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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 12, issue 3
Atmos. Meas. Tech., 12, 1697-1716, 2019
https://doi.org/10.5194/amt-12-1697-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 1697-1716, 2019
https://doi.org/10.5194/amt-12-1697-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Mar 2019

Research article | 18 Mar 2019

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

Antonio Di Noia et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Antonio Di Noia on behalf of the Authors (20 Feb 2019)  Author's response    Manuscript
ED: Publish as is (21 Feb 2019) by Alexander Kokhanovsky
Publications Copernicus
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Short summary
We present a neural network algorithm for the retrieval of cloud physical properties from multi-angle polarimetric measurements. We have trained the algorithm on a large dataset of synthetic measurements and applied it to a year of POLDER-3 data. A comparison against MODIS cloud products reveals that our algorithm is capable of performing cloud property retrievals on a global scale and possibly improves the estimates of cloud effective radius over land with respect to existing POLDER-3 products.
We present a neural network algorithm for the retrieval of cloud physical properties from...
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