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Statistical forecasting of tropical cyclone landfall activities over the North Indian Ocean rim countries


Wahiduzzaman, M and Yeasmin, A, Statistical forecasting of tropical cyclone landfall activities over the North Indian Ocean rim countries, Atmospheric Research, 227 pp. 89-100. ISSN 0169-8095 (2019) [Refereed Article]

Copyright Statement

2019 Elsevier B.V. All rights reserved.

DOI: doi:10.1016/j.atmosres.2019.04.034


This paper investigates the forecast potential of tropical cyclone (TC) landfall probabilities over the North Indian Ocean (NIO) rim countries using ocean-climate predictor variables in a new statistical seasonal forecast model. A Poisson regression model was used to predict the landfall probabilities for a period of 35 years of TC observations (19792013) from the Joint Typhoon Warning Center and the predictor variables include sea surface temperature, Southern Oscillation Index and ocean heat content. Poisson regression was found skilful in hindcasting of tropical cyclone landfall frequency for the NIO region with a correlation coefficient in the forecasted hindcast time series of 0.65 and 31% improvement above climatology. In the present study, genesis was modelled by kernel density estimation, tracks were fitted using a generalised additive model (GAM) approach with a Euler integration step, and landfall location was estimated using a country mask. This GAM model that is previously demonstrated very skilful to simulate TC landfall across the NIO rim countries by Wahiduzzaman et al., 2017, Wahiduzzaman et al., 2019 deems skilful for this study.

Item Details

Item Type:Refereed Article
Keywords:statistical forecasting, tropical cyclones, poisson regression, generalised additive model, North Indian Ocean
Research Division:Earth Sciences
Research Group:Atmospheric sciences
Research Field:Meteorology
Objective Division:Environmental Management
Objective Group:Air quality, atmosphere and weather
Objective Field:Atmospheric processes and dynamics
UTAS Author:Wahiduzzaman, M (Mr Mohammad Wahiduzzaman)
ID Code:137542
Year Published:2019
Web of Science® Times Cited:24
Deposited By:Oceans and Cryosphere
Deposited On:2020-02-19
Last Modified:2020-04-16

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