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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 10, 3215-3230, 2017
https://doi.org/10.5194/amt-10-3215-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
04 Sep 2017
Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing
André Ehrlich1, Eike Bierwirth1,a, Larysa Istomina2, and Manfred Wendisch1 1Leipzig Institute for Meteorology (LIM), University of Leipzig, Leipzig, Germany
2Institute of Environmental Physics, University of Bremen, Bremen, Germany
anow at: PIER-ELECTRONIC GmbH, Nassaustr. 33–35, 65719 Hofheim-Wallau, Germany
Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C.

In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) 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 snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.


Citation: Ehrlich, A., Bierwirth, E., Istomina, L., and Wendisch, M.: Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing, Atmos. Meas. Tech., 10, 3215-3230, https://doi.org/10.5194/amt-10-3215-2017, 2017.
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Short summary
In the Arctic, uncertainties in passive solar remote sensing of cloud properties arise from uncertainties in the assumed spectral surface albedo, mainly determined by the generally unknown effective snow grain size. Therefore, a retrieval method is presented that simultaneously derives liquid water cloud and snow surface parameters, including cloud optical thickness, droplet effective radius, and effective snow grain size. Airborne measurements were used to test the retrieval procedure.
In the Arctic, uncertainties in passive solar remote sensing of cloud properties arise from...
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