Classifying aerosol type using in situ surface spectral aerosol optical properties
Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties...
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Copernicus Publications
2017
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Online Access: | https://doi.org/10.5194/acp-17-12097-2017 https://noa.gwlb.de/receive/cop_mods_00042168 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00041788/acp-17-12097-2017.pdf https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.pdf |
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article Verlagsveröffentlichung Schmeisser, Lauren Andrews, Elisabeth Ogren, John A. Sheridan, Patrick Jefferson, Anne Sharma, Sangeeta Kim, Jeong Eun Sherman, James P. Sorribas, Mar Kalapov, Ivo Arsov, Todor Angelov, Christo Mayol-Bracero, Olga L. Labuschagne, Casper Kim, Sang-Woo Hoffer, András Lin, Neng-Huei Chia, Hao-Ping Bergin, Michael Sun, Junying Liu, Peng Wu, Hao Classifying aerosol type using in situ surface spectral aerosol optical properties |
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article Verlagsveröffentlichung |
description |
Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations. |
format |
Article in Journal/Newspaper |
author |
Schmeisser, Lauren Andrews, Elisabeth Ogren, John A. Sheridan, Patrick Jefferson, Anne Sharma, Sangeeta Kim, Jeong Eun Sherman, James P. Sorribas, Mar Kalapov, Ivo Arsov, Todor Angelov, Christo Mayol-Bracero, Olga L. Labuschagne, Casper Kim, Sang-Woo Hoffer, András Lin, Neng-Huei Chia, Hao-Ping Bergin, Michael Sun, Junying Liu, Peng Wu, Hao |
author_facet |
Schmeisser, Lauren Andrews, Elisabeth Ogren, John A. Sheridan, Patrick Jefferson, Anne Sharma, Sangeeta Kim, Jeong Eun Sherman, James P. Sorribas, Mar Kalapov, Ivo Arsov, Todor Angelov, Christo Mayol-Bracero, Olga L. Labuschagne, Casper Kim, Sang-Woo Hoffer, András Lin, Neng-Huei Chia, Hao-Ping Bergin, Michael Sun, Junying Liu, Peng Wu, Hao |
author_sort |
Schmeisser, Lauren |
title |
Classifying aerosol type using in situ surface spectral aerosol optical properties |
title_short |
Classifying aerosol type using in situ surface spectral aerosol optical properties |
title_full |
Classifying aerosol type using in situ surface spectral aerosol optical properties |
title_fullStr |
Classifying aerosol type using in situ surface spectral aerosol optical properties |
title_full_unstemmed |
Classifying aerosol type using in situ surface spectral aerosol optical properties |
title_sort |
classifying aerosol type using in situ surface spectral aerosol optical properties |
publisher |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/acp-17-12097-2017 https://noa.gwlb.de/receive/cop_mods_00042168 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00041788/acp-17-12097-2017.pdf https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.pdf |
geographic |
Arctic |
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Arctic |
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albedo Arctic |
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albedo Arctic |
op_relation |
Atmospheric Chemistry and Physics -- http://www.atmos-chem-phys.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2069847 -- 1680-7324 https://doi.org/10.5194/acp-17-12097-2017 https://noa.gwlb.de/receive/cop_mods_00042168 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00041788/acp-17-12097-2017.pdf https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.pdf |
op_rights |
uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/acp-17-12097-2017 |
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Atmospheric Chemistry and Physics |
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17 |
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19 |
container_start_page |
12097 |
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12120 |
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1766250270410407936 |
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00042168 2023-05-15T13:12:05+02:00 Classifying aerosol type using in situ surface spectral aerosol optical properties Schmeisser, Lauren Andrews, Elisabeth Ogren, John A. Sheridan, Patrick Jefferson, Anne Sharma, Sangeeta Kim, Jeong Eun Sherman, James P. Sorribas, Mar Kalapov, Ivo Arsov, Todor Angelov, Christo Mayol-Bracero, Olga L. Labuschagne, Casper Kim, Sang-Woo Hoffer, András Lin, Neng-Huei Chia, Hao-Ping Bergin, Michael Sun, Junying Liu, Peng Wu, Hao 2017-10 electronic https://doi.org/10.5194/acp-17-12097-2017 https://noa.gwlb.de/receive/cop_mods_00042168 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00041788/acp-17-12097-2017.pdf https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.pdf eng eng Copernicus Publications Atmospheric Chemistry and Physics -- http://www.atmos-chem-phys.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2069847 -- 1680-7324 https://doi.org/10.5194/acp-17-12097-2017 https://noa.gwlb.de/receive/cop_mods_00042168 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00041788/acp-17-12097-2017.pdf https://acp.copernicus.org/articles/17/12097/2017/acp-17-12097-2017.pdf uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2017 ftnonlinearchiv https://doi.org/10.5194/acp-17-12097-2017 2022-02-08T22:41:12Z Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations. Article in Journal/Newspaper albedo Arctic Niedersächsisches Online-Archiv NOA Arctic Atmospheric Chemistry and Physics 17 19 12097 12120 |