1. Nonlinear high-dimensional data assimilation
Fully nonlinear data-assimilation are needed to get better predictions in high-dimensional environmental models (atmosphere, ocean etc.).
However, the high dimension makes this very difficult
NCEO is world-leading in this field and has made very good progress using so-called particle filters, giving NCEO a unique capability.

2. uncertainty in whole ensemble of model runs
Particle filter
x observation
individual model run
uncertainty in whole ensemble of model runs x x
time
Note the growing uncertainty between observations, and the
contraction of the ensemble of model runs at observation times.

3. Standard particle filter
Truth
Standard particle filter
Vorticity field evolution showing interacting eddies
Left : Truth
Right: ensemble mean of standard method, with
data assimilation every 50 time steps.
Results are not very good.

4. New Particle filter
Truth
Vorticity field evolution showing interacting eddies
Left : Truth
Right: ensemble mean of new method, with
data assimilation every 50 time steps
Results are much better!

5. Future
The new method is now implemented and being tested in several high-dimensional environmental models, such as an ocean model and a climate model.