Tropical cyclone track and intensity forecasting using remotely-sensed images

Abstract

A tropical cyclone disaster is one of the most destructive natural hazards on earth and the main cause of death or injury to humans as well as damage or loss of valuable goods or properties, such as buildings, communication systems, agricultural land, etc. To mitigate severe impacts, track and intensity forecasting is a world-widely adopted process. With accurate forecasting, proactive measures can be appropriately applied on time to reduce both human and property losses. However, Thailand has insufficient meteorological data to apply the NWP model. In fact, the forecasting is done manually in Thailand. This makes the forecasting unreliable and time consuming, which leaves not enough time to prepare a good warning bulletin. To address these problems, this paper proposes an integrated short-range tropical cyclone track and intensity forecasting system by using only 11 features which were extracted from satellite images with improvement of the traditional statistical methods. The performance of the model is satisfactory, giving an average of 4.12 degrees of 6 hours, 12 hours and 24 hours track forecasting errors from best track data and the average errors is lower than traditional techniques by 14.16% on Mercator projection map and the average intensity forecasting errors of 6 hours, 12 hours and 24 hours lower than traditional techniques by 25.18%.