Complex networks for tracking extreme rainfall during typhoons

Öztürk, Uğur; Marwan, Norbert; Korup, Oliver; Saitô, Hitoshi; Agarwal, Ankit; Grossman, Michael; Zaiki, Masumi; Kurths, Jürgen

doi:10.1063/1.5004480

Cited by 35 publications

(28 citation statements)

References 65 publications

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“…The authors used a high-resolution (0.25 • × 0.25 • ) monthly gridded precipitation dataset developed by the Indian Meteorological Department (IMD) for the spatial domain of 66.5 to 100 • E and 6.5 to 38.5 • N, covering the mainland region of India (Pai et al, 2014). The gridded data have been generated from the observations of 6995 gauging stations across India (Pai et al, 2014). Details about these data can be obtained from http://imd.gov.in (last access: 13 August 2019) (homepage/rainfall information).…”

Section: Discussionmentioning

confidence: 99%

Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

Kurths

Agarwal

Shukla

et al. 2019

Nonlin. Processes Geophys.

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Abstract. A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting.

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Section: Discussionmentioning

confidence: 99%

Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

Kurths

Agarwal

Shukla

et al. 2019

Nonlin. Processes Geophys.

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“…Event synchronization (ES) has been specifically designed to calculate nonlinear correlations among bivariate time series with events defined on them (Quiroga et al, 2002). This method has advantages over other time-delayed correlation techniques (e.g., Pearson lag correlation), as it allows us to investigate extreme event series (such as non-Gaussian and event-like datasets) and uses a dynamic time delay (Ozturk et al, 2018). The latter refers to a time delay that is adjusted according to the two time series being compared, which allows for better adaptability to the variable and region of interest.…”

Section: Event Synchronizationmentioning

confidence: 99%

Optimal design of hydrometric station networks based on complex network analysis

Agarwal

Marwan

Rathinasamy

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.

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“…As a final note, we emphasize that previous related works (e.g., Ozturk et al, 2018Ozturk et al, , 2019 have employed a different eventbased similarity measure called event synchronization strength (ESS), which is based on a similar rationale as our event coincidence rates (Quiroga et al, 2002). As recent studies have revealed, ESS provides systematically biased values in the presence of temporally clustered events, i.e., events recorded at subsequent time steps, which is not the case for ECA (Hassanibesheli and Donner, 2019;Odenweller and Donner, 2020;Wolf et al, 2020)).…”

Section: Event Coincidence Analysis (Eca)mentioning

confidence: 96%

Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season

Wolf¹,

Öztürk²,

Cheung³

et al. 2020

Preprint

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Abstract. Investigating the synchrony and interdependency of heavy rainfall occurrences is crucial to understand the underlying physical mechanisms and reduce physical and economic damages by improved forecasting strategies. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events. To thoroughly expand on previous works employing the latter framework to the East Asian Summer Monsoon (EASM) system, we focus here on changes in the spatial organization of synchronous heavy precipitation events across the monsoon season (April to August) by studying the temporal evolution of corresponding network characteristics in terms of a sliding window approach. Specifically, we utilize functional climate networks together with event coincidence analysis for identifying and characterizing synchronous activity from daily rainfall estimates between 1998 and 2018. Our results demonstrate that the formation of the Baiu front as a main feature of the EASM is reflected by a double-band structure of synchronous heavy rainfall with two centers north and south of the front. Although the two separated bands are strongly related to either low- or high-level winds which are commonly assumed to be independent, we provide evidence that it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way. Our findings shed some new light on the interplay between tropical and extratropical factors controlling the EASM intraseasonal evolution, which could potentially help to improve future forecasts of the Baiu onset in different regions of East Asia.

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Complex networks for tracking extreme rainfall during typhoons

Cited by 35 publications

References 65 publications

Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

Optimal design of hydrometric station networks based on complex network analysis

Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season

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