Abstract
A tropical cyclone (TC) is one of the most destructive natural disasters that can cause heavy loss of life and property. Determining a TC’s center is crucial to TC forecasting. It is difficult to locate the center of a TC during its formation stage and dissipation period. To address this problem, a novel objective algorithm called cloud motion wind (CMW) was proposed for detecting a TC’s center using infrared (IR) image sequences from a geostationary meteorology satellite. First, the optical flow model with weighted median filtering was utilized to build a cloud motion wind field. Second, the density matrix method was used to calculate the center of the TC. FY-2E (Fengyun-2E geostationary meteorological satellites) IR images of three TCs in the Northwest Pacific, Halong, Rammasun and Haiyan were analyzed using the proposed algorithm. The present algorithm estimated the track with an averaged track error of around 41 km. Experimental results compared with the observed track that was given by the China Meteorological Administration (CMA) show that the proposed method provided accurate estimates of the cyclone center. The present algorithm has the potential to be employed to assist forecasters to detect the track of imminent TC.
Highlights
A tropical cyclone, which is a serious natural disaster, may cause heavy losses to society and economy when it reaches a certain extent
A number of methods for detecting the center of a tropical cyclone have been developed in the past few decades, including wind field analysis [6] and pattern matching [7,8], which used a linear discriminant analysis technique to determine the probability of an eye existing in any given IR image
Models based on three different machine learning (ML) algorithms were proposed for detecting tropical cyclone formation using satellite data [25]
Summary
A tropical cyclone, which is a serious natural disaster, may cause heavy losses to society and economy when it reaches a certain extent. A number of methods for detecting the center of a tropical cyclone have been developed in the past few decades, including wind field analysis [6] and pattern matching [7,8], which used a linear discriminant analysis technique to determine the probability of an eye existing in any given IR image. Models based on three different machine learning (ML) algorithms were proposed for detecting tropical cyclone formation using satellite data [25]. It is difficult to locate the center of a TC during its formation stage and dissipation period To address this problem, this paper proposed a new algorithm to identify the center of a TC based on cloud motion wind. For more information about the density matrix method, please refer to our previous work [31]
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