Advancing precipitation prediction using a new-generation storm-resolving model framework – SIMA-MPAS (V1.0): a case study over the western United States

Abstract. Global climate models (GCMs) have advanced in many ways as computing power has allowed more complexity and finer resolutions. As GCMs reach storm-resolving scales, they need to be able produce realistic precipitation intensity, duration, frequency at fine scales with consideration of scale-aware parameterization. This study uses a state-of-the-art GCM nonhydrostatic dynamical core – the Model for Prediction Across Scales (MPAS), incorporated atmospheric component (Community Atmosphere Model, CAM) open-source Community Earth System (CESM), within Integrated Modeling (SIMA) framework (referred SIMA-MPAS). At uniform coarse (here, 120 km) grid resolution, SIMA-MPAS configuration is comparable standard hydrostatic CESM (with finite-volume (FV) core) reasonable energy mass conservation on climatological timescales. With balance performance between CAM-FV (workhorse (newly developed core), it gives confidence SIMA-MPAS's applications resolution. To evaluate this, we focus how model performs when reaching scale 3 km. do this efficiently, compose case using variable-resolution refined mesh km covering western USA 60 over rest globe. We evaluated satellite station-based gridded observations comparison traditional regional (WRF, Weather Research Forecasting model). Our results show representations complex terrains temporally spatially. Along much improved near-surface temperature, topography, land–air interactions, also demonstrate significantly enhanced snowpack distributions. work illustrates that global resolution can variability, fine-scale features, extremes advance both weather studies. next-generation could ultimately bridge large-scale forcing constraints better inform impacts predictions across scales.