Relation between SLA and SST: coupled pattern analysis of the data collected during two years of Mediterranean forecasting system pilot project (MFSPP)

Bruno Buongiorno Nardelli; Gilles Larnicol; Emma D'Acunzo; Rosalia Santoleri; Pierre-Yves Le Traon

doi:10.1117/12.452743

14 January 2002 Relation between SLA and SST: coupled pattern analysis of the data collected during two years of Mediterranean forecasting system pilot project (MFSPP)

Bruno Buongiorno Nardelli, Gilles Larnicol, Emma D'Acunzo, Rosalia Santoleri, Pierre-Yves Le Traon

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Proceedings Volume 4544, Remote Sensing of the Ocean and Sea Ice 2001; (2002) https://doi.org/10.1117/12.452743
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

Remote sensing instruments provide a huge volume of measurements of sea surface parameters which are of fundamental interest to the oceanographic community, mainly because of the high spatial and temporal coverage of satellite sensors respect to traditional techniques. In fact, the whole ocean circulation is actually driven by processes occurring at the air-sea interface, as the energy is mainly supplied to the ocean by atmosphere-ocean interactions. Consequently, altimeter derived SSH and SST obtained from AVHRR, that have already proved to be quit useful to evaluate the sea surface variability, can be though t as possible tools to investigate the ocean dynamics and atmosphere-ocean interaction more deeply. In this work, 2 years of Mediterranean Forecasting System Pilot Project dat over the Mediterranean basin are analyzed. The relationship between SST and SLA is investigated applying an objective method for coupled pattern detection, based on the singular value decomposition of the covariance of the two data-sets.

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Bruno Buongiorno Nardelli, Gilles Larnicol, Emma D'Acunzo, Rosalia Santoleri, and Pierre-Yves Le Traon "Relation between SLA and SST: coupled pattern analysis of the data collected during two years of Mediterranean forecasting system pilot project (MFSPP)", Proc. SPIE 4544, Remote Sensing of the Ocean and Sea Ice 2001, (14 January 2002); https://doi.org/10.1117/12.452743

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KEYWORDS

Satellites

Remote sensing

Artificial intelligence

Data modeling

Sensors

Wind energy

Climatology

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