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

Weber, Anna; Köcher, Gregor; Mayer, Bernhard (2025): Parameterization of 3D cloud geometry and a neural-network-based fast forward operator for polarized radiative transfer. Atmospheric Measurement Techniques, 18 (20). pp. 5805-5821. ISSN 1867-8548

Abstract

Clouds generally have a complex three-dimensional geometry. However, realistic three-dimensional radiative transfer simulations of clouds are computationally expensive, so most retrievals of cloud properties assume one-dimensional clouds, which introduces retrieval biases. In this work, a fast forward operator for polarized 3D radiative transfer in the visible wavelength range is presented. To this end, a new approximation for 3D radiative transfer, the InDEpendent column local halF-sphere ApproXimation (IDEFAX), is introduced. The basic idea behind this approximation is similar to the independent column approximation assuming plane-parallel clouds. However, every column is approximated by an independent field of 3D half-spherical clouds instead of a plane-parallel homogeneous cloud. This field of half-spherical clouds is defined by the local cloud surface orientation angles and the cloud fraction. Thus, the IDEFAX has only three more parameters compared to the plane-parallel approximation. To obtain a fast forward operator, artificial neural networks are trained for both the plane-parallel and the half-spherical cloud assumptions. The IDEFAX and the neural network forward operators are validated against polarized 3D radiative transfer simulations with MYSTIC for low-level Arctic mixed-phase clouds using a realistic cloud field simulated with the WRF model. The use of the IDEFAX significantly improves the representation of 3D radiative effects in the simulated radiance fields compared to the plane-parallel independent column approximation. Due to the implementation of the forward operator with neural networks, the computation time for both approximations is comparable and about 5 orders of magnitude faster than full 3D radiative transfer simulations for the shown example. The introduced neural network forward operators are constructed to be used in retrievals of cloud properties with the specMACS instrument. However, the methods are also applicable to other measurements in the visible wavelength range and to model data.

• Dokumententyp: Artikel (LMU)
• Organisationseinheit (Fakultäten): 17 Physik
• DFG-Fachsystematik der Wissenschaftsbereiche: Naturwissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 442667104