Abstract. Since 19 October 2016, and in the framework of Copernicus Marine Environment Monitoring Service (CMEMS), Mercator Ocean has delivered real-time daily services (weekly analyses and daily 10-day forecasts) with a new global 1∕12∘ high-resolution (eddy-resolving) monitoring and forecasting system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a three-dimensional multivariate modal decomposition of the background error. Along-track altimeter data, satellite sea surface temperature, sea ice concentration, and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-VAR scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. This paper describes the recent updates applied to the system and discusses the importance of fine tuning an ocean monitoring and forecasting system. It details more particularly the impact of the initialization, the correction of precipitation, the assimilation of climatological temperature and salinity in the deep ocean, the construction of the background error covariance and the adaptive tuning of observation error on increasing the realism of the analysis and forecasts. The scientific assessment of the ocean estimations are illustrated with diagnostics over some particular years, assorted with time series over the time period 2007–2016. The overall impact of the integration of all updates on the product quality is also discussed, highlighting a gain in performance and reliability of the current global monitoring and forecasting system compared to its previous version.

The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model. This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters) and data sources (satellite sea surface temperature) have as great an impact as the system design (model physics and assimilation parameters). The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007-2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean dynamic topography corrects local biases in the Indonesian Throughflow and in the western tropical Pacific. This improves also the subsurface currents at the Equator. The global systems give an accurate description of water masses almost everywhere. Between 0 and 500 m, departures from in situ observations rarely exceed 1 • C and 0.2 psu. The assimilation of an improved sea surface temperature product aims to better represent the sea ice concentration and the sea ice edge. The systems under development are still suffering from a drift which can only be detected by means of a 5-yr hindcast, preventing us from upgrading them in real time. This emphasizes the need to pursue research while building future systems for MyOcean2 forecasting.

Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4• -resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sealevel anomaly and sea-surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D-Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open-water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

[1] A 1993-1996 hindcast of the North Atlantic circulation is presented and analyzed. Multivariate satellite observations (sea surface height and sea surface temperature) are assimilated into an isopycnic coordinate ocean model (Miami Isopycnic Coordinate Ocean Model [MICOM]) using a reduced order Kalman filter (the SEEK filter). The objective is to validate the assimilation system in hindcast mode before running a near real-time exercise as part of the European DIADEM project. The analysis scheme is upgraded to make the statistical parameterization as realistic as possible, and this is shown to be particularly important as soon as a multivariate data set is used. Second, the paper describes the modifications to the SEEK filter that are necessary to meet the requirements of a layered model and to benefit from the advantages of the isopycnic coordinate. In particular, the need for an initialization procedure is explained, and this dynamical adjustment is shown to be more natural and robust in the context of the isopycnic coordinate. The hindcast experiment is validated by comparisons with observations, by studying innovation statistics, and by comparing the results with independent observations (expendable bathythermograph [XBT] profiles).INDEX TERMS: 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation; 4263 Oceanography: General: Ocean prediction; 4532 Oceanography: Physical: General circulation; 4520 Oceanography: Physical: Eddies and mesoscale processes; KEYWORDS: data assimilation, ocean model, Atlantic Ocean circulation Citation: Brankart, J.-M., C.-E. Testut, P. Brasseur, and J. Verron, Implementation of a multivariate data assimilation scheme for isopycnic coordinate ocean

To cite this version:Charles-Emmanuel Testut, Pierre Brasseur, Jean-Michel Brankart, Jacques Verron. Assimilation of sea-surface temperature and altimetric observations during 1992-1993 into an eddy-permitting primitive equation model Sea-surface temperature (SST) and sea-surface height (SSH) observations collected from space between October 1992 and December 1993 have been assimilated into a realistic primitive equation model of the North Atlantic Ocean circulation at eddy permitting resolution. The assimilated SST data originate from AVHRR observations gathered and processed within the NASA Pathfinder project; the altimetric data consist of SSH maps computed as the sum of a time-invariant dynamic topography and gridded sea-level anomalies obtained by combining Topex/Poseidon and ERS altimeter data. The assimilation scheme is a reduced-rank Kalman filter derived from the Singular Evolutive Extended Kalman (SEEK) methodology [J. Mar. Syst. 16 (1998) 323], in which the error statistics is represented in a subspace of small dimension. The error subspace is initialized with a truncated series of Empirical Orthogonal Functions (EOFs) of the system variability. The analysis algorithm includes a mechanism to update the forecast error statistics adaptively using all pertinent informations from the innovation vector. Hindcast experiments have been conducted with a 1/3j model of the North Atlantic basin forced with ECMWF atmospheric reanalyses. The impact of the data assimilated during 1993 is assessed by examining how observed (SSH and SST) and nonobserved variables (such as velocity and thermohaline properties in the interior of the ocean) are modified by the assimilation scheme. Finally, the validation of the hindcast experiments with independent XBT measurements is performed in order to evaluate the objective skill of the procedure. The various diagnostics demonstrate the positive impact of the satellite data to hindcast the upper ocean circulation at eddy permitting resolution and the capacity of the scheme to estimate the geographic distribution of the forecast error.

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SUMMARYDuring the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics.The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Système d'Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using 'altimetry-only' or 'multi-data' set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm.Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.

In the Kalman filter standard algorithm, the computational complexity of the observational update is proportional to the cube of the number y of observations (leading behavior for large y). In realistic atmospheric or oceanic applications, involving an increasing quantity of available observations, this often leads to a prohibitive cost and to the necessity of simplifying the problem by aggregating or dropping observations. If the filter error covariance matrices are in square root form, as in square root or ensemble Kalman filters, the standard algorithm can be transformed to be linear in y, providing that the observation error covariance matrix is diagonal. This is a significant drawback of this transformed algorithm and often leads to an assumption of uncorrelated observation errors for the sake of numerical efficiency. In this paper, it is shown that the linearity of the transformed algorithm in y can be preserved for other forms of the observation error covariance matrix. In particular, quite general correlation structures (with analytic asymptotic expressions) can be simulated simply by augmenting the observation vector with differences of the original observations, such as their discrete gradients. Errors in ocean altimetric observations are spatially correlated, as for instance orbit or atmospheric errors along the satellite track. Adequately parameterizing these correlations can directly improve the quality of observational updates and the accuracy of the associated error estimates. In this paper, the example of the North Brazil Current circulation is used to demonstrate the importance of this effect, which is especially significant in that region of moderate ratio between signal amplitude and observation noise, and to show that the efficient parameterization that is proposed for the observation error correlations is appropriate to take it into account. Adding explicit gradient observations also receives a physical justification. This parameterization is thus proved to be useful to ocean data assimilation systems that are based on square root or ensemble Kalman filters, as soon as the number of observations becomes penalizing, and if a sophisticated parameterization of the observation error correlations is required. * Current affiliation: MERCATOR-Ocean,