气候变化领域集成服务门户(CCPortal): A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations

CCPortal

DOI	10.5194/acp-24-2861-2024
	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
发表日期	2024
ISSN	1680-7316
EISSN	1680-7324
起始页码	24
结束页码	5
卷号	24 期号:5
英文摘要	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 kappa -Kohler 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.
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001190525700001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301509
作者单位	California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); United States Department of Energy (DOE); Oak Ridge National Laboratory; Environmental Defense Fund; Department of Space (DoS), Government of India; Physical Research Laboratory - India; University of Oklahoma System; University of Oklahoma Health Sciences Center; National Aeronautics & Space Administration (NASA); NASA Langley Research Center
推荐引用方式 GB/T 7714	Patel, Piyushkumar N.,Jiang, Jonathan H.,Gautam, Ritesh,et al. A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations[J],2024,24(5).
APA	Patel, Piyushkumar N..,Jiang, Jonathan H..,Gautam, Ritesh.,Gadhavi, Harish.,Kalashnikova, Olga.,...&Omar, Ali.(2024).A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations.ATMOSPHERIC CHEMISTRY AND PHYSICS,24(5).
MLA	Patel, Piyushkumar N.,et al."A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations".ATMOSPHERIC CHEMISTRY AND PHYSICS 24.5(2024).