A first attempt at assimilating microwave-derived SST to improve the predictive capability of a coupled, high-resolution Eta-POM forecasting system

Abstract

This study implements the assimilation of sea surface temperature (SST) data acquired by passive microwave remote sensing to a high-resolution, primitive-equation ocean model. The aim was to improve a forecasting tool capable of predicting the surface ocean processes linked to the air–sea interactions at sub-mesoscale level using one-way coupled, atmosphere–ocean modelling. An assimilation scheme based on a Newtonian relaxation scheme was fine-tuned to improve the forecasting skill of the ocean model. The ocean model was driven by predicted, synchronous air–sea fluxes derived by an overlying atmosphere model, remotely sensed SST and lateral boundary conditions derived from its previous run. The estimation of the model forecasting error was based on statistical and spatial comparison with remotely sensed observations. The optimal nudging coefficient was found to be 5 × 10⁻⁴ for 12 hours, giving a mean bias of −0.07°C. Forecast validation was done against calibrated AVHRR scenes using a new approach to calibrate region-specific scenes based on the split-window technique. This work demonstrates the benefit of using passive microwave remote sensing to improve high-resolution ocean forecasting systems. It also shows the high complementarity of infrared and passive microwave satellite sensors to provide information on the surface thermodynamics of the Ionian Sea.

Acknowledgements

This work was carried out using the computing facilities of the Euro-Mediterranean Centre on Insular Coastal Dynamics (Council of Europe, Malta). The technical support and guidance of Svetlana Music, Slobodan Nickovic, Bosko Telenta, and Goran Pejanovic is much appreciated. The support of the Director of the Centre, Anton Micallef, is duly acknowledged. The authors are also grateful to Remote Sensing Systems (http://www.remss.com), sponsored by the NASA Earth Science REASoN DISCOVER Project, for making available important data used in this work.