Scientific strategies associated with mitigating the challenges in predicting track and intensity of cyclones across the world basins

Abstract

The present study analyses the scientific strategies associated with mitigating the challenges in predicting track and intensity of cyclones across the world basins. The relevant ‘51’ original publications on predicting track and intensity of cyclones across the world basins utilising numerical weather prediction (NWP) models, machine learning and satellite based approach were chosen using a focused orderly search and were used to develop the selection benchmarks for the systematic literature review. According to our review, out of selected ‘51’ studies, a total of ‘19’(37.25%) and ‘12’ (23.52%) and ‘9’ (17.64%) studies were focused on Pacific (including south China sea) Atlantic and North Indian Ocean (NIO) basins respectively. Three studies were conducted over both the Pacific and Atlantic Basins. However, the remaining ‘8’ studies were not over any specific basin. After 2015, the widespread use of machine based algorithm and analysis began, with a focus on the improvement in intensity prediction of TCs. Satellite based approach was employed in nearly all of the studies, and data assimilation techniques were in nascent stage during 2006-2013. To create detailed cyclone prediction track and intensity of cyclones across the world basins, a balance among data assimilation systems, modelling systems, and machine learning to uncover predictability drivers is necessary. Globally, predicting track and intensity of cyclones should take into account future climate change scenarios in the world basins. In order to gain improvement in this direction, further research is needed to fill in the knowledge gaps that have been identified.