Dynamical evolution of the error statistics with the SEEK filter to assimilate altimetric data in eddy-resolving ocean models

ISSN 0035 - 9009International audienceThe Singular Evolutive Extended Kalman (SEEK) filter introduced by Pham et al. is applied to a primitive‐equation model in order to reconstruct the mesoscale circulation typical of the mid‐latitude ocean from altimetric data. The SEEK filter is a variant of the Kalman‐filter algorithm based on two concepts: the order reduction of the initial‐error covariance matrix, and the dynamical evolution of the reduced‐order basis. This makes the method potentially suitable for problems with a high number of degrees of freedom.Previous work has shown the ability of a steady version of the filter to improve the vertical structure of the ocean thermocline in the case of the quasi‐linear dynamics associated with the equatorial tropical Pacific Ocean, and the need to combine the dynamical evolution of the basis with an adaptive scheme in a mid‐latitude ocean model of the Gulf Stream region.This work examines the potential advantages of the dynamical evolution of the basis functions with simple assimilation experiments. It demonstrates the ability of the method to propagate in time the statistical properties of the system when the filter is initialized properly. However, the lack of robustness of the filter is investigated theoretically and experimentally, showing the need to consider variants of the method when the filter is not properly initialized

Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis

In biogeochemical models coupled to ocean circulation models, vertical mixing is an important physical process which governs the nutrient supply and the plankton residence in the euphotic layer. However, vertical mixing is often poorly represented in numerical simulations because of approximate parameterizations of sub-grid scale turbulence, wind forcing errors and other mis-represented processes such as restratification by mesoscale eddies. Getting a sufficient knowledge of the nature and structure of these errors is necessary to implement appropriate data assimilation methods and to evaluate if they can be controlled by a given observation system. <br><br> In this paper, Monte Carlo simulations are conducted to study mixing errors induced by approximate wind forcings in a three-dimensional coupled physical-biogeochemical model of the North Atlantic with a 1/4° horizontal resolution. An ensemble forecast involving 200 members is performed during the 1998 spring bloom, by prescribing perturbations of the wind forcing to generate mixing errors. The biogeochemical response is shown to be rather complex because of nonlinearities and threshold effects in the coupled model. The response of the surface phytoplankton depends on the region of interest and is particularly sensitive to the local stratification. In addition, the statistical relationships computed between the various physical and biogeochemical variables reflect the signature of the non-Gaussian behaviour of the system. It is shown that significant information on the ecosystem can be retrieved from observations of chlorophyll concentration or sea surface temperature if a simple nonlinear change of variables (anamorphosis) is performed by mapping separately and locally the ensemble percentiles of the distributions of each state variable on the Gaussian percentiles. The results of idealized observational updates (performed with perfect observations and neglecting horizontal correlations) indicate that the implementation of this anamorphosis method into sequential assimilation schemes can substantially improve the accuracy of the estimation with respect to classical computations based on the Gaussian assumption

Implementation of Non-Destructive Evaluation and Process Monitoring in DLP-based Additive ManufacturingAbstract

This article describes a method of in-situ process monitoring in the digital light processing (DLP) 3D printer. It is based on the continuous measurement of the adhesion force between printing surface and bottom of a liquid resin bath. This method is suitable only for the bottom-up DPL printers. Control system compares the force at the moment of unsticking of printed layer from the bottom of the tank, when it has the largest value in printing cycle, with theoretical value. Implementation of suggested algorithm can make detection of faults during the printing process possible

Interactions between the Somali Current eddies during the summer monsoon: insights from a numerical study

International audienceThree hindcast simulations of the global ocean circulation differing by resolution (1/4 or 1/12°) or parametrization or atmospheric forcing are used to describe the interactions between the large anticyclonic eddies generated by the Somali Current system during the Southwest Monsoon. The present investigation of the Somalian coherent eddy structures allows us to identify the origin and the subsequent development of the cyclones flanked upon the Great Whirl (GW) previously identified by Beal and Dono-hue (2013) in satellite observations and to establish that similar cyclones are also flanked upon the Southern Gyre (SG). These cyclones are identified as potential actors in mixing water masses within the large eddies and offshore the coast of Somalia. All three simulations bring to light that during the period when the Southwest Monsoon is well established, the SG moves northward along the Somali coast and encounters the GW. The interaction between the SG and the GW is a collision without merging, in a way that has not been described in observations up to now. During the collision the GW is pushed to the east of Socotra Island, sheds several smaller patches of anticyclonic vorticity, and often reforms into the Socotra Eddy, thus proposing a formation mechanism for that eddy. During this process the GW gives up its place to the SG. This process is robust throughout the three simulations

Optimal adjustment of the atmospheric forcing parameters of ocean models using sea surface temperature data assimilation

In ocean general circulation models, near-surface atmospheric variables used to specify the atmospheric boundary condition remain one of the main sources of error. The objective of this research is to constrain the surface forcing function of an ocean model by sea surface temperature (SST) data assimilation. For that purpose, a set of corrections for ERAinterim (hereafter ERAi) reanalysis data is estimated for the period of 1989–2007, using a sequential assimilation method, with ensemble experiments to evaluate the impact of uncertain atmospheric forcing on the ocean state. The control vector of the assimilation method is extended to atmospheric variables to obtain monthly mean parameter corrections by assimilating monthly SST and sea surface salinity (SSS) climatological data in a low resolution global configuration of the NEMO model. In this context, the careful determination of the prior probability distribution of the parameters is an important matter. This paper demonstrates the importance of isolating the impact of forcing errors in the model to perform relevant ensemble experiments. <br><br> The results obtained for every month of the period between 1989 and 2007 show that the estimated parameters produce the same kind of impact on the SST as the analysis itself. The objective is then to evaluate the long-term time series of the forcing parameters focusing on trends and mean error corrections of air–sea fluxes. Our corrections tend to equilibrate the net heat-flux balance at the global scale (highly positive in ERAi database), and to remove the potentially unrealistic negative trend (leading to ocean cooling) in the ERAi net heat flux over the whole time period. More specifically in the intertropical band, we reduce the warm bias of ERAi data by mostly modifying the latent heat flux by wind speed intensification. Consistently, when used to force the model, the corrected parameters lead to a better agreement between the mean SST produced by the model and mean SST observations over the period of 1989–2007 in the intertropical band

Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

International audienceThe diffusive back-and-forth nudging (DBFN) is an easy-to-implement iterative data assimilation method based on the well-known nudging method. It consists of a sequence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore, in the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability, the diffusion terms also have their sign reversed, giving a dif-fusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model non-resolved scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article, the DBFN approximations and their consequences for the data assimilation system setup are analysed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much simpler implementation and a shorter CPU time for convergence. The conducted sensitivity tests show that the 4Dvar profits of long assimilation windows to propagate surface information downwards, and that for the DBFN, it is worth using short assimilation windows to reduce the impact of diffusion-induced errors. Moreover, the DBFN is less sensitive to the first guess than the 4Dvar

Fault detection and isolation of faults in a multivariate process with Bayesian network

The main objective of this paper is to present a new method of detection and isolation with a Bayesian network. For that, a combination of two original works is made. The first one is the work of Li et al. [1] who proposed a causal decomposition of the T2 statistic. The second one is a previous work on the detection of fault with Bayesian networks [2], notably on the modeling of multivariate control charts in a Bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing to decide if a fault has appeared in the process. This structure permits the isolation of the variables implicated in the fault. A particular interest of the method is the fact that the detection and the isolation can be made with a unique tool: a Bayesian network

Pour en finir avec le Bronze final ? Les haches à douille de type armoricain en France

A discussion about socket armorican bronze axes datation. They are from Ha D period (VII th & VIt h century B.C.)Révision de la datation des haches à douille de type armoricain, au seul Hallstatt D (VIIe-VIe s; av. J.-C.