Articles | Volume 12, issue 3
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, Otto P. Hasekamp, Bastiaan van Diedenhoven, and Zhibo Zhang


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

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
AR by Antonio Di Noia on behalf of the Authors (24 Feb 2019)
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.