Articles | Volume 10, issue 14
Atmos. Chem. Phys., 10, 6685–6697, 2010
https://doi.org/10.5194/acp-10-6685-2010
Atmos. Chem. Phys., 10, 6685–6697, 2010
https://doi.org/10.5194/acp-10-6685-2010

  22 Jul 2010

22 Jul 2010

Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment

D. Huang1, A. J. Gasiewski2, and W. Wiscombe1,3 D. Huang et al.
  • 1Brookhaven National Laboratory, Upton, NY 11973, USA
  • 2University of Colorado, Boulder, CO 80309, USA
  • 3NASA Goddard Space Flight Center (code 913), Greenbelt, MD 20771, USA

Abstract. Tomographic methods offer great potential for retrieving three-dimensional spatial distributions of cloud liquid water from radiometric observations by passive microwave sensors. Fixed tomographic systems require multiple radiometers, while mobile systems can use just a single radiometer. Part 1 (this paper) examines the results from a limited cloud tomography trial with a single-radiometer airborne system carried out as part of the 2003 AMSR-E validation campaign over Wakasa Bay of the Sea of Japan. During this trial, the Polarimetric Scanning Radiometer (PSR) and Microwave Imaging Radiometer (MIR) aboard the NASA P-3 research aircraft provided a useful dataset for testing the cloud tomography method over a system of low-level clouds. We do tomographic retrievals with a constrained inversion algorithm using three configurations: PSR, MIR, and combined PSR and MIR data. The liquid water paths from the PSR retrieval are consistent with those from the MIR retrieval. The retrieved cloud field based on the combined data appears to be physically plausible and consistent with the cloud image obtained by a cloud radar. We find that some vertically-uniform clouds appear at high altitudes in the retrieved field where the radar shows clear sky. This is likely due to the sub-optimal data collection strategy. This sets the stage for Part 2 of this study that aims to define optimal data collection strategies using observation system simulation experiments.

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