Articles | Volume 8, issue 16
Atmos. Chem. Phys., 8, 4741–4757, 2008
Atmos. Chem. Phys., 8, 4741–4757, 2008

  18 Aug 2008

18 Aug 2008

Remote sensing of cloud sides of deep convection: towards a three-dimensional retrieval of cloud particle size profiles

T. Zinner1,2, A. Marshak1, S. Lang3, J. V. Martins1,4, and B. Mayer2 T. Zinner et al.
  • 1NASA – Goddard Space Flight Center, Climate and Radiation Branch, Greenbelt, MD, USA
  • 2Deutsches Zentrum für Luft- und Raumfahrt, Inst. für Physik der Atmosphäre, Oberpfaffenhofen, 82230 Wessling, Germany
  • 3NASA – Goddard Space Flight Center, Mesoscale Atmospheric Processes Branch, Greenbelt and Science Systems and Applications Inc., Lanham, MD, USA
  • 4Department of Physics and Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA

Abstract. 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.

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.

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.

Final-revised paper