- Title
- Seasonal movement prediction of tropical cyclone over the North Indian Ocean by using atmospheric climate variables in statistical models
- Creator
- Wahiduzzaman, Md; Yeasmin, Alea; Luo, Jing-Jia
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/174674
- Identifier
- vital:14911
- Identifier
-
https://doi.org/10.1016/j.atmosres.2020.105089
- Identifier
- ISBN:0169-8095
- Abstract
- In this study, the contribution of atmospheric climate variables to the prediction skill of tropical cyclone (TC) activity over the North Indian Ocean (NIO) has been investigated. Statistical forecast models are developed through generalised linear model (GLM) and generalised additive model (GAM) for tropical cyclone trajectories over the NIO using atmospheric climate variables as predictors. TC from the Joint Typhoon Warning Centre and sets of climate predictor data were analysed for a period of 35-year (1979-2013). The velocity field is predicted by fitting GAM in each month and season. Hindcast validation method is applied to assess the reliability of the model. The skill of the GAM model is compared with GLM and found to be more successful in forecasting TC movement over the NIO region.; M.Wahiduzzaman has been supported by Nanjing University of Information Science and Technology, China postdoctoral fellowship fund. Authors are grateful to three anonymous reviewers for their input which improved the quality of the manuscript.
- Publisher
- Elsevier
- Relation
- Atmospheric Research Vol. 245, no. (2020), p. 1-13
- Rights
- Metadata is freely available under a CCO license
- Rights
- Copyright © 2020 Elsevier B.V. All rights reserved.
- Subject
- 0299 Other Physical Sciences; 0401 Atmospheric Sciences; Cyclone movement; Atmospheric climate variables; Generalised additive model; Generalised linear model; North Indian Ocean
- Reviewed
- Funder
- M.Wahiduzzaman has been supported by Nanjing University of Information Science and Technology, China postdoctoral fellowship fund. Authors are grateful to three anonymous reviewers for their input which improved the quality of the manuscript.
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