Author(s): Upendra Kumar Sahoo and Tirthankar Ghosh

Abstract: A tropical cyclone (TC) is a rapidly rotating atmospheric system that has a low-pressure center, namely an eye, strong winds, and a spiral arrangement of thunderstorms that produces heavy rain and causes severe destruction. Every year TC of the Northern Indian Ocean (NIO) basin affects South-eastern and Southwestern India significantly. Super Cyclone (1999), Mala (2006), Gonu (2007), Nargis (2008), Aila (2009), Giri (2010), Phailin (2013), Hudhud (2014), Fani (2019), Pabuk (2019), Amphan (2020), Yaas (2021) are the few such cyclones which affected the Indian coastal region at a large extent. Therefore, reliable forecasts of these events are very essential. It is the intensity, maximum wind speed of a storm, that causes damage to properties and lives. Therefore, along with track, intensity prediction of TCs should also be emphasized. In this study, a simple linear regression and an artificial neural network models are proposed for predicting the intensity of cyclones over NIO. The model parameters are estimated from the cyclone database that developed over the NIO basin during the period 2001-2019. Minimum Sea level Pressure (MSLP) and maximum sustained wind (VMAX) are selected as the parameters for the models. In the study we also compared the forecast results of considered the models with Indian Meteorological department (IMD) models. Simple regression model mostly outperforms all the model included in the study. The results indicate the suitability of the model for operational use.

DOI: 10.22271/maths.2023.v8.i6b.1464

Pages: 138-150 | Views: 130 | Downloads: 13

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