advanced data-assimilation methods for satellite observations

Advanced data-assimilation methods for satellite observations

Data-Assimilation Research Centre DARCUniversity of Reading

ESA Advanced Data Assimilation project July 2012

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

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.

t=0 t=50 t=100

y y

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

Quality of the ensemble: ensemble mean

Quality of the ensemble: rank histogram

How a-geostrophic is the flow?

Energy spectra

Unforced stochastic model After Particle filter update

Energy in unbalanced modes

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)