IMIS | Flanders Marine Institute
 

Flanders Marine Institute

Platform for marine research

IMIS

Publications | Institutes | Persons | Datasets | Projects | Maps
[ report an error in this record ]basket (0): add | show Printer-friendly version

A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation
Beckers, J.-M.; Barth, A.; Tomazic, I.; Alvera-Azcárate, A. (2014). A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation. Ocean Sci. 10(5): 845-862. dx.doi.org/10.5194/os-10-845-2014
In: Ocean Science. Copernicus: Göttingen. ISSN 1812-0784, more
Peer reviewed article  

Available in  Authors 

Keyword
    Marine

Authors  Top 
  • Beckers, J.-M., more
  • Barth, A., more
  • Tomazic, I., more
  • Alvera-Azcárate, A., more

Abstract
    We present a method in which the optimal interpolation of multi-scale processes can be expanded into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the different mathematical equivalent formulations, we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well-controlled test case. The clear guidelines deduced from this experiment are then applied to a real situation in which we combine large-scale analysis of hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite images using data interpolating empirical orthogonal functions (DINEOF) with a local optimal interpolation using a Gaussian covariance. It is shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data.

All data in IMIS is subject to the VLIZ privacy policy Top | Authors