Atmospheric radiative transfer plays a central role in understanding global climate change and anthropogenic climate forcing, and in the remote sensing of surface and atmospheric properties. Because of their opacity and highly scattering nature, clouds (covering more than half the planet at any time) pose unique challenges in atmospheric radiative transfer calculations.

Some widely-used assumptions regarding clouds—such as having a flat top and base, horizontal uniformity, and infinite extent—are amenable to simple one-dimensional (1-D) radiative transfer and are therefore attractive from a computational point of view. However, these assumptions are completely unrealistic and yield errors. The ever-increasing need to realistically simulate cloud radiative processes in remote sensing and energy budget applications has contributed to the recent rapid growth of the three-dimensional (3-D) radiative transfer (RT) community [e.g., Marshak and Davis, 2005].