A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations
Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (NCCN) usi...
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00072105 2024-04-14T08:00:18+00:00 A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations Patel, Piyushkumar N. Jiang, Jonathan H. Gautam, Ritesh Gadhavi, Harish Kalashnikova, Olga Garay, Michael J. Gao, Lan Xu, Feng Omar, Ali 2024-03 electronic https://doi.org/10.5194/acp-24-2861-2024 https://noa.gwlb.de/receive/cop_mods_00072105 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070332/acp-24-2861-2024.pdf https://acp.copernicus.org/articles/24/2861/2024/acp-24-2861-2024.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-24-2861-2024 https://noa.gwlb.de/receive/cop_mods_00072105 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070332/acp-24-2861-2024.pdf https://acp.copernicus.org/articles/24/2861/2024/acp-24-2861-2024.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2024 ftnonlinearchiv https://doi.org/10.5194/acp-24-2861-2024 2024-03-19T12:18:16Z Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing. Article in Journal/Newspaper Aerosol Robotic Network Niedersächsisches Online-Archiv NOA Atmospheric Chemistry and Physics 24 5 2861 2883 |
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article Verlagsveröffentlichung Patel, Piyushkumar N. Jiang, Jonathan H. Gautam, Ritesh Gadhavi, Harish Kalashnikova, Olga Garay, Michael J. Gao, Lan Xu, Feng Omar, Ali A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
topic_facet |
article Verlagsveröffentlichung |
description |
Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing. |
format |
Article in Journal/Newspaper |
author |
Patel, Piyushkumar N. Jiang, Jonathan H. Gautam, Ritesh Gadhavi, Harish Kalashnikova, Olga Garay, Michael J. Gao, Lan Xu, Feng Omar, Ali |
author_facet |
Patel, Piyushkumar N. Jiang, Jonathan H. Gautam, Ritesh Gadhavi, Harish Kalashnikova, Olga Garay, Michael J. Gao, Lan Xu, Feng Omar, Ali |
author_sort |
Patel, Piyushkumar N. |
title |
A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
title_short |
A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
title_full |
A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
title_fullStr |
A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
title_full_unstemmed |
A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
title_sort |
remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/acp-24-2861-2024 https://noa.gwlb.de/receive/cop_mods_00072105 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070332/acp-24-2861-2024.pdf https://acp.copernicus.org/articles/24/2861/2024/acp-24-2861-2024.pdf |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
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-24-2861-2024 https://noa.gwlb.de/receive/cop_mods_00072105 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070332/acp-24-2861-2024.pdf https://acp.copernicus.org/articles/24/2861/2024/acp-24-2861-2024.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/acp-24-2861-2024 |
container_title |
Atmospheric Chemistry and Physics |
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24 |
container_issue |
5 |
container_start_page |
2861 |
op_container_end_page |
2883 |
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1796299487581306880 |