Advanced data-assimilation methods for satellite observations

1. Advanced data-assimilation methods for satellite observations Data-Assimilation Research Centre DARC University of Reading ESA Advanced Data Assimilation project July 2012

2. Overview • Task 1: Data-assimilation methods for nonlinear non-Gaussian and multi-scale problems • Task 2: Quantifying and representing uncertainty in models and observations at multiple scales • Task 3: Exploration of advanced data-assimilation schemes to retrieve new snow products

3. The Equivalent-Weights Particle Filter • Use simple proposal at each time step, e.g. relaxation to observations. • Use different proposal at final time step to ensure that weights are very similar. y y t=0 t=50 t=100

4. Balance preservation • Geophysical flows exhibit certain relations between variables called balance relation • Examples are geostrophic balance and hydrostatic balance • It is crucial that the data-assimilation method retains these balances to a large extend to avoid strongly unbalanced states, like strong gravity waves • This is studied here in an ocean model

5. Quality of the ensemble: ensemble mean

6. Quality of the ensemble: rank histogram

7. How a-geostrophic is the flow?

8. Energy spectra Unforced stochastic model After Particle filter update

9. Energy in unbalanced modes

10. Conclusions • Equivalent-weights particle filter performs well for the ocean model. • The scheme does not introduce gravity wave energy beyond what the stochastic forcing does. • Gravity-wave energy varies substantially over the particles, suggesting that underlying state and random effects are important • Present work: sensitivity to observation strategy (WP 1.2)