Welkom op het expertplatform!
Dit platform verschaft informatie en kennis omtrent de WL expertisedomeinen 'hydraulica en sediment', 'havens en waterwegen', 'waterbouwkundige constructies', 'waterbeheer' en 'kustbescherming' - gaande van WL medewerkers met hun expertise, het curriculum van deze instelling, tot publicaties, projecten, data (op termijn) en evenementen waarin het WL betrokken is.
Het WL onderschrijft het belang van "open access" voor de ontsluiting van haar onderzoeksresultaten. Lees er meer over in ons openaccessbeleid.
Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF
Alvera-Azcárate, A.; Barth, A.; Sirjacobs, D.; Beckers, J.M. (2009). Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF. Ocean Sci. 5(4): 475-485
In: Ocean Science. Copernicus: Göttingen. ISSN 1812-0784; e-ISSN 1812-0792
|   |
| Auteurs | | Top |
- Alvera-Azcárate, A.
- Barth, A.
- Sirjacobs, D.
- Beckers, J.M.
|
|
|
| Abstract |
DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
