气候变化领域集成服务门户(CCPortal): Markov Chain Monte Carlo simulation and regression approach guided by El Niño–Southern Oscillation to model the tropical cyclone occurrence over the Bay of Bengal

CCPortal

DOI	10.1007/s00382-020-05610-x
	Markov Chain Monte Carlo simulation and regression approach guided by El Niño–Southern Oscillation to model the tropical cyclone occurrence over the Bay of Bengal
	Wahiduzzaman M.; Yeasmin A.; Luo J.-J.; Quadir D.A.; Van Amstel A.; Cheung K.; Yuan C.
发表日期	2021
ISSN	0930-7575
起始页码	873
结束页码	898
卷号	56 期号:2021-09-10
英文摘要	Tropical cyclone (TC) is one of the most devastating weather systems that causes enormous loss of life and property in the coastal regions of Bay of Bengal (BoB). Statistical forecasting of TC occurrence can help decision-makers and inhabitants in shoreline zones to take necessary planning and actions in advance. In this study, we have investigated the impact of El Niño–Southern Oscillation (ENSO) on the frequency of TC over the BoB by using 100 years TC and Southern Oscillation Index data. The frequency of TC is approximated through observation and Markov Chain Monte Carlo (MCMC) simulation. Two-sample Student’s t test has been applied for examining the statistical significance where the results are significant at 5% level for all cyclonic disturbances. The monthly and seasonal distribution show this feature more distinctly. The total annual frequency of depressions and cyclonic storms in El Niño and La Niña conditions does not differ much, but the monthly/seasonal distribution shows high differences for certain months and seasons. The simulated frequency of TC landfall using MCMC matches well with the observation. The proposed methodology is illustrated through a case study in BoB rim countries-Bangladesh, India, Sri Lanka and Myanmar. Poisson and Bayesian regression have also been used to predict the probabilities of TC frequency over the BoB. Both the regression approaches show 10 and 32% improvement than climatology for the forecast and cross-validation skill respectively. We have also analyzed TC impact over Bangladesh as a case study. Possible links of the variation of TC activities with the largescale geographical distribution of sea surface temperature, vertical wind shear, vorticity, moisture and relative humidity are also explored. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
英文关键词	Bangladesh; Bay of Bengal; Bayesian regression; El Niño–Southern Oscillation; Markov Chain Monte Carlo; Poisson regression; Tropical cyclones
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/183285
作者单位	Institute for Climate and Application Research (ICAR)/Key Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology (NUIST), Nanjing, China; School of Engineering, Information Technology and Physical Sciences, Federation University, Ballarat, VIC, Australia; NPI University of Bangladesh, Dhaka, Bangladesh; Department of Environmental Sciences (Environmental Systems Analysis Group), Wageningen University and Research Centre, Wageningen, Netherlands; Department of Climate Research, NSW Department of Planning Industry and Environment, Parramatta, NSW, Australia
推荐引用方式 GB/T 7714	Wahiduzzaman M.,Yeasmin A.,Luo J.-J.,等. Markov Chain Monte Carlo simulation and regression approach guided by El Niño–Southern Oscillation to model the tropical cyclone occurrence over the Bay of Bengal[J],2021,56(2021-09-10).
APA	Wahiduzzaman M..,Yeasmin A..,Luo J.-J..,Quadir D.A..,Van Amstel A..,...&Yuan C..(2021).Markov Chain Monte Carlo simulation and regression approach guided by El Niño–Southern Oscillation to model the tropical cyclone occurrence over the Bay of Bengal.Climate Dynamics,56(2021-09-10).
MLA	Wahiduzzaman M.,et al."Markov Chain Monte Carlo simulation and regression approach guided by El Niño–Southern Oscillation to model the tropical cyclone occurrence over the Bay of Bengal".Climate Dynamics 56.2021-09-10(2021).