Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
    3.841
  • CiteScore value: 3.71 CiteScore
    3.71
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
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.
Data sets

MODIS Atmosphere {L3} Daily Product S. Platnick et~al. https://doi.org/10.5067/MODIS/MOD08_D3.006

Publications Copernicus
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.
We present a neural network algorithm for the retrieval of cloud physical properties from...
Citation