Abstract
The purpose of this work is to present a new methodology for identifying geographical regions within which the climatic behaviour of a meteorological variable is coherent. We have chosen temperature as the variable of interest, and thermal coherence is defined here in the sense of having a strong (negative) correlation between terrain altitude and temperature. An improved method of constrained spatial cluster analysis is described in the form of a new constrained clustering algorithm. The methodology includes spatial bootstrap statistical tests to provide a more realistic measure of the uncertainty of the coefficient of correlation together with a spatial test of the correlation of residuals. The results are used as optimal estimates of areal temperature averages. The methodology is illustrated by applying it to the annual mean temperature measured at 1220 temperature stations across Spain.
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Dowd, P., Wang, H., Pardo-Igúzquiza, E., Yang, Y. (2017). Constrained Spatial Clustering of Climate Variables for Geostatistical Reconstruction of Optimal Time Series and Spatial Fields. In: Gómez-Hernández, J., Rodrigo-Ilarri, J., Rodrigo-Clavero, M., Cassiraga, E., Vargas-Guzmán, J. (eds) Geostatistics Valencia 2016. Quantitative Geology and Geostatistics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-46819-8_61
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DOI: https://doi.org/10.1007/978-3-319-46819-8_61
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