Recent migration of tropical cyclones toward coasts

Wang, Shuai; Toumi, Ralf

doi:10.1126/science.abb9038

Cited by 149 publications

(124 citation statements)

References 42 publications

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“…The plot suggests that hurricanes with higher intensities are more likely to be distributed at shorter distances to the coastline. This phenomenon has been observed in other recent studies [26]. For example, Wang and Toumi have noted that the distance at which the tropical cyclone hits its peak intensity has grown closer to the coastline, decreasing at a rate of 30km per decade.…”

Section: Introductionsupporting

confidence: 78%

Johan

Wang

Tan

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Hurricanes are one of the most catastrophic natural forces with potential to inflict severe damages to properties and loss of human lives from high winds and inland flooding. Accurate long-term forecasting of the trajectory and intensity of advancing hurricanes is therefore crucial to provide timely warnings for civilians and emergency responders to mitigate costly damages and their lifethreatening impact. In this paper, we present a novel online learning framework called JOHAN that simultaneously predicts the trajectory and intensity of a hurricane based on outputs produced by an ensemble of dynamic (physical) hurricane models. In addition, JOHAN is designed to generate accurate forecasts of the ordinalvalued hurricane intensity categories to ensure that their severity level can be reliably communicated to the public. The framework also employs exponentially-weighted quantile loss functions to bias the algorithm towards improving its prediction accuracy for high category hurricanes approaching landfall. Experimental results using real-world hurricane data demonstrated the superiority of JOHAN compared to several state-of-the-art learning approaches. CCS CONCEPTS• Information systems → Data mining; • Applied computing → Forecasting.

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Section: Introductionsupporting

confidence: 78%

Johan

Wang

Tan

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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“…Both PC1 and PC2 shows signi cant interannual variability, while PC3 illustrates lower frequency variations compared with PC1 and PC2, with dominant negative values in mid 1990s and 2005 to 2015, positive values from the end of last century to the beginning of this century. The spatial pattern associated with PC3 also concurrent with a poleward migration of tropical cyclones toward East Asia coast, which might be connected with the PDO phase change and Hadley circulation expansion (Wang and Toumi 2021). Note that the combination of three leading EOF mode listed above captures overall spatial patterns of TC activity over WNP.…”

Section: Tc Track Datamentioning

confidence: 86%

Dynamical-Statistical Hybrid Seasonal Prediction of Tropical Cyclone Track Density Over Western North Pacific

Da-Quan

Chen²

2021

Preprint

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Compared with total account of basin-wide tropical cyclones (TC) genesis, the prevailing tracks of TC activity and its potential of landfalling is more important for disaster prevention. Despite its relatively lower predictability, a statistical-dynamical hybrid prediction model was developed based on the knowledge of the physical mechanism between western North Pacific (WNP) TC activity and related large-scale environmental fields from July to September. The leading modes of spatial-temporal variation of WNP TC tracks density its climatological peak season (July to September) was extracted using empirical orthogonal function (EOF) decomposition. The interannual variation of leading EOF modes of WNP TC track density was predicted using multiple linear regressions (MLR) method based on predictors selected by correlation analysis of both observational and Beijing Climate Center climate system model version 1.1 (BCC_CSM1.1) hindcast data. The predicted spatial distribution of WNP TC tracks density was obtained through weighted composite of forecasting EOF modes according to its variance explained respectively. Results of one-year-out cross validation indicates that forecast model well captures the interannual variation of WNP TC prevailing moving tracks, especially in South China Sea (SCS) and southeastern quadrant of WNP. The prediction skill enhanced with decreased forecast lead time, with anomaly correlation coefficient (ACC) of northern SCS and southeast quadrant of WNP reaches 0.6 for the period 1991-2020 with one month forecast lead time. Forecast assessment based on different ENSO phases indicate that source of predictability of WNP TC tracks was mainly originate from ENSO events, especially strong El Niño events.

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“…There is a growing consensus among scholars regarding the increasing intensity of, and risks induced by tropical cyclones (Edmonds et al, 2020;Ramsay, 2014). However, the risk posed by a tropical cyclone is not materialized unless it makes landfall (Camargo & Wing, 2021), and recent trend analysis suggests that tropical cyclones are more frequently moving toward the coastline and making landfall within their lifetime maximum intensity (Wang & Toumi, 2021). In recent decades, coastal communities in particular are experiencing more frequent and more intense tropical cyclones and associated storm surges and flooding than before (Bevacqua et al, 2020;Witze, 2018).…”

Section: List Of Tables Chapter Onementioning

confidence: 99%