A novel algorithm for convective cell identification and tracking based on Optical Character Recognition Neural Network. Issue 16 (2nd November 2021)

• Record Type: Journal Article
• Authors: Ranganayakulu, S. V.; Subrahmanyam, K. V.; Niranjan, A.
• Is Part Of: Journal of electromagnetic waves and applications. Volume 35:Issue 16(2021)
• Page Start: 2239
• Page End: 2255
• Publication Date: 2021-11-02
• Subjects: Mesoscale Convective System -- convective cell -- identification -- tracking -- Optical Character Recognition -- DWR reflectivity images; Electromagnetic waves -- Periodicals; Electromagnetic devices -- Periodicals; 621.3
• DOI: 10.1080/09205071.2021.1941299
• Languages: English
• ISSNs: 0920-5071

Abstract

Abstract : Identifying the core of the convective cloud is paramount important to the identification of the Mesoscale Convective System (MCS) and its evolution is of great significance in the weather and climate system. To study the initiation and development of convective core systems, an automatic tracking algorithm named "Convective cell Identification and TRAcking (CITRA)" has been developed to identify and track the convective cells in MCSs using Doppler Weather Radar reflectivity images based Optical Character Recognition using Long Shortterm Memory Neural Network. Further, CITRA algorithm calculates the convective cell physical properties such as centroid, convective core size and area, duration, maximum extent, distance and direction from the Radar centre. CITRA algorithm tested on 1255 convective cells identification and tracked 90 distinct convective system families along with physical properties through their evolution during the monsoon periods of 2017–2019. The details of CITRA algorithm is described in the present paper.