Theoretical and Applied Climatology

, Volume 132, Issue 1–2, pp 301–311 | Cite as

The application of low-rank and sparse decomposition method in the field of climatology

Original Paper


The present study reports a low-rank and sparse decomposition method that separates the mean and the variability of a climate data field. Until now, the application of this technique was limited only in areas such as image processing, web data ranking, and bioinformatics data analysis. In climate science, this method exactly separates the original data into a set of low-rank and sparse components, wherein the low-rank components depict the linearly correlated dataset (expected or mean behavior), and the sparse component represents the variation or perturbation in the dataset from its mean behavior. The study attempts to verify the efficacy of this proposed technique in the field of climatology with two examples of real world. The first example attempts this technique on the maximum wind-speed (MWS) data for the Indian Ocean (IO) region. The study brings to light a decadal reversal pattern in the MWS for the North Indian Ocean (NIO) during the months of June, July, and August (JJA). The second example deals with the sea surface temperature (SST) data for the Bay of Bengal region that exhibits a distinct pattern in the sparse component. The study highlights the importance of the proposed technique used for interpretation and visualization of climate data.


Low-rank and sparse decomposition Climate system Climate signal Wind speed Sea surface temperature Climate indices Indian Ocean 



The authors express their sincere gratitude to Dr. Swadhin Behera, Group Leader, Climate Variability Prediction and Application Research Group, Application Laboratory, JAMSTEC, Japan for sharing the IODS index values used in the present study.


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Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.Department of Ocean Engineering and Naval ArchitectureIndian Institute of Technology KharagpurKharagpurIndia

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