Research

Work packages

Each Principle Investigator (PI) will contribute to all the Work Packages (WPs) in a synergetic collaboration; the WPs cannot be completed without applying the expertise of the entire team. The PIs that will lead each WP are identified below. Postdoctoral Research Assistants and PhD students will also work across the project on multiple WPs. View current opportunities to join the team >>

Work packages

Work package 1 (WP1)

Multi-modal ocean data acquisition, analysis and interpretation

Principle investigators
  • Bertrand Chapron
  • Darryl Holm

Work package 2 (WP2)

Uncertainty representation and stochastic parameterization

Principle investigators
  • Darryl Holm
  • Dan Crisan
  • Etienne Mémin

Work package 3 (WP3)

Numerical schemes for stochastic fluid transport and diffusion

Principle investigators
  • Etienne Mémin
  • Darryl Holm

Work package 4 (WP4)

Multiscale ensemble Data Assimilation and forecasting methods

Principle investigators
  • Dan Crisan
  • Etienne Mémin

A Venn diagram showing how WP2 (Uncertainty representation and stochastic parameterization), WP3 (Numerical schemes for stochastic fluid transport and diffusion) and WP4 (Multiscale ensemble Data Assimilation and forecasting methods) overlap to make WP1 (Observations)

An iterative sequence of goals

The work packages detailed above are organised to achieve the following iterative sequence of goals:

  1. Compile and interpret unique high-resolution IFREMER’s satellite, float and drifter datasets and high resolution numerical simulation (WP1)
  2. Extract the statistical properties from the datasets needed for the mathematical model to separate the velocities of the ocean currents into coarse-scale drift and fine-scale noise (WP1+WP2)
  3. Apply the statistics of the fine-scale noise in our new class of mathematical models to derive rigorous equations for the coarse-scale drift velocities in a set of realistic exemplars, discussed below (WP2)
  4. Create an ensemble of computer simulations for each exemplar at coarse resolution with the corresponding new stochastic equations calibrated from the statistical properties of the satellite observations (WP2+WP3)
  5. Apply newly developed data assimilation methods to this ensemble of simulations to find the most likely solutions among them for the currents and waves, along with their uncertainty and expected variability (WP4)
  6. Compare the results with further Satellite Observations (WP1)
  7. Design and make improvements in steps (1) and (2) to better formulate the observed statistics (all WPs)
  8. Go to step (3), and complete the cycle again until satisfactory convergence has been achieved.