Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 3
Articles | Volume 12, issue 3
https://doi.org/10.5194/amt-12-1697-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-1697-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 3,504 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,617 803 84 3,504 82 77
  • HTML: 2,617
  • PDF: 803
  • XML: 84
  • Total: 3,504
  • BibTeX: 82
  • EndNote: 77
Views and downloads (calculated since 02 Nov 2018)
Cumulative views and downloads (calculated since 02 Nov 2018)

Viewed (geographical distribution)

Total article views: 3,504 (including HTML, PDF, and XML) Thereof 3,175 with geography defined and 329 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 03 Nov 2024
Download
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.
Read more