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On using principal ...
On using principal components to represent stations in empirical-statistical downscaling
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Benestad, R.E (author)
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- Chen, Deliang, 1961 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences,Chalmers tekniska högskola,Chalmers University of Technology,University of Gothenburg
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Mezghan, A. (author)
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Fan, L. (author)
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Parding, K. (author)
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(creator_code:org_t)
- 2015-12-01
- 2015
- English.
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In: Tellus. Series A, Dynamic meteorology and oceanography. - : Stockholm University Press. - 0280-6495 .- 1600-0870. ; 67:28326
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
Keyword
- empirical–statistical downscaling
- temperature
- principal component analysis
- empirical–statistical downscaling
Publication and Content Type
- ref (subject category)
- art (subject category)
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