Modeling analysis and experiments of dual-FOV multiple scattering Raman lidar for detecting water cloud microphysical properties
Year: 2024
Type: article
Abstract: Clouds are the primary regulators of the Earth’s climate, but the mechanisms through which they influence climate are poorly understood. Probing the microphysical properties of liquid water clouds is an urgent need for research on the Earth’s atmospheric system and atmospheric physics. In this paper, an iterative inversion of water cloud microphysical properties for a multiple scattering Raman lidar is presented, which can enable continuous, vertical measurements of the extinction coefficient of water clouds, the liquid water content (LWC), the effective radius of the cloud droplets, and cloud droplet number concentration (CDNC). The technique based on a constructed dual-field-of-view (dual-FOV) device combined with a quasi-small-angle (QSA) approximation model was investigated. The determination method of the optimal field of view (FOV) for lidar detection was discussed to enhance the sensitivity of the measured signals to the observed cloud microphysical parameters. Field observations with a co-located microwave radiometer (MWR) were carried out to verify the effectiveness of the lidar system, also an error analysis of the measurement examples was performed. The dual FOV Raman lidar experimental LWC measurement has an average deviation of 0.034 g/m 3 and the relative error is 27.2% relative to the MWR measurement. The system has important potential applications in aerosol-cloud interactions and provides new ideas and methods for studying water cloud microphysical properties, further studying aerosol indirect effects.
Source: Optics Express
DOI: 10.1364/oe.542594
Authors: Liangliang Qi, Fan Yang, Lei Tian, Jiandong Mao, Hu Zhao, Dengxin Hua
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,270
Awards: G4178050463, G7045539851
OpenAlex ID: W4404104261
