A depolarisation lidar-based method for the determination of liquid-cloud microphysical properties

Abstract

Abstract. The fact that polarisation lidars measure a depolarisation signal in liquid clouds due to the occurrence of multiple scattering is well known. The degree of measured depolarisation depends on the lidar characteristics (e.g. wavelength and receiver field of view) as well as the cloud macrophysical (e.g. cloud-base altitude) and microphysical (e.g. effective radius, liquid water content) properties. Efforts seeking to use depolarisation information in a quantitative manner to retrieve cloud properties have been undertaken with, arguably, limited practical success. In this work we present a retrieval procedure applicable to clouds with (quasi-)linear liquid water content (LWC) profiles and (quasi-)constant cloud-droplet number density in the cloud-base region. Thus limiting the applicability of the procedure allows us to reduce the cloud variables to two parameters (namely the derivative of the liquid water content with height and the extinction at a fixed distance above cloud base). This simplification, in turn, allows us to employ a fast and robust optimal-estimation inversion using pre-computed look-up tables produced using extensive lidar Monte Carlo (MC) multiple-scattering simulations. In this paper, we describe the theory behind the inversion procedure and successfully apply it to simulated observations based on large-eddy simulation (LES) model output. The inversion procedure is then applied to actual depolarisation lidar data corresponding to a range of cases taken from the Cabauw measurement site in the central Netherlands. The lidar results were then used to predict the corresponding cloud-base region radar reflectivities. In non-drizzling condition, it was found that the lidar inversion results can be used to predict the observed radar reflectivities with an accuracy within the radar calibration uncertainty (2–3 dBZ). This result strongly supports the accuracy of the lidar inversion results. Results of a comparison between ground-based aerosol number concentration and lidar-derived cloud-droplet number densities are also presented and discussed. The observed relationship between the two quantities is seen to be consistent with the results of previous studies based on aircraft-based in situ measurements.

Local ice formation via liquid water growth in slowly ascending humid aerosol/liquid water layers observed with ground‐based lidars and radiosondes

“…It should be mentioned that the multiple scattering can also induce an increase in the depolarization ratio with increasing penetration of laser light into the cloud. Furthermore, the larger the lidar FOV, the stronger the multiple-scattering-induced depolarization [Hu et al, 2006;Donovan et al, 2015]. Thus, a small lidar FOV (~1 mrad) is favorable for reducing the multiple-scattering effect.…”

Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles

“…The technique has been used for a long time to monitor and investigate cirrus cloud systems (e.g., Sassen, 1991Sassen, , 2005Reichardt et al, 2002Reichardt et al, , 2008 and polar stratospheric cloud evolution (see, e.g., Browell et al, 1990;Achtert and Tesche, 2014). The method is well suited to study heterogeneous ice formation in mixed-phased clouds (e.g., Sassen et al, 2003;Ansmann et al, 2005Ansmann et al, , 2008Ansmann et al, 2009a;Seifert et al, , 2011 and liquidwater cloud developments (e.g., Bissonnette, 2005;Donovan et al, 2015). Meanwhile, polarization lidars are intensively used to explore aerosol mixtures and to identify soil, desert, and volcanic dust (e.g., McNeil and Carswell, 1975;Iwaska and Hayashida, 1981;Winker and Osborn, 1992;Gobbi, 1998;Murayama et al, 1999Murayama et al, , 2004Cairo et al, 1999;Gobbi et al, 2000;Sakai et al, 2003;Sassen et al, 2007;Freudenthaler et al, 2009;Ansmann et al, 2010;Ansmann et al, 2011a;Groß et al, 2012;Miffre et al, 2012;Amiridis et al, 2013;Nisantzi et al, 2014).…”

Twomey Effect in Subtropical Stratocumulus Clouds From Uv Depolarization Lidar

“…Observations from the lidar were processed using an inversion method which produces estimates of cloud microphysical properties such as the effective radius and cloud droplet number density. This new inversion method is based on Monte Carlo modelling of multiple scattering within idealised semiadiabatic clouds [3]. The method utilizes the propensity of light to become depolarized when it undergoes multiple scattering within a liquid water cloud.…”