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Abstract :
[en] Several satellites measure Sea Surface Temperature (SST), each of these with different technical specificities and error sources. Together with in situ data, they form a highly complementary data set. The creation of merged SST products, integrating the strengths of each of its components and minimising their weaknesses, is however not an easy task, but it is certainly a desirable goal that has generated a large amount of research over the last years. The main objectives of this project are, among others:
1.To develop a technology, based on DINEOF (Data Interpolating Empirical Orthogonal Functions), that allows to merge different data sets at very different sampling intervals (in space and time) and create an integrated product at the highest sampling frequency and with the highest quality possible.
2.To provide improved, merged analyses of variables such as SST and chlorophyll.
3.Obtain a better understanding of the diurnal cycle of the studied variables.
4.To better understand the relation between variables (and take advantage of these relationship to improve the analyses).
5.Using the above-mentioned developments, explore the capability of the developed technology to produce SST forecasts based on multi-variate EOFs and model forecasts.
We will present the first preliminary results for merging different SST data sets, as well as our plans for future developments and applications.
Website of the project: http://www.gher.ulg.ac.be/HiSea/
