Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3D polarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developed convectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Current techniques are based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometry defaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud-droplets, changes its polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and glory angular regions can be used to infer the droplet size distribution. This work defines and derives a framework for a full 3D tomography of cloud droplets for both their mass concentration in space and their distribution across a range of sizes. This 3D retrieval of key microphysical properties is made tractable by our novel approach that involves a restructuring and differentiation of an open-source polarized 3D RT code to accommodate a special two-step optimization technique. Physically-realistic synthetic clouds are used to demonstrate the methodology with rigorous uncertainty quantification.

Effects of Biomass Burning on Stratocumulus Droplet Characteristics, Drizzle Rate, and Composition

This study reports on airborne measurements of stratocumulus cloud properties under varying degrees of influence from biomass burning (BB) plumes off the California coast. Data are reported from five total airborne campaigns based in Marina, California, with two of them including influence from wildfires in different areas along the coast of the western United States. The results indicate that subcloud cloud condensation nuclei number concentration and mass concentrations of important aerosol species (organics, sulfate, nitrate) were better correlated with cloud droplet number concentration (N d) as compared to respective above-cloud aerosol data. Given that the majority of BB particles resided above cloud tops, this is an important consideration for future work in the region as the data indicate that the subcloud BB particles likely were entrained from the free troposphere. Lower cloud condensation nuclei activation fractions were observed for BB-impacted clouds as compared to non-BB clouds due, at least partly, to less hygroscopic aerosols. Relationships between N d and either droplet effective radius or drizzle rate are preserved regardless of BB influence, indicative of how parameterizations can exhibit consistent skill for varying degrees of BB influence as long as N d is known. Lastly, the composition of both droplet residual particles and cloud water changed significantly when clouds were impacted by BB plumes, with differences observed for different fire sources stemming largely from effects of plume aging time and dust influence.

Influence of Turbulent Fluctuations on Cloud Droplet Size Dispersion and Aerosol Indirect Effects

Cloud droplet relative dispersion, defined as the standard deviation over the mean cloud droplet size, is of central importance in determining and understanding aerosol indirect effects. In recent work, it was found that cloud droplet size distributions become broader as a result of supersaturation variability and that the sensitivity of this effect is inversely related to cloud droplet number density. The subject is investigated in further detail using an extensive dataset from a laboratory cloud chamber capable of producing steady-state turbulence. An extended stochastic theory is found to successfully describe properties of the droplet size distribution, including an analytical expression for the relative dispersion. The latter is found to depend on the cloud droplet removal time, which in turn increases with the cloud droplet number density. The results show that relative dispersion decreases monotonically with increasing droplet number density, consistent with some recent atmospheric observations. Experiments spanning fast to slow microphysics regimes are reported. The observed dispersion is used to estimate time scales for autoconversion, demonstrating the important role of the turbulence-induced broadening effect on precipitation development. An initial effort is made to extend the stochastic theory to an atmospheric context with a steady updraft, for which autoconversion time is the controlling factor for droplet lifetime. As in the cloud chamber, relative dispersion is found to increase with decreasing cloud droplet number density.

Dispersion Aerosol Indirect Effect in Turbulent Clouds: Laboratory Measurements of Effective Radius

Cloud optical properties are determined not only by the number density n _d and mean radius r ¯ of cloud droplets but also by the shape of the droplet size distribution. The change in cloud optical depth with changing n _d , due to the change in distribution shape, is known as the dispersion effect. Droplet relative dispersion is defined as d = σ r / r ¯ . For the first time, a commonly used effective radius parameterization is tested in a controlled laboratory environment by creating a turbulent cloud. Stochastic condensation growth suggests d independent of n _d for a nonprecipitating cloud, hence nearly zero albedo susceptibility due to the dispersion effect. However, for size-dependent removal, such as in a laboratory cloud or highly clean atmospheric conditions, stochastic condensation produces a weak dispersion effect. The albedo susceptibility due to turbulence broadening has the same sign as the Twomey effect and augments it by order 10%.

Dual-field-of-view Raman lidar measurements for the retrieval of cloud microphysical properties

Dual-field-of-view Raman lidar measurements, detecting Raman-scattered light with two fields of view simultaneously, are used for the first time to retrieve cloud microphysical properties. The measurements are performed with the Multiwavelength Atmospheric Raman Lidar for Temperature, Humidity, and Aerosol Profiling (MARTHA) at the Leibniz Institute for Tropospheric Research in Leipzig, Germany. Light that is scattered in forward direction by cloud droplets and inelastically backscattered by N2 molecules is detected. A forward iterative algorithm uses the measured signals to derive profiles of the effective cloud droplet radius, extinction coefficient, and liquid-water content of the investigated clouds. The setup, algorithm, error analysis, and a measurement example are presented. The obtained liquid-water path is validated by observations with a microwave radiometer. With the capability to retrieve aerosol properties as well as cloud microphysical properties, the Raman lidar MARTHA is an ideal tool for studies of the aerosol indirect effect.