Project-Team Clime

Scientific topics

• Topic 1: State estimation: analysis and forecast

  • Non-Gaussian aspects of data assimilation
  • Multiscale data assimilation
  • Accounting for sampling errors in the ensemble Kalman filter
  • New ensemble variational methods
  • Minimax filter
  • Air quality at urban scale

  Urban air quality simulation

  Firespread model evaluation based on a large observation data set

  An iterative ensemble Kalman smoother

• Topic 2: Inverse modeling

  • Network design in air quality and accidental dispersion
  • Error and hyperparameter estimation in inverse modeling
  • Localization of the source term of accidental releases
  • Quantifying the representativeness error

  Reconstruction of tropospheric emissions

  Some theoretical aspects of source and parameter estimation

• Topic 3: Image assimilation

  • State estimation for image processing problems
  • Model reduction applied to image sequences
  • Assessing model error
  • Non linear observation operators
  • Designing ensembles from image data

  Modeling ocean surface circulation

  Overview of Image Assimilation

  Optimal Bases for Motion Representation

  Which Observational Data for Estimating Surface Currents
  

• Topic 4: Uncertainty quantification and risk assessment

  • Ensemble forecasting
  • Uncertainty quantification
  • Model reduction and emulation

  Ensemble Forecast of Analyses With Uncertainty Estimation

  Statistical emulation of a static air quality model at urban scale