Many applications of atmospheric composition and climate data involve the comparison or combination of vertically resolved atmospheric state variables. Calculating differences and combining data require harmonization of data representations in terms of physical quantities and vertical sampling at least. If one or both datasets result from a retrieval process, knowledge of prior information and averaging kernel matrices in principle allows retrieval differences to be accounted for as well. Spatiotemporal mismatch of the sensed air masses and its contribution to the data discrepancies can be estimated with chemistry transport modeling support. In this work an overview of harmonization or matching operations for atmospheric profile observations is provided. The effect of these manipulations on the information content of the original data and on the uncertainty budget of data comparisons is examined and discussed.