Accelerating large-eddy simulations of clouds with Tensor Processing Units

TL;DR

This paper demonstrates the use of Tensor Processing Units (TPUs) to accelerate large-eddy simulations of low clouds, enabling faster and more scalable cloud modeling crucial for climate research.

Contribution

It introduces a TPU-based LES code for cloud simulation, achieving significant speedups and scalability, facilitating extensive computational experiments and dataset generation.

Findings

Successful simulation of stratocumulus clouds during DYCOMS

Achieved 10x speedup over real-time in large domains

Enabled large-scale, scalable cloud simulations on TPUs

Abstract

Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large-eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large datasets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on Tensor Processing Units (TPUs), application-specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU-based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with $10\times$ speedup over real-time evolution in domains with a $34.7~\mathrm{km} \times 53.8~\mathrm{km}$ horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds.