Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data

The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.

[2]  Yeon‐Hee Kim,et al.  Estimation of the liquid water content and Z–LWC relationship using Ka‐band cloud radar and a microwave radiometer , 2018 .

[3]  Yunfei Che,et al.  An improvement of the retrieval of temperature and relative humidity profiles from a combination of active and passive remote sensing , 2019, Meteorology and Atmospheric Physics.

[4]  G. Mie Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen , 1908 .

[5]  P. Kollias,et al.  Multifrequency radar observations of clouds and precipitation including the G-band , 2021 .

[6]  R. Misumi,et al.  Characteristics of Droplet Size Distributions in Low-Level Stratiform Clouds Observed from Tokyo Skytree , 2018 .

[7]  Chunsheng Zhao,et al.  Statistical analysis of microphysical properties and the parameterization of effective radius of warm clouds in Beijing area , 2009 .

[8]  Chuanfeng Zhao,et al.  An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies , 2011 .

[9]  K. Gunn,et al.  The microwave properties of precipitation particles , 1954 .

[10]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[11]  Chunsheng Zhao,et al.  Method to retrieve cloud condensation nuclei number concentrations using lidar measurements , 2019, Atmospheric Measurement Techniques.

[12]  Jianjun Liu,et al.  Cloud optical and microphysical properties derived from ground‐based and satellite sensors over a site in the Yangtze Delta region , 2013 .

[13]  U. Blahak,et al.  Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models , 2021, Atmosphere.

[14]  Pavlos Kollias,et al.  Radar-radiometer retrievals of cloud number concentration and dispersion parameter in nondrizzling marine stratocumulus , 2013 .