Abstract
Abstract This study investigates the capabilities of a non-hydrostatic global, variable-resolution model in simulating tropical cyclone precipitation, with historically significant Typhoon Fitow (1323) as a case study. Employing three grid settings (24 km, 60-10 km, 60-3 Ian) and two microphysical parameterization schemes (WSM6 and Thompson), the study investigates the influence of grid resolution and microphysical parameterization on precipitation simulation. The simulated precipitation intensity and spatial distribution of high-resolution grids exhibit better agreement with the observations compared to the coarse-resolution grids. Specifically, the 60-3 km grid setting shows the greatest improvement in spatial correlation with observed precipitation data compared to the 24 km grid. Through the analysis of the thermal dynamic field, the high-resolution grid configuration more effectively simulates indicators for strong convective weather events, such as convective available potential energy (CAPE), helicity, and nonadiabatic heating. Analysis of TRMM satellite observations reveals that the high-resolution grid simulation results more accurately capture the distribution characteristics of hydrometeor mixing ratio compared to the coarse-resolution grids. Differences in hydrometeor content within convective clouds are more pronounced across grid resolutions than in stratiform clouds, even with the same parameterization scheme. Additionally, at the same resolution, the disparity in ice-phase particle content between the two schemes is much greater than the disparity in liquid-phase particle content. It is also noteworthy that the WSM6 scheme delivers superior performance compared to the Thompson scheme. In summary, this study demonstrates that refining model resolution has a more significant impact on precipitation intensity than the selection of physical parameterization scheme. The Model for Prediction Across Scales (MPAS), using a high-resolution variable-resolution grid, can be effectively used for typhoon precipitation simulation research.
NETL
NSTL
Springer Nature