English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

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

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.
https://doi.org/10.1063/5.0101714

Item is

Files

show Files
hide Files
:
28275oa.pdf (Publisher version), 7MB
Name:
28275oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
De, Somnath1, Author
Gupta, Shraddha2, Author              
Unni, Vishnu R.1, Author
Ravindran, Rewanth1, Author
Kasthuri, Praveen1, Author
Marwan, Norbert2, Author              
Kurths, Jürgen2, Author              
Sujith, R. I.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2023-01-182023-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0101714
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Extremes
Research topic keyword: Weather
Research topic keyword: Oceans
Regional keyword: Asia
Regional keyword: Oceania/Australia
Model / method: Nonlinear Data Analysis
OATYPE: Hybrid - American Institute of Physics
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 33 Sequence Number: 013129 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)