Arizona winter extreme precipitation: synoptic types and numerical weather prediction evaluations
Tyler Maio1, Eyad Atallah1, Hsin-I Chang1, and Bo Svoma2
1Department of Hydrology and Atmospheric Sciences
The University of Arizona, Tucson, AZ
2Salt River Project, Phoenix, AZ
Extreme cool-season precipitation in central Arizona is frequently associated with landfalling atmospheric rivers (ARs) and plays a critical role in water supply within the Salt and Verde River Basins. Variability in synoptic-scale structure influences precipitation intensity and distribution across complex terrain, with implications for forecast skill. Thirteen extreme cool-season events between 2021 and 2023 were classified into two dominant regimes: Progressive Atmospheric Rivers (PARs) and Zonal Atmospheric Rivers (ZARs). Convective-permitting model simulations were conducted using the Weather Research and Forecasting (WRF) model and the Model for Prediction Across Scales (MPAS). Sensitivity experiments were also conducted to evaluate the impact of cloud microphysics parameterization. Model simulations were evaluated against PRISM 48-hour precipitation accumulations and rain gauge observations within headwater regions of the Salt and Verde River Basins. ZAR events exhibit larger wet biases in the convective-permitting model simulations than PAR events and disproportionately influence aggregate error statistics. In contrast, HRRR forecasts display a systematic dry bias, which is reduced by both WRF and MPAS, with MPAS using the Thompson microphysics scheme performing best overall. Across all case studies, the Thompson scheme consistently outperforms WSM6, consistent with improved representation of mixed-phase processes during cool-season, orographically enhanced precipitation. All simulations overestimate precipitation along ridgelines, indicating persistent terrain-anchored biases, though bias magnitude varies by the choice of microphysics scheme which can potentially reduce bias over the complex terrain. These findings demonstrate that synoptic regime and cloud microphysics can jointly modulate quantitative precipitation forecast skill during extreme AR events in complex terrain.