Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5805-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-18-5805-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Research article
 | 
27 Oct 2025
Research article |  | 27 Oct 2025

Parameterization of 3D cloud geometry and a neural-network-based fast forward operator for polarized radiative transfer

Anna Weber, Gregor Köcher, and Bernhard Mayer

Viewed

Total article views: 5,085 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,823 1,041 221 5,085 199 175 208
  • HTML: 3,823
  • PDF: 1,041
  • XML: 221
  • Total: 5,085
  • Supplement: 199
  • BibTeX: 175
  • EndNote: 208
Views and downloads (calculated since 10 Jul 2025)
Cumulative views and downloads (calculated since 10 Jul 2025)

Viewed (geographical distribution)

Total article views: 5,085 (including HTML, PDF, and XML) Thereof 4,933 with geography defined and 152 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 25 Sep 2026
Download
Short summary
A neural-network-based fast forward operator for polarized 3D radiative transfer is presented. The forward operator uses the new InDEpendent column local halF-sphere ApproXimation (IDEFAX). Polarized radiances simulated with IDEFAX and the forward operator were validated against full 3D radiative transfer simulations with MYSTIC and show a significantly improved representation of 3D radiative effects compared to the plane-parallel independent column approximation at comparable computation times.
Read more
Share
Special issue
HALO-(AC)3 – an airborne campaign to study air...