Abstract
Abstract. The recently developed 3-D TenStream radiative transfer solver was integrated into the University of California, Los Angeles large-eddy simulation (UCLA-LES) cloud-resolving model. This work documents the overall performance of the TenStream solver as well as the technical challenges of migrating from 1-D schemes to 3-D schemes. In particular the employed Monte Carlo spectral integration needed to be reexamined in conjunction with 3-D radiative transfer. Despite the fact that the spectral sampling has to be performed uniformly over the whole domain, we find that the Monte Carlo spectral integration remains valid. To understand the performance characteristics of the coupled TenStream solver, we conducted weak as well as strong-scaling experiments. In this context, we investigate two matrix preconditioner: geometric algebraic multigrid preconditioning (GAMG) and block Jacobi incomplete LU (ILU) factorization and find that algebraic multigrid preconditioning performs well for complex scenes and highly parallelized simulations. The TenStream solver is tested for up to 4096 cores and shows a parallel scaling efficiency of 80–90 % on various supercomputers. Compared to the widely employed 1-D delta-Eddington two-stream solver, the computational costs for the radiative transfer solver alone increases by a factor of 5–10.
Highlights
To improve climate predictions and weather forecasts we need to understand the delicate linkage between clouds and radiation
We investigate two matrix preconditioner: geometric algebraic multigrid preconditioning (GAMG) and block Jacobi incomplete LU (ILU) factorization and find that algebraic multigrid preconditioning performs well for complex scenes and highly parallelized simulations
We described the necessary steps to couple the 3-D TenStream radiation solver to the UCLA-large-eddy simulations (LESs) model
Summary
To improve climate predictions and weather forecasts we need to understand the delicate linkage between clouds and radiation. The error that is introduced by the random sampling is assumed to be unbiased and uncorrelated in space and time and should not change the overall course of the simulation Their algorithm is known as Monte Carlo spectral integration and is implemented in the UCLA-LES. In order to couple the TenStream solver to the UCLA-LES we need to revisit the Monte Carlo spectral integration and check if it is still valid if used with 3-D solvers. Another reason for the computational burden is the complexity of the radiation solver alone.
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