Mediterranean Forecasting System: An improved assimilation scheme for sea‐level anomaly and its validation

The assimilation of satellite and in situ data in the Mediterranean Forecast System (MFS) is based on an optimal interpolation scheme which uses empirical orthogonal functions (EOFs) to represent vertical modes of the background-error correlation matrix. In this study we present a new methodology to estimate, and the calculation of, these multivariate EOFs. The new EOFs are considered time and space varying (seasonal time-scales and subregional). They examine the vertical-error cross-variance between temperature, salinity, barotropic stream function and sea-level anomaly. These EOFs are used to assimilate four years of along-track sea-level anomaly data. The validation of MFS analyses and forecasts using the assimilation system diagnostics and the comparison with independent observations show that, in relation to an old operational scheme which was using only one EOF, the use of several multivariate EOFs significantly improves the accuracy of analyses and forecasts in the Mediterranean. Copyright © 2005 Royal Meteorological Society

[1]  Robert F. Cahalan,et al.  Sampling Errors in the Estimation of Empirical Orthogonal Functions , 1982 .

[2]  P. De Mey,et al.  The Mediterranean ocean forecasting system: first phase of implementation (1998–2001) , 2003 .

[3]  N. Pinardi,et al.  Simulation of the Mediterranean Sea circulation from 1979 to 1993: Part I. The interannual variability , 2002 .

[4]  P. L. Traon,et al.  AN IMPROVED MAPPING METHOD OF MULTISATELLITE ALTIMETER DATA , 1998 .

[5]  R. Todling,et al.  Suboptimal Schemes for Atmospheric Data Assimilation Based on the Kalman Filter , 1994 .

[6]  Nadia Pinardi,et al.  A model study of air–sea interactions in the Mediterranean Sea , 1998 .

[7]  M. Tonani,et al.  EU-sponsored effort improves monitoring of circulation variability in the Mediterranean , 2001 .

[8]  Anthony Rosati,et al.  The sea surface pressure formulation of rigid lid models. Implications for altimetric data assimilation studies , 1995 .

[9]  Mounir Benkiran,et al.  A Multivariate Reduced-order Optimal Interpolation Method and its Application to the Mediterranean Basin-scale Circulation , 2002 .

[10]  Ming Ji,et al.  An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part I: The Ocean Data Assimilation System , 1998 .

[11]  Marina Tonani,et al.  Assimilation scheme of the Mediterranean Forecasting System: operational implementation , 2003 .

[12]  S. Sparnocchia,et al.  Multivariate Empirical Orthogonal Function analysis of the upper thermocline structure of the Mediterranean Sea from observations and model simulations , 2003 .

[13]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[14]  J. Derber,et al.  A reformulation of the background error covariance in the ECMWF global data assimilation system , 1999 .

[15]  Nadia Pinardi,et al.  Variability of the large scale general circulation of the Mediterranean Sea from observations and modelling: a review , 2000 .