Traitement des Incertitudes en Océanographie

Ce mémoire aborde le problème général du traitement des incertitudes en océanographie, en le considérant de façon transverse, tant dans le cadre du problème direct (modélisation des océans) que du problème inverse (méthodes d'assimilation de données). Les questions abordées sont en particulier les méthodes de simulations d'ensemble, avec paramétrisation stochastiques des incertitudes, et les méthodes de réduction des incertitudes par assimilation données (sous hypothèse gaussienne et non-gaussienne)

Comparative assimilation of Topex/poseidon and ERS altimetric data and of TAO temperature data in the tropical Pacific Ocean during 1994-1998, and the mean sea-surface height issue

International audienceFive years of Topex/Poseidon (T/P) and ERS sea level anomaly (SLA) data (1994–1998) are assimilated every 10 days into a primitive equation model of the tropical Pacific ocean. The data assimilation technique used here is a reduced-order Kalman filter derived from the Singular Evolutive Extended Kalman (SEEK) filter [J. Mar. Syst. 16(3–4) (1998) 323] with an error covariance matrix parameterised by a subset of multivariate 3D global empirical orthogonal functions (EOFs). The assimilation run is compared to the free run and to independent data from the TAO network. The impact of sea-surface height (SSH) assimilation on surface and subsurface temperature and currents is estimated in the equatorial band. In a second stage, temperature data from the TAO array are assimilated in the same conditions as in the first stage. The comparison between the results of the two assimilation experiments is made mainly with a view to gaining insights into the mean sea-surface height (MSSH) for the assimilation of altimeter data, and more generally, into the question of biases. XBT observations and TAO array data are then used to build a physically more consistent mean sea-surface height for assimilation of SLA data. Results from the assimilation of altimeter data referenced to this new MSSH show significant improvements

Toward a data assimilation system for NEMO

International audienceIn this note, we discuss the project that has been conceived and the first achievement steps that have been carried out to set up a data assimila-­ tion system associated to NEMO. Of specific interest here are applications to operational oceanography. This data assimilation system is sche-­ matically made of three subcomponents: Interface Components, Built-in Components and External Components. Several elements of this NEMO data assimilation system have already been developed by various groups in France and in Europe and several of them could be introduced in the system (the linear Tangent and Adjoint Model, TAM, is one of the most important of them as far as variational assimilation is concerned), some others will require specific developments. Finally, we introduce the SEABASS reference configuration that is proposed to be the NEMO data as-­ similation demonstrator and the experimentation and training platform for data assimilation activities with NEMO. These various thoughts take advantage of the advances and discussions that have been carried out by the NEMOASSIM working group

An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments

International audienceA cross-validation algorithm is developed to perform probabilistic observing system simulation experiments (OSSEs). The use of a probability distribution of ''true'' states is considered rather than a single ''truth'' using a cross-validation algorithm in which each member of an ensemble simulation is alternatively used as the ''truth'' and to simulate synthetic observation data that reflect the observing system to be evaluated. The other available members are used to produce an updated ensemble by assimilating the specific data, while a probabilistic evaluation of the observation impacts is obtained using a comprehensive set of verification skill scores. To showcase this new type of OSSE studies with tractable numerical costs, a simple biogeochemical application under the Horizon 2020 AtlantOS project is presented for a single assimilation time step, in order to investigate the value of adding biogeochemical (BGC)-Argo floats to the existing satellite ocean color observations. Further experiments must be performed in time as well for a rigorous and effective evaluation of the BGC-Argo network design, though some evidence from this preliminary work suggests that assimilating chlorophyll data from a BGC-Argo array of 1000 floats can provide additional error reduction at the surface, where the use of spatial ocean color data is limited (due to cloudy conditions), as well at depths ranging from 50 to 150 m

Assimilation of ARGO temperature profile, sea surface temperature and altimetric satellite data into an eddy permitting primitive equation model of the North Atlantic Ocean

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Using a SEEK filter as data assimilation kernel in NEMOVAR inner loops

Data assimilation (DA) is divided in two main branches: (i) variational assimilation and (ii) stochastic assimilation. Unlike variational assimilation, stochastic assimilation does not rely on a functional minimization with iterative process and gradient estimations. The stochastic branch includes Kalman Filter-type approaches and low rank approximations such as SEEK Filter. NEMOVAR is designed for variational applications, however its two stages inner-outer loops allow for different assimilation kernel in the inner loop. The present report study the use of a SEEK filter within NEMOVAR