solution according to Corporal-Lodangco et al [42]). Therefore, k = 3 was selected for both periods.…”

Section: Ranking Of Extreme Precipitation Events By Regionsmentioning

confidence: 84%

“…The Elbow plot shows at what value of k, the distance between the mean of a cluster and the other data points in the cluster is low and begins to show a change in the slope of the curve. The silhouette coefficient is a measure of the cohesion of each cluster and how well the clusters are separated [42]. The K-mean method has been also used at the Caribbean regional scale with reanalysis data for determining daily near-surface large-scale atmospheric circulation patterns over the wider Caribbean [43].…”

Section: Analysis Of Extreme Precipitation Events By Regionsmentioning

confidence: 99%

Space-Time Assessment of Extreme Precipitation in Cuba between 1980 and 2019 from Multi-Source Weighted-Ensemble Precipitation Dataset

et al. 2021

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Precipitation extremes such as heavy rainfall and floods are of great interest for climate scientists, particularly for small islands vulnerable to weather phenomena such as hurricanes. In this study, we investigated the spatio-temporal evolution of extreme rainfall over Cuba from 1980 to 2019, separating the dry and rainy periods. In addition, a ranking of extreme precipitation events was performed, which provides the number of events, the area affected, and a ranking of their magnitude by considering the magnitude of anomalies. The analysis was conducted using daily data from the multi-source weighted-ensemble precipitation (MSWEPv2). In determining the extreme precipitation ranking, the daily extreme precipitation anomaly was calculated with respect to the 95th percentile climatological distribution, giving a measure of the rarity of the event for each day and each grid point. For a more detailed analysis regarding the ranking, a separation was made by regions applying the K-mean methodology. The months belonging to the rainy period of the year presented the highest amount of precipitation above the 95th percentile compared to results obtained for the dry period. Of the six months belonging to the cyclonic season, in five of them Cuba was affected, directly or indirectly, by a tropical cyclone. The years 1982–83 and 1998 presented the highest-ranking value for the dry and rainy periods, respectively. Moreover, a trend analysis revealed an increase in the trend of occurrence of extreme events and a decrease in the percentage of the area affected. The analysis by regions showed a similar behavior to that carried out for all of Cuba. It was found that the warm phase of the ENSO events influenced approximately ~22% of the occurrence of extreme events for both periods.

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Cluster Analysis of North Atlantic Tropical Cyclones

Cited by 23 publications

References 15 publications

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis

Space-Time Assessment of Extreme Precipitation in Cuba between 1980 and 2019 from Multi-Source Weighted-Ensemble Precipitation Dataset

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