Singular vector decomposition for sensitivity analyses of tropospheric chemical scenarios
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. One possibility for optimisation of the state estimate is to target the observation of those parameters that have the largest potential of resulting in forecast improvements. In the present work, the technique of singular vector analysis is applied to atmospheric chemical modelling in order to identify the most sensitive chemical compounds during a given time period and prioritise them for measurement. Novel to the current work is the fact that, in the application of singular vector analysis, not only the initial values but also the emissions are considered as target variables for adaptive observation strategies. This specific application of singular vector analysis is studied in the context of a chemistry box model allowing for validation of its new features for two chemical regimes. The time and regime dependence of the ozone (O 3) and peroxyacetyl nitrate (PAN) formation potential of individual volatile organic compounds (VOCs) is investigated. Results show that the combined sensitivity of O 3 and PAN to individual VOCs is strongly dependent on the photochemical scenario and simulation interval used. Particularly the alkanes show increasing sensitivities with increasing simulation length. Classifying the VOCs as being of high, medium, little or negligible importance for the formation of O 3 and PAN allows for the identification of those VOCs that may be omitted from measurement. We find that it is possible to omit 6 out of 18 VOCs considered for initial value measurement and 4 out of 12 VOCs considered for emission measurement. The omission of these VOCs is independent of photochemical regime and simulation length. The VOCs selected for measuring account for more than 96% and 90% of the O 3 and PAN sensitivity to VOCs, respectively.