A large number of SLA observations at a high along track horizontal resolution are an important ingredi- ent of the data assimilation in the Mediterranean Forecasting System (MFS). Recently, new higher-frequency SLA prod- ucts have become available, and the atmospheric pressure forcing has been implemented in the numerical model used in the MFS data assimilation system. In a set of numerical ex- periments, we show that, in order to obtain the most accurate analyses, the ocean model should include the atmospheric pressure forcing and the observations should contain the at- mospheric pressure signal. When the model is not forced by the atmospheric pressure, the high-frequency filtering of SLA observations, however, improves the quality of the SLA analyses. It is further shown by comparing the power density spectra of the model fields and observations that the model is able to extract the correct information from noisy observa- tions even without their filtering during the pre-processing.

Assimilation of SLA along track observations in the Mediterranean with an oceanographic model forced by atmospheric pressure

P. Oddo;PINARDI, NADIA;
2012

Abstract

A large number of SLA observations at a high along track horizontal resolution are an important ingredi- ent of the data assimilation in the Mediterranean Forecasting System (MFS). Recently, new higher-frequency SLA prod- ucts have become available, and the atmospheric pressure forcing has been implemented in the numerical model used in the MFS data assimilation system. In a set of numerical ex- periments, we show that, in order to obtain the most accurate analyses, the ocean model should include the atmospheric pressure forcing and the observations should contain the at- mospheric pressure signal. When the model is not forced by the atmospheric pressure, the high-frequency filtering of SLA observations, however, improves the quality of the SLA analyses. It is further shown by comparing the power density spectra of the model fields and observations that the model is able to extract the correct information from noisy observa- tions even without their filtering during the pre-processing.
2012
S. Dobricic; C. Dufau; P. Oddo; N. Pinardi; I. Pujol; M.-H. Rio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/129064
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