Enhancing Numerical Model Accuracy with Quasi Double-Precision: Extending the Application to Dynamics and Tracers in MPAS-A

The limitations of high-performance computing (HPC) often impose significant constraints on the development and performance of numerical models. While double precision ensures high accuracy in traditional models, it comes with substantial computational costs. Lower precision can reduce computational expenses but may introduce round-off errors that degrade model accuracy. The Quasi double-precision (QDP) algorithm addresses these errors by maintaining corrections, thereby improving result accuracy. Building on previous work where the QDP method demonstrated effective enhancement in the dynamics core of the Model for Prediction Across Scales-Atmosphere (MPAS-A), this study explores the potential of extending the QDP approach to both the dynamics core and tracer components. The impact of the QDP method on these two components is explored through a series of planned experiments using idealized and real-data cases. By applying QDP to both the dynamics and tracer components, this research aims to assess the joint effects on model accuracy and efficiency, seeking to demonstrate the feasibility of further enhancing model performance without significantly increasing computational resources. This study offers a promising path toward more cost-effective simulations in numerical weather prediction and climate modeling.