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DDM- Data Assimilation and Optimisation

Data assimilation deals with the problem of estimating model parameters or model state variables by combining prior estimates and observations, together with information about their uncertainties, in a statistically optimal manner. CERFACS has well-established data assimilation activities in oceanography, atmospheric chemistry, hydrology/hydraulics and wildfires, which are conducted in close collaboration with CERFACS partners, and leading operational centres and research institutes in forecasting and retrospective analysis.

Optimization concerns the development of numerical algorithms for solving diverse linear and nonlinear problems. It involves studying the properties of the algorithms, such as convergence and complexity, as well as improving their performance through preconditioning techniques. CERFACS develops derivative-based and derivative-free algorithms for a variety of applications including data assimilation, Earth imaging and aerodynamics design.

Accuracy, efficiency, scalability, robustness and practicability are important considerations when designing methods for data assimilation and optimization. To develop effective methods requires a good understanding of the underlying application and problem characteristics.

The current actions in Data Assimilation at CERFACS are organized in actions :

  • Develop advanced algorithms for variational and ensemble-variational DA
  • Develop advanced methods for modelling and estimating error covariances
  • Develop ensemble generation and surrogate model methodologies for DA
  • Improve and extend the use of in situ and remotely-sensed data in DA

These actions are implemented on Use Cases

  • Use Case #1: Extending methodologies in variational and ensemble-variational DA: Accounting for nonlinearity; model error estimation & representation; preconditioning; developing robust minimization for extreme-valued observations. Implementation & evaluation in OOPS-based and other systems.
  • Use Case #2: Development of flexible and efficient covariance models for B: multi-variate; accounting for rotational anisotropy in correlations; multi-scale; ensembles; localization; SPDEs; preconditioning; hybrid; coupled; estimation procedures; MLMC. Implementation & evaluation in OOPS-based systems including NEMOVAR for ocean DA, and other systems.
  • Use Case #3: Development of flexible and efficient covariance models for R: unstructured meshes; SPDEs; FEMs; 2D, 3D and 4D (space + inter-channel + time); R-1; preconditioning; estimation procedures. Implementation & evaluation in existing Matlab framework and OOPS-based systems.
  • Use Case #4: Development of efficient ensemble-based DA: Ensemble generation & validation methodologies; surrogate models for stochastic covariance estimation. Development for hydraulics, wildfire & pollutant dispersion.
  • Use Case #5: Assimilation of satellite and image data: high-resolution altimeter (SWOT) and SST; high-resolution & hyper-spectral sounders (IASI); patterns/fronts. Developments for hydraulics & hydrology, ocean, atmospheric chemistry, history matching and wildfire applications.
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NEWS

A bronze medal to Cerfacs for “Allons-Y À Vélo” !

CERFACS |  26 July 2021

A bronze medal to Cerfacs for "Allons-Y À Vélo" ! Cerfacs is on the podium for its cycling regularity during the "Allons-Y À Vélo" period (May 25 - June 25) in its category (100-500 employees).During this period, 37% of Cerfacs employees came to work by bike during at least 4 consecutive days. "Allons-Y À Vélo" is a campaign organised by the association "2 Pieds 2 Roues" and "La Maison du Vélo de Toulouse" to promote the use of the bicycle for every day travels; all details can be found on allonsyavelo.le-pic.org . Cerfacs encourages its employees to use their bicycle to come to work as they benefit from the bicycle mileage allowance (FMD for Forfait Mobilité Durable) of up to 500 Euros per year.Read more


The H2OPE project wins the prize Joseph Fourier 2021

CERFACS |  26 July 2021

The H2OPE project wins the prize Joseph Fourier 2021 The prize, launched by Atos and GENCI, aims to reward the work of researchers, academics and industry in two strategic areas: Advanced Computing (HPC, Quantum, Edge) and Artificial Intelligence, and, in the 2021 edition , gives particular importance to Decarbonation. The 1st Prize was awarded to the H2OPE or “H2OPErability for safe and clean gas turbine engines” project from CERFACS in Toulouse. This project aims, via the LES AVBP code, to model at high resolution a mixed combustion process associating conventional fuels with hydrogen (bi-fuels) as one of the most promising technical solutions to achieve "net zero emissions" of CO2 in the industrial sector. It was presented by Walter Agostinelli, Davide Laera, Laurent Gicquel, and Thierry Poinsot. Press release :Read more

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