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


Total article views: 2,846 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,056 729 61 2,846 52 56
  • HTML: 2,056
  • PDF: 729
  • XML: 61
  • Total: 2,846
  • BibTeX: 52
  • EndNote: 56
Views and downloads (calculated since 02 Nov 2018)
Cumulative views and downloads (calculated since 02 Nov 2018)

Viewed (geographical distribution)

Total article views: 2,846 (including HTML, PDF, and XML) Thereof 2,518 with geography defined and 328 with unknown origin.
Country # Views %
  • 1


Latest update: 21 Feb 2024
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.