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On using principal components to represent stations in empirical-statistical downscaling

Artikel i vetenskaplig tidskrift
Författare R.E Benestad
Deliang Chen
A. Mezghan
L. Fan
K. Parding
Publicerad i Tellus. Series A, Dynamic meteorology and oceanography
Volym 67
Nummer/häfte 28326
ISSN 0280-6495
Publiceringsår 2015
Publicerad vid Institutionen för geovetenskaper
Språk en
Länkar dx.doi.org/10.3402/tellusa.v67.2832...
Ämnesord empirical–statistical downscaling, temperature, principal component analysis
Ämneskategorier Geovetenskap och miljövetenskap

Sammanfattning

We test a strategy for downscaling seasonal mean temperature for many locations within a region, based on principal component analysis (PCA), and assess potential benefits of this strategy which include an enhancement of the signal-to-noise ratio, more efficient computations, and reduced sensitivity to the choice of predictor domain. These conditions are tested in some case studies for parts of Europe (northern and central) and northern China. Results show that the downscaled results were not highly sensitive to whether a PCA-basis or a more traditional strategy was used. However, the results based on a PCA were associated with marginally and systematically higher correlation scores as well as lower root-mean-squared errors. The results were also consistent with the notion that PCA emphasises the large-scale dependency in the station data and an enhancement of the signal-to-noise ratio. Furthermore, the computations were more efficient when the predictands were represented in terms of principal components.

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