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Title: Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

Abstract

Here, 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 particleresolved 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.

Authors:
ORCiD logo [1];  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1376182
Report Number(s):
BNL-114165-2017-JA
Journal ID: ISSN 2169-897X; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
Grant/Contract Number:  
SC00112704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Volume: 122; Journal Issue: 18; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; aerosol modeling; cloud condensation nuclei; particle-based modeling; quadrature method of moments

Citation Formats

Fierce, Laura, and McGraw, Robert L. Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations. United States: N. p., 2017. Web. doi:10.1002/2016JD026335.
Fierce, Laura, & McGraw, Robert L. Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations. United States. doi:10.1002/2016JD026335.
Fierce, Laura, and McGraw, Robert L. Wed . "Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations". United States. doi:10.1002/2016JD026335. https://www.osti.gov/servlets/purl/1376182.
@article{osti_1376182,
title = {Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations},
author = {Fierce, Laura and McGraw, Robert L.},
abstractNote = {Here, 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 particleresolved 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.},
doi = {10.1002/2016JD026335},
journal = {Journal of Geophysical Research: Atmospheres},
number = 18,
volume = 122,
place = {United States},
year = {Wed Jul 26 00:00:00 EDT 2017},
month = {Wed Jul 26 00:00:00 EDT 2017}
}

Journal Article:
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