Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

TL;DR

This paper introduces a novel quadrature-based framework using linear programming and entropy-inspired cost functions to efficiently represent complex aerosol distributions and their cloud condensation nuclei activity in large-scale models.

Contribution

It presents a new maximum-entropy quadrature method that accurately captures aerosol properties without relying on fixed size bins or distribution assumptions.

Findings

Accurately reproduces CCN activity in complex aerosol distributions.

Provides bounds on aerosol properties like CCN number concentration.

Offers a flexible, non-parametric approach to aerosol representation.

Abstract

Sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particle-resolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosol properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.