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
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 iteration...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-10-01
|
| Series: | Ocean Science |
| Online Access: | http://www.ocean-sci.net/10/845/2014/os-10-845-2014.pdf |
| Summary: | 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. |
|---|---|
| ISSN: | 1812-0784 1812-0792 |