Correlation analysis of climate indices and precipitation using wavelet image processing approach

Mingdong Sun, Xuyong Li, Gwangseob Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

For extensive research on the connection and the phase relationships between climate indices and the precipitation time series in Korea, three wavelet image processing are applied to examine the relation between the climate indices (NAO, SOI, NOI, PDO, WP and NP) and precipitation in Korea. The continuous wavelet, cross-wavelet analysis and wavelet coherence is utilized to expand and present regions with common high significant frequency and phase features of the precipitation and these climate indices time series. It is found that the all wavelet frequency spectrum of these climate indices time series have some similar significant frequency features as the spectrum of precipitation time series in Korea have. There are significant variations of around 4–6 years of periodicities in all spectrum analysis. It is illustrated that there is strong underlying connection between precipitation variability and climate indices that implied by the cross wavelet and wavelet coherence analysis. The results clearly demonstrate that the climate indices have the influential consistent correlation relationship with the precipitation variation in Korea.

Original languageEnglish
Title of host publicationFrontier Computing - Theory, Technologies and Applications FC 2017
EditorsNeil Y. Yen, Jason C. Hung, Lin Hui
PublisherSpringer Verlag
Pages231-240
Number of pages10
ISBN (Print)9789811073977
DOIs
StatePublished - 2018
Event6th International Conference on Frontier Computing, FC 2017 - Osaka, Japan
Duration: 12 Jul 201714 Jul 2017

Publication series

NameLecture Notes in Electrical Engineering
Volume464
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference6th International Conference on Frontier Computing, FC 2017
Country/TerritoryJapan
CityOsaka
Period12/07/1714/07/17

Keywords

  • Climate indices
  • Correlation analysis
  • Precipitation
  • Wavelet image processing

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