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| Titel |
On the sensitivity of field reconstruction and prediction using Empirical Orthogonal Functions derived from gappy data |
| VerfasserIn |
Marc Taylor, Martin Losch, Manfred Wenzel, Jens Schröter |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250081118
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| Zusammenfassung |
| Empirical Orthogonal Function (EOF) analysis is commonly used in the climate sciences and
elsewhere to describe, reconstruct, and predict highly dimensional data fields. When data
contain a high percentage of missing values (i.e. ’gappy’), alternate approaches must be used
in order to correctly derive EOFs. The aims of this paper are to assess the accuracy of
several EOF approaches in the reconstruction and prediction of gappy data fields,
using the Galapagos Archipelago as a case study area. EOF approaches included
least-squares estimations via a covariance matrix decomposition (EIGEN, SVD), ’Data
Interpolating Empirical Orthogonal Functions’ (DINEOF), and a novel approach called
’Recursively-Subtracted Empirical Orthogonal Functions’ (RSEOF). Model-derived data
of historical surface Chlorophyll a concentrations and sea surface temperature,
combined with a mask derived from gaps in remote sensing estimates, allowed for the
creation of ’true’ and ’observed’ fields by which to gauge the performance of EOF
approaches. Only DINEOF and RSEOF were found to be appropriate for gappy
data reconstruction and prediction. DINEOF proved to be the superior approach
in terms of accuracy, especially for data with a high Noise/Signal ratio, although
RSEOF may be preferred for larger data fields due to its relatively faster computation
time. |
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