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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Nov 2018

Research article | 02 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

Antonio Di Noia1, Otto P. Hasekamp1, Bastiaan van Diedenhoven2,3, and Zhibo Zhang4 Antonio Di Noia et al.
  • 1SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584CA Utrecht, the Netherlands
  • 2Columbia University, Center for Climate Systems Research, 2910 Broadway, New York, NY 10025, United States
  • 3NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, United States
  • 4Physics Department, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21228, United States

Abstract. This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument, on board the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization, and has been applied to the processing of one year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate resolution Imaging Spectroradiometer (MODIS) products show negative biases around −2 in retrieved cloud optical thicknesses (COTs) and between −1 and −2μm in retrieved cloud effective radii. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.

Antonio Di Noia et al.
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