Complex network approach for detecting tropical cyclones

Abstract

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.

Study of Interaction and Complete Merging of Binary Cyclones Using Complex Networks

“…So, inherently, we have made the assumption that the cyclone pair is not affected by the climate behavior outside the selected spatial region. Furthermore, in order to study the rapid intensification and weakening of the cyclones and the changes in their mutual interactions, we use a temporal resolution of 3 hours for the relative vorticity data set, as often used by cyclone track forecasters 60,70 .…”

“…Recently Gupta et al 60 used time-evolving complex networks of mean sea level pressure (MSLP) data to study cyclones in the North Indian Ocean and tropical North Atlantic Ocean basins. They demonstrated that network-based indicators can be used to characterize the topological evolution of the regional climate system during highly localized weather extremes which occur over short time scales such as cyclones and detect cyclone tracks, besides climate phenomena like monsoon and the El Niño-Southern Oscillation (ENSO) with occur over seasonal or annual time scales.…”

“…II B). In contrast to the correlation-based networks 60,62 which depicts only statistical relationships, the induced velocity networks represent real physical links indi-cating the induction of velocity by one flow element on the others. By considering the instantaneous vorticity field as a directed spatiotemporal network, we compute network measures, such as the in-degree and the out-degree, which count the number of links going to and arising from a particular grid point (see Sec.…”

“…II C). This enables us to track the changes in the interaction zone of the binary cyclone system at every instant, as they approach each other, instead of examining the whole lifespan of the cyclones as in Gupta et al 60 . We find that changes in the in-degree reflect the strength of the mutual interaction between two cyclones, while those in the out-degree are indicative of the vortical interactions within a cyclone.…”

“…Our results show that as the two cyclones approach each other, the transitions occur in the network topology, which can be used to classify the complete merging process into several interaction stages. Therefore, our analysis does not only help to characterize the evolution of the cyclone 60 but also quantifies better the mutual interaction when it is in the vicinity of another cyclone system compared to separation distance 17,28,29 and gives an early indication of the CM.…”