Model-based evaluation of cloud geometry and droplet size retrievals from 2-D polarized measurements of specMACS

Abstract. Cloud radiative properties play a significant role in radiation and energy budgets and are influenced by both the cloud top height and particle size distribution. Both cloud top heights and particle size distributions can be derived from two-dimensional intensity and polarization measurements by the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). The cloud top heights are determined using a stereographic method (Kölling et al., 2019) and the particle size distributions are derived in terms of the cloud effective radius and the effective variance from multidirectional polarized measurements of the cloudbow (Pörtge et al., 2023). In this study, the two methods are validated using realistic 3-D radiative transfer simulations of specMACS measurements of a synthetic field of shallow cumulus clouds to ensure the methods’ accuracy and to determine possible error sources. The simulations are performed with the 3-D Monte Carlo radiative transport model MYSTIC (Mayer, 2009) using cloud data from highly resolved LES simulations. Both retrieval methods are applied to the simulated data and compared to the respective properties of the underlying cloud field from the LES simulations. Moreover, the influence of the cloud development on both methods is evaluated by applying the algorithms to idealized simulated data where the clouds did not change during the simulated overflight of one minute over the cloud field. For the cloud top height retrieval an absolute mean difference of less than 70 m with a standard deviation of about 130 m compared to the expected heights from the model is found. The elimination of the cloud development as a possible error source results in mean differences of (46 ± 140) m. For the effective radius, an absolute average difference of about (−0.2 ± 1.30) µm from the expected effective radius from the LES model input is derived for the realistic simulation and (−0.03 ± 1.27) µm for the simulation without cloud development. The difference between the effective variance derived from the cloudbow retrieval and the expected effective variance is (0.02 ± 0.05) for both simulations.