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

To evaluate this, we focus on how the SIMA-MPAS model performs when reaching a storm-resolving scale at 3 km. To do this efficiently, we compose a case study using a SIMAMPAS variable-resolution configuration with a refined mesh of 3 km covering the western USA and 60 km over the rest of the globe. We evaluated the model performance using satellite and station-based gridded observations with comparison to a traditional regional climate model (WRF, the Weather Research and Forecasting model).

Our results show realistic representations of precipitation over the refined complex terrains temporally and spatially. Along with much improved near-surface temperature, realistic topography, and land-air interactions, we also demonstrate significantly enhanced snowpack distributions. This work illustrates that the global SIMA-MPAS at storm-resolving resolution can produce much more realistic regional climate variability, finescale features, and extremes to advance both climate and weather studies.

This next-generation storm-resolving model could ultimately bridge large-scale forcing constraints and better inform climate impacts and weather predictions across scales.