Abstract. The Degree of Freedom for Signal (DFS) is generalized and applied to estimate the potential observability of observation networks for augmented model state and parameter estimations. The control of predictive geophysical model systems by measurements is dependent on a sufficient observational basis. Control parameters may include prognostic state variables, mostly the initial values, and insufficiently known model parameters, to which the simulation is sensitive. As for chemistry-transport models, emission rates are at least as important as initial values for model evolution control. Extending the optimisation parameter set must be met by observation networks, which allows for controlling the entire optimisation task. In this paper, we introduce a DFS based approach with respect to address both, emission rates and initial value observability. By applying a Kalman smoother, a quantitative assessment method on the efficiency of observation configurations is developed based on the singular value decomposition. For practical reasons an ensemble based version is derived for covariance modelling. The observability analysis tool can be generalized to additional model parameters.
How to cite. Wu, X., Elbern, H., and Jacob, B.: The degree of freedom for signal assessment of measurement networks for joint chemical state and emission analysis, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2017-220, 2017.
It is novel that the tangent linear form of the atmospheric transport model was extended by emissions under the assumption that emissions preserve the invariant diurnal profiles. Base on the Kalman smoother, the degree of freedom for signal and several metrics is derived as the criterion to evaluate the potential improvement of model states. Besides, sensitivities of observations was formulated by seeking the fastest directions of the perturbation ratio between initial states and observations.
It is novel that the tangent linear form of the atmospheric transport model was extended by...