A Revised Scheme to Compute Horizontal Covariances in an Oceanographic 3D-VAR Assimilation System.

We propose an improvement of an oceanographic three dimensional variational assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter (RF) with the third order of accuracy (3rd-RF), instead of an RFwith first order of accuracy (1st-RF), to approximate horizontal Gaussian covariances. An advantage of the proposed scheme is that the CPU's time can be substantially reduced with benefits on the large scale applications. Experiments estimating the impact of 3rd-RF are performed by assimilating oceanographic data in two realistic oceanographic applications. The results evince benefits in terms of assimilation process computational time, accuracy of the Gaussian correlation modeling, and show that the 3rd-RF is a suitable tool for operational data assimilation

An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project

A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state

Detecting unstable structures and controlling error growth by assimilation of standard and adaptive observations in a primitive equation ocean model

International audienceOceanic and atmospheric prediction is based on cyclic analysis-forecast systems that assimilate new observations as they become available. In such observationally forced systems, errors amplify depending on their components along the unstable directions; these can be estimated by Breeding on the Data Assimilation System (BDAS). Assimilation in the Unstable Subspace (AUS) uses the available observations to estimate the amplitude of the unstable structures (computed by BDAS), present in the forecast error field, in order to eliminate them and to control the error growth. For this purpose, it is crucial that the observational network can detect the unstable structures that are active in the system. These concepts are demonstrated here by twin experiments with a large state dimension, primitive equation ocean model and an observational network having a fixed and an adaptive component. The latter consists of observations taken each time at different locations, chosen to target the estimated instabilities, whose positions and features depend on the dynamical characteristics of the flow. The adaptive placement and the dynamically consistent assimilation of observations (both relying upon the estimate of the unstable directions of the data-forced system), allow to obtain a remarkable reduction of errors with respect to a non-adaptive setting. The space distribution of the positions chosen for the observations allows to characterize the evolution of instabilities, from deep layers in western boundary current regions, to near-surface layers in the eastward jet area

Integration of ARGO trajectories in the Mediterranean Forecasting System and impact on the regional analysis of the Western Mediterranean circulation

The impact of ARGO float trajectory assimilation on the quality of ocean analyses is studied by means of an operational oceanographic model implemented in the Mediterranean Sea and a 3D-var assimilation scheme. For the first time, both ARGO trajectories and vertical profiles of temperature and salinity (TS) together with satellite altimeter data of sea level anomaly (SLA) are assimilated to produce analyses for short term forecasts. The study period covers three months during winter 2005 when four ARGO trajectories were present in the northwestern Mediterranean Sea. The scheme is first assessed computing the misfits between observations and model forecast and analysis. The misfit statistics appear improved for float trajectories, while they are not degraded for the other assimilated variables (TS profiles and SLA). This indicates that the trajectory integration is consistent with the other components of the assimilation system, and provides new information on horizontal pressure gradients. Comparisons between analyses obtained with and without trajectory assimilation suggest that trajectory assimilation can impact on the description of boundary currents and their instabilities, as well as mesoscale activity at regional scales. Changes are depicted by intermediate water mass redistributions, mesoscale eddy relocations and net transport modulations. These impacts are detailed and assessed considering historical and simultaneous in-situ datasets. The results motivate the integration of ARGO trajectories in the operational Mediterranean Forecasting System

Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHAs); depth profiles of temperature and salinity from Argo floats, autonomous Seagliders, and shipboard conductivity–temperature–depth (CTD); and surface velocity measurements from high-frequency radar (HFR). The performance of the state estimate is examined against a forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.</p

An experimental 2D-Var retrieval using AMSR2

A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels\u27 sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2D-Var for cloud-free retrievals and opens the possibility of stand-alone 3D-Var retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution

Ensemble perturbation smoother for optimizing tidal boundary conditions by assimilation of High-Frequency radar surface currents - application to the German Bight

High-Frequency (HF) radars measure the ocean surface currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each boundary condition perturbation. This cost function ensures that the boundary condition perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM). Optimized boundary values are obtained by assimilating all observations using the co-variances of the ensemble simulation

Reconstructing the recent past ocean variability: Status and perspective

In the last two decades, climate research has benefited from the continuous development of analysis systems dedicated to operational monitoring and forecasting, which opened up the possibility of exploiting the state of the art modeling and data assimilation tools to reconstruct and study the ocean during the past decades. This activity became feasible also thanks to the increasing availability of long time series of high-quality in situ and remotely-sensed observations. Retrospective analyses (or simply reanalyses or ocean syntheses), indeed combine quality controlled reprocessed ocean observations with a state-of-the-art ocean general circulation model (OGCM) using data assimilation methods to estimate the time-varying, three-dimensional state of the ocean. Ocean reanalyses benefit from data assimilation algorithms that are usually inherited from operational oceanography, although they require specific treatment in order to avoid spurious drifts stemming from instrumental or model biases. Unlike observation-only products, ocean reanalyses take advantage of time-varying atmospheric forcing, usually coming from an atmospheric reanalysis, and dynamical and physical balances implied by the OGCM. Here we give an excursus on the availability of global and regional ocean reanalyses, their applications, their strengths and weaknesses, and their future developments foreseen at the present time