Comparison of Two Operational Microphysics Schemes Across Various Regional-MPAS Simulations

arXiv:2606.12762v1 Announce Type: new Abstract: Accurately representing convection and precipitation remains a persistent challenge for Numerical Weather Prediction (NWP) models due to biases in convective initiation, storm organization, and rainfall distribution, particularly in subtropical/tropical environments. This study evaluated how microphysics parameterizations influence convective organization and precipitation using hindcasts with the Model for Prediction Across Scales - Atmosphere (MPAS-A) on a variable-resolution mesh down to 1-km resolution. Two operational microphysics schemes, National Severe Storm Labs (NSSL) microphysics and Thompson-Eidhammer Microphysics Parameterization for Operations (TEMPO), were examined across three subtropical/tropical regions during boreal summer under strongly- and weakly-forced regimes. Both schemes captured the general timing and placement of convection, but differed in storm structure and rainfall distribution. TEMPO produced more numerous, weaker convective cores with earlier, more widespread precipitation and cooler surface conditions, while NSSL favored fewer, stronger cores and updrafts with more cloud water, ice, and graupel hydrometeors, though less snow, and more spatially concentrated, intense rainfall. Despite these structural differences, both schemes diverged more from observations than from each other, producing scattered convective cells with minimal mesoscale organization and insufficient stratiform precipitation. The simulations also exhibited regime-dependent errors, with rainfall under- (over)-represented in strongly- (weakly)-forced regimes and forecast skill notably lower in the latter. Improving representation of localized precipitation processes remains essential for capturing convection across a wider range of scales and regimes. Future work should target microphysics evaluation across regimes and regions, with process-level improvements reducing convective biases.