  The controllability of advection-diffusion systems, subject to uncertain
initial values and emission rates, is estimated, given sparse and error
affected observations of prognostic state variables. In predictive geophysical
model systems, like atmospheric chemistry simulations, different parameter
families influence the temporal evolution of the system.This renders
initial-value-only optimisation by traditional data assimilation methods as
insufficient. In this paper, a quantitative assessment method on validation of
measurement configurations to optimize initial values and emission rates, and
how to balance them, is introduced. In this theoretical approach, Kalman filter
and smoother and their ensemble based versions are combined with a singular
value decomposition, to evaluate the potential improvement associated with
specific observational network configurations. Further, with the same singular
vector analysis for the efficiency of observations, their sensitivity to model
control can be identified by determining the direction and strength of maximum
perturbation in a finite-time interval.
