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Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> New-particle formation from condensable vapors is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and impacts. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular models and experimental studies. Such data can be implemented in large-scale climate and air quality models as parameterizations or look-up tables. Models benchmarked against measurement data provide a straight-forward means to assess formation rates over a wide range of atmospheric conditions for given chemical compounds. Ideally, the implementation of such formation rate data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate look-up tables of formation rates for user-defined input parameters. The table generator routine applies a molecular cluster dynamics model with quantum chemistry input, but other types of particle formation models may be used as well. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing straight-forward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters.