k-Gaps: a novel technique for clustering incomplete climatological time series



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Carro Calvo, Leopoldo and Jaume Santero, Fernando and García Herrera, Ricardo and Salcedo Sanz, Sancho (2021) k-Gaps: a novel technique for clustering incomplete climatological time series. Theoretical and Applied Climatology, 143 (1-2). pp. 447-460. ISSN 0177-798X

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Official URL: http://dx.doi.org/10.1007/s00704-020-03396-w


In this paper, we show a new clustering technique (k-gaps) aiming to generate a robust regionalization using sparse climate datasets with incomplete information in space and time. Hence, this method provides a new approach to cluster time series of different temporal lengths, using most of the information contained in heterogeneous sets of climate records that, otherwise, would be eliminated during data homogenization procedures. The robustness of the method has been validated with different synthetic datasets, demonstrating that k-gaps performs well with sample-starved datasets and missing climate information for at least 55% of the study period. We show that the algorithm is able to generate a climatically consistent regionalization based on temperature observations similar to those obtained with complete time series, outperforming other clustering methodologies developed to work with fragmentary information. k-Gaps clusters can therefore provide a useful framework for the study of long-term climate trends and the detection of past extreme events at regional scales.

Item Type:Article
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© The Author(s) 2020. This work was supported by the Ministerio de Economía y Competitividad through the PALEOSTRAT (CGL2015-69699-R) and TIN2017-85887-C2-2-P projects. Jaume-Santero was funded by grant BES-2016-077030 from the Ministerio de Economía y Competitividad and the European Social Fund.

Uncontrolled Keywords:Clustering techniques; Climatological time series; Climate trends; Regional analysis
Subjects:Sciences > Physics > Atmospheric physics
ID Code:65285
Deposited On:17 May 2021 16:21
Last Modified:19 May 2021 15:11

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