Abstract
Abstract. The non-hydrostatic atmospheric Model for Prediction Across Scales (MPAS-A), a global variable-resolution modeling framework, is applied at a range of resolutions from hydrostatic (60, 30, 16 km) to non-hydrostatic (4 km) scales using regional refinement over East Asia to simulate an extreme precipitation event. The event is triggered by a typical wind shear in the lower layer of the Meiyu front in East China on 25–27 June 2012 during the East Asian summer monsoon season. The simulations are evaluated using ground observations and reanalysis data. The simulated distribution and intensity of precipitation are analyzed to investigate the sensitivity to model configuration, resolution, and physics parameterizations. In general, simulations using global uniform-resolution and variable-resolution meshes share similar characteristics of precipitation and wind in the refined region with comparable horizontal resolution. Further experiments at multiple resolutions reveal the significant impacts of horizontal resolution on simulating the distribution and intensity of precipitation and updrafts. More specifically, simulations at coarser resolutions shift the zonal distribution of the rain belt and produce weaker heavy precipitation centers that are misplaced relative to the observed locations. In comparison, simulations employing 4 km cell spacing produce more realistic features of precipitation and wind. The difference among experiments in modeling rain belt features is mainly due to the difference in simulated wind shear formation and evolution during this event. Sensitivity experiments show that cloud microphysics have significant effects on modeling precipitation at non-hydrostatic scales, but their impacts are relatively small compared to that of convective parameterizations for simulations at hydrostatic scales. This study provides the first evidence supporting the use of convection-permitting global variable-resolution simulations for studying and improving forecasting of extreme precipitation over East China and motivates the need for a more systematic study of heavy precipitation events and the impacts of physics parameterizations and topography in the future. The key points are as follows. Model for Prediction Across Scales (MPAS) simulations at global uniform and variable resolutions share similar characteristics of precipitation and wind in the refined region. Numerical experiments reveal significant impacts of resolution on simulating the distribution and intensity of precipitation and updrafts. This study provides evidence supporting the use of convection-permitting global variable-resolution simulation to study extreme precipitation.
Highlights
Extreme precipitation receives great attention because of its potential for generating floods, landslides, and other hazardous conditions
This study explores the use of a non-hydrostatic global variable-resolution model, the Model for Prediction Across Scales (MPAS), to model an extreme precipitation event in East China
We examine the MPAS performance in simulating a heavy precipitation event over the Yangtze River Delta (YRD) region of East China and investigate its sensitivity to horizontal resolution and physics parameterizations
Summary
Extreme precipitation receives great attention because of its potential for generating floods, landslides, and other hazardous conditions. The spatiotemporal variations of extreme precipitation over East China and their possible causes and underlying mechanisms have been investigated in many previous studies using observations and models Liu et al (2015) analyzed data from meteorological stations in East China and found significant increases in heavy precipitation at both rural and urban stations during 1955–2011. This enhanced precipitation intensity in East China has been partly attributed to localized daytime precipitation events (Guo et al, 2017). A regional climate model was used to simulate the regional climate extremes of China and noted large sensitivity of the simulated summer heavy precipitation over East China to the choice of cumulus parameterizations (Hui et al, 2015)
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