Remote sensing of ice crystal asymmetry parameter using multi-directional polarization measurements – Part 1: Methodology and evaluation with simulated measurements

Abstract

Abstract. We present a new remote sensing technique to infer the average asymmetry parameter of ice crystals near cloud top from multi-directional polarization measurements. The method is based on previous findings that (a) complex aggregates of hexagonal crystals generally have scattering phase matrices resembling those of their components; and (b) scattering phase matrices systematically vary with aspect ratios of crystals and their degree of microscale surface roughness. Ice cloud asymmetry parameters are inferred from multi-directional polarized reflectance measurements by searching for the closest fit in a look-up table of simulated polarized reflectances computed for cloud layers that contain individual, randomly oriented hexagonal columns and plates with varying aspect ratios and roughness values. The asymmetry parameter of the hexagonal particle that leads to the best fit with the measurements is considered the retrieved value. For clouds with optical thickness less than 5, the cloud optical thickness must be retrieved simultaneously with the asymmetry parameter, while for optically thicker clouds the asymmetry parameter retrieval is independent of cloud optical thickness. Evaluation of the technique using simulated measurements based on the optical properties of a number of complex particles and their mixtures shows that the ice crystal asymmetry parameters are generally retrieved to within 5 %, or about 0.04 in absolute terms. The retrieval scheme is largely independent of calibration errors, range and sampling density of scattering angles and random noise in the measurements. The approach can be applied to measurements of past, current and future airborne and satellite instruments that measure multi-directional polarized reflectances of ice-topped clouds.

Ice particle habit and surface roughness derived from PARASOL polarization measurements

“…The abundance of small ice particles may be due to the shattering of large ice particles on the probe inlet, artificially increasing the number of small particles seen in the images (Korolev et al, 2011). Compact aggregates and spatial bullet rosettes are also seen and tend to be more common in the midlatitudes (Um and McFarquhar, 2007). Some of the particles observed by the CPI probe appear to have hollow structures, a feature noted by Magono and Lee (1966), Schmitt and Heymsfield (2007), Baran (2012), and Yang et al (2008c, specifically, the photograph by Steven Warren shown in Fig.…”

“…Surface roughness has been observed in laboratory measurements of ice and from in situ measurements obtained in field campaigns (Korolev et al, 2011;Um and McFarquhar, 2007;Baumgardner et al, , 2011Neshyba et al, 2013). Thermodynamic conditions influence whether an ice particle is roughened and how rough it becomes (Baran et al, 2011), but it is currently unknown what percentage of ice particles in clouds might be roughened or how rough they are.…”

“…Even though ice clouds can be optically thin they are still important because of their spatial coverage, which can reach 70 % in the tropics (Nazaryan et al, 2008;Guignard et al, 2012). The ice habit and degree of surface roughness of the ice particles within natural ice clouds can have a large impact on the radiative properties of the clouds and affect estimates of their radiative forcing (Baran, 2009;Wendisch et al, 2005Wendisch et al, , 2007Yang et al, 2008a, b;Yi et al, 2013). Assuming rough ice particles instead of smooth can lead to a twofold increase in the amount of solar energy reflected back at the top of the atmosphere (Ulanowski et al, 2006;Baran, 2012).…”

“…Any roughness present will affect the radiative properties of ice clouds by changing the scattering phase function, resulting in less forward scattering and a lower value of the asymmetry parameter g (van Diedenhoven et al, 2012a).…”

Photopolarimetric retrievals of snow properties

“…However, the formalism is recognized to model the total reflectance with sufficient accuracy for reasons likely associated with the large number of scattering events taking place within the snowpack. Based on this assumption, the method is expected to perform even better for the polarization component because the polarimetric signatures of a medium are known to originate from its top layer (van Diedenhoven et al, 2013): deeper into the medium (i.e., past the first units of optical depth) multiple scattering randomizes polarization.…”