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  Non-linear time series analysis of precipitation events using regional climate networks for Germany

Rheinwalt, A., Boers, N., Marwan, N., Kurths, J., Hoffmann, P., Gerstengarbe, F.-W., Werner, P. C. (2016): Non-linear time series analysis of precipitation events using regional climate networks for Germany. - Climate Dynamics, 46, 3, 1065-1074.
https://doi.org/10.1007/s00382-015-2632-z

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_20539 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_20539_6
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Rheinwalt, Aljoscha1, ???ENUM_CREATORROLE_AUTHOR???           
Boers, Niklas1, ???ENUM_CREATORROLE_AUTHOR???                 
Marwan, Norbert1, ???ENUM_CREATORROLE_AUTHOR???                 
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
Hoffmann, Peter1, ???ENUM_CREATORROLE_AUTHOR???                 
Gerstengarbe, Friedrich-Wilhelm1, ???ENUM_CREATORROLE_AUTHOR???           
Werner, Peter C.1, ???ENUM_CREATORROLE_AUTHOR???           
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: Synchronous occurrences of heavy rainfall events and the study of their relation in time and space are of large socio-economical relevance, for instance for the agricultural and insurance sectors, but also for the general well-being of the population. In this study, the spatial synchronization structure is analyzed as a regional climate network constructed from precipitation event series. The similarity between event series is determined by the number of synchronous occurrences. We propose a novel standardization of this number that results in synchronization scores which are not biased by the number of events in the respective time series. Additionally, we introduce a new version of the network measure directionality that measures the spatial directionality of weighted links by also taking account of the effects of the spatial embedding of the network. This measure provides an estimate of heavy precipitation isochrones by pointing out directions along which rainfall events synchronize. We propose a climatological interpretation of this measure in terms of propagating fronts or event traces and confirm it for Germany by comparing our results to known atmospheric circulation patterns.

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 ???ViewItemFull_lblDates???: 2016
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1007/s00382-015-2632-z
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: Climate Impacts & Vulnerabilities - Research Domain II
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: Transdisciplinary Concepts & Methods - Research Domain IV
???ENUM_IDENTIFIERTYPE_EDOC???: 6995
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Extremes
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Nonlinear Data Analysis
???ENUM_IDENTIFIERTYPE_REGIONALK???: Germany
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: FutureLab - Artificial Intelligence in the Anthropocene
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Hydroclimatic Risks
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Development of advanced time series analysis techniques
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Network- and machine-learning-based prediction of extreme events
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???ViewItemFull_lblSourceTitle???: Climate Dynamics
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
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