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Improved field reconstruction with the analog method: searching the CCA space

Journal: Climate Research
Year: 2003   Volume: 24
Initial page: 199   Last page: 213
Status: Published
PDF file: 2003_fernandez_CRa.pdf
Authors:
, Sáenz, J.

The analog based downscaling method is revisited in an application to precipitation
data for the northern coast of the Iberian Peninsula. Analog situations of a large-scale predictor are
searched in the historical record and the regional-scale predictand is reconstructed by using the analog
records found. The usual approach is insensitive to the predictand variable, and we present a new
approach using Canonical Correlation Analysis (CCA), which consists in projecting the predictor
field onto the spatial patterns obtained in a CCA between the predictor and predictand variables and
searching for analogies in this dimension-reduced predictor. This approach is tested against the usual
analog search, based on the projection onto the patterns derived from Principal Component Analysis
(PCA), and the more commonly used linear CCA downscaling technique. In a projection space of the
same dimension, the new approach performs better in reconstructing the precipitation (based on correlation
and variance skill scores) than the PCA approach. The CCA linear method yields a similar
correlation skill by comparison to our new approach, but reconstructs a much lower fraction of the
variance. The non-normality of the probability density function inherent to the precipitation data is
partly lost by the linear method, whereas it is preserved by the analog methods. A sensitivity analysis
on several parameters of the analog search was also conducted. The improvement of the CCA
approach over analogs seems to be related to the identification in the predictor field of the areas most
closely connected to the predictand.

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