Parallelized Remapping Algorithms for km-scale Global Weather and Climate Simulations with Icosahedral Grid System

A corrigendum was issued for this paper on February 13, 2024. You can download the corrigendum from the Supplemental Material section of this citation page.

In weather and climate research, latitude–longitude grid data are typically used for analysis and visualization, and remapping from model native grids to latitude–longitude grids typically requires a significant amount of time. Here, we developed a series of parallelized remapping algorithms for NICAM, a global weather and climate model with an icosahedral grid system, and demonstrated their performance with global 14–0.87-km mesh model data on the supercomputer Fugaku. The original remapping tool in NICAM supports parallelization only in reading and interpolating data. In our proposed algorithms, the process of data writing is parallelized by separating output files or using the MPI-IO library, both of which enable us to remap 0.87-km mesh data with 670 million horizontal grid points and 94 vertical levels. The benchmark with 14-km mesh data shows that the developed algorithms significantly outperform the original algorithm in terms of elapsed time (by 7.4–8.7 times) and memory usage (by 2.8–5.0 times). Among the proposed algorithms, the separation of output files, along with reduced MPI communication size, leads to a better performance in the elapsed time and its scalability, and the use of the MPI-IO library leads to a better performance in memory usage. The remapping year per wall-clock day, assuming a six-hourly output interval, is up to 0.56 with 3.5-km mesh data, demonstrating the feasibility of handling global cloud-resolving climate simulation data in a practical time. This study demonstrates the importance of IO performance, including MPI-IO, in accelerating weather and climate research on future supercomputers.