IIT Madras, IIT Hyderabad, German scientists tackle problem of tropical cyclones merging

NEW DELHI: A team of Indian Institute of Technology (IIT) Madras, IIT Hyderabad and Potsdam Institute of Climate Impact Research (PIK), Germany, carried out a research on merging of tropical cyclones. The team used a data-driven approach based on the interdisciplinary methodology of complex networks for the study.

Their findings have been published in an article ‘Study of interaction and complete merging of binary cyclones using complex networks’ published in Chaos: An Interdisciplinary Journal of Nonlinear Science.

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RI Sujith of department of aerospace engineering, IIT Madras, said, “Analysing cyclone interactions using the novel framework has the potential to improve the accuracy of the early warning signals provided by meteorological organisations to the government so that they can take pre-emptive and early action to reduce the impact of such disasters.”

IIT Madras said the team studied the Fujiwhara interaction between two cyclonic vortices. Complex networks, which encodes the pattern of interaction of a complex system, were used to study the interaction, it added.

The team was able to distinguish the different stages of mutual interaction between two cyclones using this methodology and provide an early indication of cyclone merger, IIT Madras said. The new method is better than conventionally used indicators such as the separation distance between two cyclones, it added.

Somnath De, the lead author of the study, said “We believe that this network-based approach can be used to study binary cyclone interactions from observational or model-based relative vorticity data to obtain better insights on the possibility of cyclone merger. It paves the path to analyse such highly unusual events in which sudden alteration of cyclone tracks or re-strengthening occurs, on a case-by-case basis, and facilitate improved prediction of cyclone tracks and the fate of such interactions”.

Vishnu R Unni of IIT Hyderabad added, “Data-driven methods for prediction of extreme weather events have a unique advantage since they allow one to identify critical patterns in the evolution of such weather events that are elusive to traditional methods.”