2016/12/19 by Laura Fierce, Robert L. McGraw · 8 citations
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Aerosol #Air Quality and Health Impacts #Atmospheric aerosols and clouds #Atmospheric chemistry and aerosols #Cloud condensation nuclei #Condensation #Multivariate statistics #Particle (ecology) #Particle number #Probability density function #Probability distribution #Tracking (education) #physics.ao-ph
paper · pdf · doi:10.1002/2016jd026335
published in Journal of Geophysical Research Atmospheres 122(18), 9867-9878 (American Geophysical Union)
arxiv created 2016/12/19 · openalex publication_date 2017/07/27 · openalex created_date 2017/08/08 · arxiv updated 2018/03/14 · openalex updated_date 2026/08/05
Abstract Atmospheric aerosol is composed of distinct multicomponent particles that are continuously modified as they are transported in the atmosphere. Resolving variability in particle physical and chemical properties requires tracking high‐dimensional probability density functions, which is not practical in large‐scale atmospheric simulations. Reduced representations of atmospheric aerosol are needed for efficient regional‐ and global‐scale chemical transport models. Although the aerosol size‐composition distribution is described by a high‐dimensional probability density function, here we show that cloud condensation nuclei activity of aerosol populations can be represented with high accuracy using an optimized set of representative particles. The sparse representation of the aerosol mixing state, designed for use in quadrature‐based moment models, is constructed from a linear program that is combined with an entropy‐inspired cost function. Unlike reduced representations common to large‐scale atmospheric models, such as modal and sectional schemes, the maximum‐entropy approach described here is not confined to predetermined size bins or assumed distribution shapes. This study is a first step toward a quadrature‐based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large‐scale simulations.