Application of Remote Sensing for Tropical Cyclone Track Forecasting Based on Statistical Methods

A tropical cyclone disaster is one of the most destructive natural hazards on earth and the main cause of death or injuries to humans as well as damages or losses of valuable goods or properties, such as buildings, communication systems, agricultural land, etc. To mitigate severe impacts, the accuracy of track forecasting model is world-widely developed and improved. The accuracy of tropical cyclone track forecasting is very important for risk area evaluation that will be affected by the tropical cyclone due to evacuation in time can reduce both human and property losses. However, Thailand has insufficient meteorological data to apply the numerical weather prediction models. In fact, the forecasting in Thailand is done manually. This makes the forecasting unreliable and time consuming, which leaves not enough time to prepare a warning bulletin. To address these problems, this paper proposes an integrated short-range tropical cyclone track forecasting system which analyzes tropical cyclone tracks from available satellite images. The performance of the model is satisfactory, giving an average of 4.92 degrees of 6 hours, 12 hours, 24 hours, 48 hours and 72 hours forecasting errors from best track data and the average error is lower than traditional techniques by 25.45% on Mercator projection map.