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

Abstract

SUMMARYThe 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 timescales 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.

Mediterranean Forecasting System: forecast and analysis assessment through skill scores

“…The model is forced at the air-sea interface with atmospheric fields from the European Centre for Medium-Range Weather Forecast (ECMWF) analyses and forecasts. The assimilation scheme used is a reduced order Optimal Interpolation system implemented in the Mediterranean Sea at different levels of complexity for the past ten years (Dobricic et al, 2004(Dobricic et al, , 2007Demirov et al, 2003). The assimilated data are: temperature and salinity vertical profiles from eXpandable BathyThermograph (XBT) and Argo, and Sea Level Anomalies (SLA) from altimetry.…”

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

“…For both methods, the average response spread is relatively small (order 3 cm) and comparable to the SLA satellite errors (Dobricic et al, 2005). The area of maximum ocean response spread corresponds to the large-variance areas in the BHM-SVW realizations, as shown for example in figure 7 of Part I.…”

“…The e j are the explained variances or eigenvalues for each mode. The EOFs have maximum amplitude at the depth of the thermocline, around 100-200 m, and they are discussed in Pinardi et al (2008), Dobricic et al (2005) and Sparnocchia et al (2003). The p field at each grid point is modelled by a Gaussian function with a decay radius of 60 km that roughly mimics the size of large mesoscale eddies in the Mediterranean Sea.…”

“…Ocean state analyses are constructed using ECMWF surface-analysis forcing fields and by assimilating available ocean data using a one-day temporal window. The error-covariance matrix is a reduced-order, multivariate matrix, described in detail in Dobricic et al (2005). The order reduction results from the decomposition of the error covariance into leading vertical and horizontal modes.…”

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting

“…Vertical shifts of water masses in background (model) T-S profiles are applied in Haines et al (2006); Ricci et al (2005). Coupled T-S Empirical Orthogonal Function (EOF) modal decomposition is also employed to reconstruct vertical temperature and salinity profiles in Dobricic et al (2005); Fujii & Kamachi (2003); Maes et al (2000). Thus, most current operational ocean data assimilation systems adopt OI or 3DVAR and have the capacity to assimilate observed salinity profiles imposing a multivariate (mainly T-S) balance relationship.…”