Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing
In the Arctic, the passive solar remote sensing of cloud properties over highly reflecting ground is challenging due to the low contrast between the clouds and underlying surfaces (sea ice and snow). Uncertainties in retrieved cloud optical thickness τ and cloud droplet effective radius r eff,C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the commonly unknown snow effective grain size r eff,S . Therefore, in a first step this snow grain size effect is quantified systematically for a conventional bi-spectral retrieval of τ and r eff,C for liquid water clouds. The largest impact of r eff,S of up to 83 % on τ and 62 % on r eff,C was found in case of small r eff,S and optically thin clouds. In the second part of the paper a retrieval method is presented that simultaneously retrieves all three parameters (τ, r eff,C , r eff,S ) in order to account for changes of the snow grain size in the cloud retrieval algorithm. Spectral cloud reflectivities at the three wavelength λ 1  = 1040 nm (sensitive to r eff,S ), λ 2  = 1650 nm (sensitive to τ), and λ 3  = 2100 nm (sensitive to r eff,C ) were normalized to reflectivity ratios and combined in a tri-spectral retrieval algorithm. Measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART-Albedometer) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct sea ice edge were analyzed. The retrieved values of τ, r eff,C , and r eff,S consistently represented the cloud properties across this transition from snow-covered sea ice to the open water and were comparable to estimates based on satellite data. Analysis showed, that the uncertainties of the tri-spectral retrieval increase for high τ, and low r eff,S , but nevertheless allows a simultaneous retrieval of cloud and surface snow properties providing snow effective grain size estimates in cloud-covered areas.