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The controllability of advection-diffusion systems, subject to uncertaininitial values and emission rates, is estimated, given sparse and erroraffected observations of prognostic state variables. In predictive geophysicalmodel systems, like atmospheric chemistry simulations, different parameterfamilies influence the temporal evolution of the system.This rendersinitial-value-only optimisation by traditional data assimilation methods asinsufficient. In this paper, a quantitative assessment method on validation ofmeasurement configurations to optimize initial values and emission rates, andhow to balance them, is introduced. In this theoretical approach, Kalman filterand smoother and their ensemble based versions are combined with a singularvalue decomposition, to evaluate the potential improvement associated withspecific observational network configurations. Further, with the same singularvector analysis for the efficiency of observations, their sensitivity to modelcontrol can be identified by determining the direction and strength of maximumperturbation in a finite-time interval.

Efficiency and Sensitivity Analysis of Observation Networks for Atmospheric Inverse Modelling with Emissions