A network-based comparative study of extreme tropical and frontal storm rainfall over Japan

Ugur Ozturk*, Nishant Malik, Kevin Cheung, Norbert Marwan, Juergen Kurths

*Corresponding author for this work

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    Frequent and intense rainfall events demand innovative techniques to better predict the extreme rainfall dynamics. This task requires essentially the assessment of the basic types of atmospheric processes that trigger extreme rainfall, and then to examine the differences between those processes, which may help to identify key patterns to improve predictive algorithms. We employ tools from network theory to compare the spatial features of extreme rainfall over the Japanese archipelago and surrounding areas caused by two atmospheric processes: the Baiu front, which occurs mainly in June and July (JJ), and the tropical storms from August to November (ASON). We infer from complex networks of satellite-derived rainfall data, which are based on the nonlinear correlation measure of event synchronization. We compare the spatial scales involved in both systems and identify different regions which receive rainfall due to the large spatial scale of the Baiu and tropical storm systems. We observed that the spatial scales involved in the Baiu driven rainfall extremes, including the synoptic processes behind the frontal development, are larger than tropical storms, which even have long tracks during extratropical transitions. We further delineate regions of coherent rainfall during the two seasons based on network communities, identifying the horizontal (east–west) rainfall bands during JJ over the Japanese archipelago, while during ASON these bands align with the island arc of Japan.

    Original languageEnglish
    Pages (from-to)521-532
    Number of pages12
    JournalClimate Dynamics
    Volume53
    Issue number1-2
    Early online date9 Jan 2019
    DOIs
    Publication statusPublished - Jul 2019

    Keywords

    • Baiu
    • Complex networks
    • Event synchronization
    • Extreme rainfall
    • Tropical storms

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