Open Access Digital Repository of Ministry of Earth Sciences, Government of India

Rainfall patterns over India: Classification with Fuzzy c-means method

Kulkarni, A and Kripalani, RH (1998) Rainfall patterns over India: Classification with Fuzzy c-means method. Theoretical and Applied Climatology, 59 (3-4). pp. 137-146.

Full text not available from this repository. (Request a copy)

Abstract

Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871-1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed.

Item Type: Article
Additional Information: Copyright of this article belongs to Springer.
Uncontrolled Keywords: classification; clustering; monsoon; rainfall, India
Subjects: Meteorology and Climatology
Depositing User: IITM Library
Date Deposited: 26 Feb 2015 06:09
Last Modified: 26 Feb 2015 06:09
URI: http://moeseprints.incois.gov.in/id/eprint/1668

Actions (login required)

View Item View Item