Complex network approach for detecting tropical cyclones

Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. C...

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Main Authors: Gupta, Shraddha, Boers, Niklas, Pappenberger, Florian, Kurths, Jürgen
Format: Article in Journal/Newspaper
Language:English
Published: Berlin 2021
Subjects:
550
Online Access:https://oa.tib.eu/renate/handle/123456789/7819
https://doi.org/10.34657/6860
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spelling ftleibnizopen:oai:oai.leibnizopen.de:bO9p6YoBg80Wlv182meD 2023-10-29T02:38:34+01:00 Complex network approach for detecting tropical cyclones Gupta, Shraddha Boers, Niklas Pappenberger, Florian Kurths, Jürgen 2021 application/pdf https://oa.tib.eu/renate/handle/123456789/7819 https://doi.org/10.34657/6860 eng eng Berlin Heidelberg : Springer CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ Climate dynamics : observational, theoretical and computational research on the climate system 57 (2021), Nr. 11-12 Complex networks Extreme weather event Mean sea level pressure Tropical cyclones 550 article Text 2021 ftleibnizopen https://doi.org/10.34657/6860 2023-10-01T23:11:02Z Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields. publishedVersion Article in Journal/Newspaper North Atlantic LeibnizOpen (The Leibniz Association)
institution Open Polar
collection LeibnizOpen (The Leibniz Association)
op_collection_id ftleibnizopen
language English
topic Complex networks
Extreme weather event
Mean sea level pressure
Tropical cyclones
550
spellingShingle Complex networks
Extreme weather event
Mean sea level pressure
Tropical cyclones
550
Gupta, Shraddha
Boers, Niklas
Pappenberger, Florian
Kurths, Jürgen
Complex network approach for detecting tropical cyclones
topic_facet Complex networks
Extreme weather event
Mean sea level pressure
Tropical cyclones
550
description Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields. publishedVersion
format Article in Journal/Newspaper
author Gupta, Shraddha
Boers, Niklas
Pappenberger, Florian
Kurths, Jürgen
author_facet Gupta, Shraddha
Boers, Niklas
Pappenberger, Florian
Kurths, Jürgen
author_sort Gupta, Shraddha
title Complex network approach for detecting tropical cyclones
title_short Complex network approach for detecting tropical cyclones
title_full Complex network approach for detecting tropical cyclones
title_fullStr Complex network approach for detecting tropical cyclones
title_full_unstemmed Complex network approach for detecting tropical cyclones
title_sort complex network approach for detecting tropical cyclones
publisher Berlin
publishDate 2021
url https://oa.tib.eu/renate/handle/123456789/7819
https://doi.org/10.34657/6860
genre North Atlantic
genre_facet North Atlantic
op_source Climate dynamics : observational, theoretical and computational research on the climate system 57 (2021), Nr. 11-12
op_rights CC BY 4.0 Unported
https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.34657/6860
_version_ 1781064711149715456