Examination of Computational Performance and Potential Applications of a Global Numerical Weather Prediction Model MPAS Using KISTI Supercomputer NURION

Abstract

To predict extreme weather events, we conducted high-resolution global atmosphere modeling and simulation using high-performance computing. Using a new-generation global weather/climate prediction model called MPAS (Model for Prediction Across Scales) with variable resolution, we tested strong scalability on the KISTI (Korea Institute of Science and Technology Information) supercomputer NURION. In addition to assessing computational performance, we simulated three typhoons that occurred in 2019 to analyze the forecast accuracy of MPAS. MPAS results were also applied to force an ADCIRC (The Advanced CIRCulation) + SWAN (Simulating Waves Nearshore) model to predict coastal flooding over southern Korea. The time-integration of MPAS showed excellent scalability up to 4096 cores of NURION KNL (KNight Landing) nodes, but a serious I/O bottleneck issue was still found after trying two additional I/O strategies (i.e., adjusting the stripe count and using a burst buffer). On the other hand, the forecast accuracy of MPAS showed very encouraging results for wind and pressure during typhoons. ADCIRC+SWAN also generated a good estimate of significant wave height for typhoon Mitag. The proposed variable-resolution MPAS model, under an efficient computational environment, could be utilized to predict and understand the highly nonlinear chaotic atmosphere and coastal flooding in typhoons.

Impact Analysis of Variable Resolution of MPAS on Intrinsic Predictability Using Bred Vectors

“…Besides, initialization, mostly reading initial data, also resulted in more than a 10% standard deviation, indicating that the amount of time it took fluctuated. Kang et al [24] has already mentioned the issue of communication among input/output Lastly, Table 4 shows that the variable resolution that we tested required about 3.2 times more computational cost when concerning only time integration. The use of variable resolution also required almost double the initialization time, mostly for reading input data.…”

“…Besides, initialization, mostly reading initial data, also resulted in more than a 10% standard deviation, indicating that the amount of time it took fluctuated. Kang et al [24] has already mentioned the issue of communication among input/output cores within NURION. Considering the improvement in terms of forecast accuracy that users can expect with variable resolution, they must understand the amount of computational cost it will take.…”

“…Indeed, a defining feature of MPAS is a variable resolution configuration that allows for a smooth varying resolution in the horizontal plane as shown in Figure 1. Indeed, Kang et al [24] tested this model with the variable resolution configuration that played a role of the atmospheric forcing for the prediction system of coastal flooding and concluded that the atmospheric forcing from MPAS worked well with the prediction of significant wave height over the coastal area of a typhoon on the Korean peninsula. Here, we examine if an improvement of the model predictability is obtained and how much we could benefit from the variable resolution setting compared to the quasiuniform horizontal resolution setting.…”