1Institute of Energy and Climate Research, IEK-8, Research Centre Jülich, Jülich, Germany
2Rhenish Institute for Environmental Research at the University of Cologne, Köln, Germany
*now at: Geophysical Institute at the University of Bergen, Bergen, Norway
Abstract. Observations of the chemical state of the atmosphere typically provide only sparse snapshots of the state of the system due to their insufficient temporal and spatial density. Therefore the measurement configurations need to be optimised to get a best possible state estimate. One possibility to optimise the state estimate is provided by observation targeting of sensitive system states, to identify measurement configurations of best value for forecast improvements. In recent years, numerical weather prediction adapted singular vector analysis with respect to initial values as a novel method to identify sensitive states. In the present work, this technique is transferred from meteorological to chemical forecast. Besides initial values, emissions are investigated as controlling variables. More precisely uncertainties in the amplitude of the diurnal profile of emissions are analysed, yielding emission factors as target variables. Singular vector analysis is extended to allow for projected target variables not only at final time but also at initial time. Further, special operators are introduced, which consider the combined influence of groups of chemical species.
As a preparation for targeted observation calculations, the concept of adaptive observations is studied with a chemistry box model. For a set of six different scenarios, the VOC versus NOx limitation of the ozone formation is investigated. Results reveal, that the singular vectors are strongly dependent on start time and length of the simulation. As expected, singular vectors with initial values as target variables tend to be more sensitive to initial values, while emission factors as target variables are more sensitive to simulation length. Further, the particular importance of chemical compounds differs strongly between absolute and relative error growth.