EGU23-13771, updated on 17 Jul 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine Learning Emulation of 3D Shortwave Radiative Transfer for Shallow Cumulus Cloud Fields

Jui-Yuan Christine Chiu1, Chen-Kuang Kevin Yang1, Jake J. Gristey2,3,4, Graham Feingold3, and William I. Gustafson5
Jui-Yuan Christine Chiu et al.
  • 1Colorado State University, Department of Atmospheric Science, Fort Collins, CO, United States of America (
  • 2Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, USA
  • 3NOAA Chemical Sciences Laboratory , Boulder, USA
  • 4Laboratory for Atmospheric and Space Physics (LASP), University of Colorado Boulder, Boulder, USA
  • 5Pacific Northwest National Laboratory, Richland, USA

Clouds play an important role in determining the Earth’s radiation budget. Despite their complex and three-dimensional (3D) structures, their interactions with radiation in models are often simplified to one-dimensional (1D), considering the time required to compute radiative transfer. Such a simplification ignores cloud Inhomogeneity and horizontal photon transport in radiative processes, which may be an acceptable approximation for low-resolution models, but can lead to significant errors and impact cloud evolution predictions in high-resolution simulations. Since model developments and operations are heading toward a higher resolution that is more susceptible to radiation errors, a fast and accurate 3D radiative transfer scheme becomes important and necessary. To address the need, we develop a machine-learning-based 3D radiative transfer emulator to provide surface radiation, shortwave fluxes at all layers, and heating rate profiles. The emulators are trained for highly heterogeneous shallow cumulus under different solar positions. We will discuss the performance of the emulators in accuracy and efficiency and discuss their potential applications.

How to cite: Chiu, J.-Y. C., Yang, C.-K. K., Gristey, J. J., Feingold, G., and Gustafson, W. I.: Machine Learning Emulation of 3D Shortwave Radiative Transfer for Shallow Cumulus Cloud Fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13771,, 2023.