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Journal Article

Study of interaction and complete merging of binary cyclones using complex networks


De,  Somnath
External Organizations;


Gupta,  Shraddha
Potsdam Institute for Climate Impact Research;

Unni,  Vishnu R.
External Organizations;

Ravindran,  Rewanth
External Organizations;

Kasthuri,  Praveen
External Organizations;


Marwan,  Norbert
Potsdam Institute for Climate Impact Research;


Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Sujith,  R. I.
External Organizations;

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De, S., Gupta, S., Unni, V. R., Ravindran, R., Kasthuri, P., Marwan, N., Kurths, J., Sujith, R. I. (2023): Study of interaction and complete merging of binary cyclones using complex networks. - Chaos, 33, 013129.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_28275
Cyclones are among the most hazardous extreme weather events on Earth. In certain scenarios, two co-rotating cyclones in close proximity to one another can drift closer and completely merge into a single cyclonic system. Identifying the dynamic transitions during such an interaction period of binary cyclones and predicting the complete merger (CM) event are challenging for weather forecasters. In this work, we suggest an innovative approach to understand the evolving vortical interactions between the cyclones during two such CM events (Noru–Kulap and Seroja–Odette) using time-evolving induced velocity-based unweighted directed networks. We find that network-based indicators, namely, in-degree and out-degree, quantify the changes in the interaction between the two cyclones and are excellent candidates to classify the interaction stages before a CM. The network indicators also help to identify the dominant cyclone during the period of interaction and quantify the variation of the strength of the dominating and merged cyclones. Finally, we show that the network measures also provide an early indication of the CM event well before its occurrence. In some active cyclone basins, more than one cyclone can be formed concurrently. Consequently, two or more cyclones can come in close spatial proximity and start interacting with each other; this type of interaction is known as the “Fujiwhara interaction.” Such an interaction may lead to many possibilities, such as weakening of both cyclones, sudden alteration in their tracks, re-strengthening of one of the cyclones due to vorticity interaction, and, very rarely, the birth of a more intense long-lived cyclone due to complete merging between them. This binary interaction between cyclones has not been fully understood and remains a major challenge for weather forecasters. This often leads to inaccurate predictions, increasing the risk of human life and property due to unpreparedness. Most previous investigations have used the separation distance between the cyclones to classify the stages of binary interaction leading to merging and to predict their merger. However, the separation distance between the cyclones does not only influence the Fujiwhara interaction but also depends on it. In particular, the Fujiwhara effect may alter the track of cyclones, leading to elastic interaction, partial straining out, or the partial merger between two cyclones. As a result, characterizing the behavior of binary cyclones based on the separation distance may be difficult. In this study, we use a novel approach based on complex networks. We analyze the vortical interactions in the spatial domain by constructing time-evolving induced velocity networks. Using two prominent examples of complete merger events, namely, the Seroja–Odette and Noru–Kulap interactions in the Northern and Southern Hemispheres, respectively, we show that network-based measures are successful in classifying the binary interaction stages.