RT Journal Article
T1 k-Gaps: a novel technique for clustering incomplete climatological time series
A1 Carro Calvo, Leopoldo
A1 Jaume Santero, Fernando
A1 García Herrera, Ricardo Francisco
A1 Salcedo Sanz, Sancho
AB 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.
PB Springer
SN 0177-798X
YR 2021
FD 2021-01
LK https://hdl.handle.net/20.500.14352/8088
UL https://hdl.handle.net/20.500.14352/8088
LA eng
NO © 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.
NO Ministerio de Economía y Competitividad (MINECO)/FEDER
DS Docta Complutense
RD 21 abr 2025