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Assimilation of Ocean Colour Data Into a Biochemical Flux Model of the Eastern Mediterranean Sea : Volume 3, Issue 5 (21/09/2006)

By Triantafyllou, G.

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Book Id: WPLBN0004020938
Format Type: PDF Article :
File Size: Pages 40
Reproduction Date: 2015

Title: Assimilation of Ocean Colour Data Into a Biochemical Flux Model of the Eastern Mediterranean Sea : Volume 3, Issue 5 (21/09/2006)  
Author: Triantafyllou, G.
Volume: Vol. 3, Issue 5
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2006
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Hoteit, I., Korres, G., Petihakis, G., Triantafyllou, G., & Banks, A. C. (2006). Assimilation of Ocean Colour Data Into a Biochemical Flux Model of the Eastern Mediterranean Sea : Volume 3, Issue 5 (21/09/2006). Retrieved from http://worldlibrary.in/


Description
Description: Hellenic Centre for Marine Research, Anavissos, Greece. Within the framework of the European MFSTEP project, an advanced multivariate sequential data assimilation system has been implemented to assimilate real chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) into a three-dimensional biochemical model of the Eastern Mediterranean. The physical ocean is described through the Princeton Ocean Model (POM) while the biochemistry of the ecosystem is tackled with the Biochemical Flux Model (BFM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics were parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs). A radius of influence was further selected around every data point to limit the range of the EOFs spatial correlations. The assimilation experiment was performed for one year over 1999 and forced with ECMWF 6 hour atmospheric fields. The accuracy of the ecological state identification by the assimilation system is assessed by the relevance of the system in fitting the data, and through the impact of the assimilation on non-observed biochemical processes. Assimilation of SeaWiFS data significantly improves the forecasting capability of the BFM model. Results, however, indicate the necessity of subsurface data to enhance the controllability of the ecosystem model in the deep layers.

Summary
Assimilation of ocean colour data into a Biochemical Flux Model of the Eastern Mediterranean Sea

 

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