Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5805-2025
https://doi.org/10.5194/amt-18-5805-2025
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

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Cited articles

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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.
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