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

doi:10.5194/acp-24-2861-2024

Title: A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations

Journal Article · Tue Mar 05 00:00:00 EST 2024 · Atmospheric Chemistry and Physics (Online)

DOI:https://doi.org/10.5194/acp-24-2861-2024· OSTI ID:2324590

^[1];

^[2]; Gautam, Ritesh ^[3];

^[4]; Kalashnikova, Olga ^[2]; Garay, Michael J. ^[2]; Gao, Lan ^[5]; Xu, Feng ^[5]; Omar, Ali ^[6]

California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL); Oak Ridge Associated Universities (ORAU), Oak Ridge, TN (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL)
Environmental Defense Fund, Washington, DC (United States)
Physical Research Laboratory, Ahmedabad (India)
University of Oklahoma, Norman, OK (United States)
NASA Langley Research Center, Hampton, VA (United States)

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 (N_CCN) 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 N_CCN 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 N_CCN. The potential of this algorithm is further evaluated by retrieving N_CCN 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 N_CCN 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); National Aeronautics and Space Administration (NASA)

Contributing Organization:: PNNL, BNL, ANL, ORNL

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2324590

Journal Information:: Atmospheric Chemistry and Physics (Online), Vol. 24, Issue 5; ISSN 1680-7324

Publisher:: Copernicus Publications, EGUCopyright Statement

Country of Publication:: United States

Language:: English

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