ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-8-4741-2008 Remote sensing of cloud sides of deep convection: towards a three-dimensional retrieval of cloud particle size profiles Zinner T. 1 2 Marshak A. 1 Lang S. 3 Martins J. V. 1 4 Mayer B. 2 NASA – Goddard Space Flight Center, Climate and Radiation Branch, Greenbelt, MD, USA Deutsches Zentrum für Luft- und Raumfahrt, Inst. für Physik der Atmosphäre, Oberpfaffenhofen, 82230 Wessling, Germany NASA – Goddard Space Flight Center, Mesoscale Atmospheric Processes Branch, Greenbelt and Science Systems and Applications Inc., Lanham, MD, USA Department of Physics and Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA 18 08 2008 8 16 4741 4757 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/8/4741/2008/acp-8-4741-2008.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/8/4741/2008/acp-8-4741-2008.pdf

The cloud scanner sensor is a central part of a recently proposed satellite remote sensing concept – the three-dimensional (3-D) cloud and aerosol interaction mission (CLAIM-3D) combining measurements of aerosol characteristics in the vicinity of clouds and profiles of cloud microphysical characteristics. Such a set of collocated measurements will allow new insights in the complex field of cloud-aerosol interactions affecting directly the development of clouds and precipitation, especially in convection. The cloud scanner measures radiance reflected or emitted by cloud sides at several wavelengths to derive a profile of cloud particle size and thermodynamic phase. For the retrieval of effective size a Bayesian approach was adopted and introduced in a preceding paper. <br><br> In this paper the potential of the approach, which has to account for the complex three-dimensional nature of cloud geometry and radiative transfer, is tested in realistic cloud observing situations. In a fully simulated environment realistic cloud resolving modelling provides complex 3-D structures of ice, water, and mixed phase clouds, from the early stage of convective development to mature deep convection. A three-dimensional Monte Carlo radiative transfer is used to realistically simulate the aspired observations. <br><br> A large number of cloud data sets and related simulated observations provide the database for an experimental Bayesian retrieval. An independent simulation of an additional cloud field serves as a synthetic test bed for the demonstration of the capabilities of the developed retrieval techniques. For this test case only a minimal overall bias in the order of 1% as well as pixel-based uncertainties in the order of 1 μm for droplets and 8 μm for ice particles were found for measurements at a high spatial resolution of 250 m.

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