The FastEddy^®Resident‐GPU Accelerated Large‐Eddy Simulation Framework: Moist Dynamics Extension, Validation and Sensitivities of Modeling Non‐Precipitating Shallow Cumulus Clouds

Abstract

Herein we describe the moist dynamics formulation implemented within the graphics processing unit‐resident large‐eddy simulation FastEddy® model, which includes a simple saturation adjustment scheme for condensation and evaporation processes. Two LES model intercomparison exercises for non‐precipitating shallow cumulus clouds are simulated in order to validate this model extension, including a static forcing and a time‐dependent forcing case. Overall, we find our dynamical, thermodynamical and microphysical quantities, along with turbulence variability and fluxes, to be commensurate with the corresponding model intercomparison results. In addition, sensitivities to specific model settings are investigated. Among these settings, it is shown that boundary layer and cloud layer structure and characteristics are sensitive to use of higher‐order advection schemes impacting the vertical distribution of cloud content and associated turbulence statistics. Increasing the timescale of the saturation scheme leads to enhanced liquid water presence and decreases vertical velocity variance within the cloud deck. In some cases, these sensitivities agree with the model‐to‐model variability reported in the intercomparison exercises, highlighting the important role of specific model implementation choices in the context of shallow cumulus convection simulations. These analyses and findings also provide the basis for future extensions and applications of FastEddy® for modeling moist convection and precipitation scenarios.

Implementation and Validation of a Generalized Actuator Disk Parameterization for Wind Turbine Simulations within the FastEddy ® Model

“…Recently, performance and efficiency advantages have been demonstrated leveraging graphics processing units (GPUs) in lieu of traditional central processing units (CPUs) for running LES models (e.g., Schalkwijk, Griffith, Post, & Jonker, 2012;Van Heerwaarden et al, 2017). The FastEddy ® model (hereafter FastEddy), introduced by Sauer and Muñoz-Esparza (2020) and Muñoz-Esparza, Sauer, Jensen, Xue, and Grabowski (2022), was developed in the Research Applications Laboratory of the National Center for Atmospheric Research (NCAR) with the intent of enabling faster and more computationally feasible turbulence-resolving LES of the atmospheric boundary layer. FastEddy exploits the characteristics of GPU hardware amenable to fine-grained parallelism including high-bandwidth memory and thousands of processing cores organized in groups capable of concurrent (parallel) processing.…”

A Case Study of Cumulus Convection Over Land in Cloud‐Resolving Simulations With a Coupled Ray Tracer

“…The fast increase in computational power and memory of graphics processing units (GPUs) has dramatically improved the perspective of coupling ray tracers to atmospheric simulations. Ray tracing is one of the core applications of modern GPUs, primarily in the gaming and movie industry, and GPU computing has proven itself as a promising technique for cloud‐resolving simulations (Muñoz‐Esparza et al., 2022; Schalkwijk et al., 2015; van Heerwaarden et al., 2017) in recent years. In this study, we leverage the computing capabilities of modern GPUs to better understand how 3D radiative effects impact atmospheric simulations.…”

On the Application of an Observations‐Based Machine Learning Parameterization of Surface Layer Fluxes Within an Atmospheric Large‐Eddy Simulation Model

“…In order to systematically analyze these aspects, we implement a generic artificial NN algorithm within NCAR's graphics processing unit (GPU)‐resident FastEddy® model (Muñoz‐Esparza, Sauer, et al., 2022; Sauer & Muñoz‐Esparza, 2020, hereafter referred to as FastEddy). FastEddy is purpose‐built to exploit the accelerated performance potential of the GPUs for numerical investigations of the ABL.…”