Covariance Localization in
Strongly Coupled Data Assimilation
2 August, 1pm, in 2890
Coupled models of the Earth system have now enabled numerical
prediction from weather time scales to climate projections. Strongly
coupled data assimilation (DA) based on an ensemble of forecasts is a
promising approach for providing initial conditions for these coupled
models due to their ability to estimate flow-dependent coupled error
covariance. Because the coupling strength between subsystems of the
Earth is not a simple function of a distance, we need a better
localization strategy than the current distance-dependent localization.
We first propose the correlation-cutoff method, where localization of
strongly coupled DA is guided by ensemble correlations of an offline DA
cycle, so that, for example, an atmospheric observation will be
assimilated into an ocean location only if the variables at the two
locations have been determined to have significantly correlated errors.
The method improves the analysis accuracy when tested with a simple
coupled model of atmosphere and ocean. We then extend the
correlation-cutoff method to a global atmosphere-ocean strongly coupled
DA with neural networks. The combination of static information provided
by the neural networks and flow-dependent error covariance estimated by
the ensemble improves the atmospheric analysis in our observation
system simulation experiment. The neural networks can reproduce the
global error statistics reasonably well, and their computational cost
in a DA system is reasonable.
As a related topic, error growth and predictability of a coupled
dynamical system with multiple timescales are explored with a simple
coupled atmosphere-ocean model. The attractor is found to have a
discontinuous response to the strength of the coupling.