TY  - JOUR
A1  - Ozturk, Ugur
A1  - Marwan, Norbert
A1  - Korup, Oliver
A1  - Saito, H.
A1  - Agarwa, Ankit
A1  - Grossman, M. J.
A1  - Zaiki, M.
A1  - Kurths, Jürgen
T1  - Complex networks for tracking extreme rainfall during typhoons
JF  - Chaos : an interdisciplinary journal of nonlinear science
N2  - Reconciling the paths of extreme rainfall with those of typhoons remains difficult despite advanced forecasting techniques. We use complex networks defined by a nonlinear synchronization measure termed event synchronization to track extreme rainfall over the Japanese islands. Directed networks objectively record patterns of heavy rain brought by frontal storms and typhoons but mask out contributions of local convective storms. We propose a radial rank method to show that paths of extreme rainfall in the typhoon season (August-November, ASON) follow the overall southwest-northeast motion of typhoons and mean rainfall gradient of Japan. The associated eye-of-the-typhoon tracks deviate notably and may thus distort estimates of heavy typhoon rainfall. We mainly found that the lower spread of rainfall tracks in ASON may enable better hindcasting than for westerly-fed frontal storms in June and July.
Y1  - 2018
U6  - https://doi.org/10.1063/1.5004480
SN  - 1054-1500
SN  - 1089-7682
VL  - 28
IS  - 7
PB  - American Institute of Physics
CY  - Melville
ER  - 
TY  - JOUR
A1  - Agarwal, Ankit
A1  - Maheswaran, Rathinasamy
A1  - Marwan, Norbert
A1  - Caesar, Levke
A1  - Kurths, Jürgen
T1  - Wavelet-based multiscale similarity measure for complex networks
JF  - The European physical journal : B, Condensed matter and complex systems
N2  - In recent years, complex network analysis facilitated the identification of universal and unexpected patterns in complex climate systems. However, the analysis and representation of a multiscale complex relationship that exists in the global climate system are limited. A logical first step in addressing this issue is to construct multiple networks over different timescales. Therefore, we propose to apply the wavelet multiscale correlation (WMC) similarity measure, which is a combination of two state-of-the-art methods, viz. wavelet and Pearson’s correlation, for investigating multiscale processes through complex networks. Firstly we decompose the data over different timescales using the wavelet approach and subsequently construct a corresponding network by Pearson’s correlation. The proposed approach is illustrated and tested on two synthetics and one real-world example. The first synthetic case study shows the efficacy of the proposed approach to unravel scale-specific connections, which are often undiscovered at a single scale. The second synthetic case study illustrates that by dividing and constructing a separate network for each time window we can detect significant changes in the signal structure. The real-world example investigates the behavior of the global sea surface temperature (SST) network at different timescales. Intriguingly, we notice that spatial dependent structure in SST evolves temporally. Overall, the proposed measure has an immense potential to provide essential insights on understanding and extending complex multivariate process studies at multiple scales.
KW  - Statistical and Nonlinear Physics
Y1  - 2018
U6  - https://doi.org/10.1140/epjb/e2018-90460-6
SN  - 1434-6028
SN  - 1434-6036
VL  - 91
IS  - 11
PB  - Springer
CY  - New York
ER  -