WRF Precipitation Performance and Predictability for Systematically Varied Parameterizations over Complex Terrain

Significance Statement Numerical models are the most common tool to predict the weather. They rely on several physics packages, some of which approximate processes that are relatively small compared to the model grid size, while others provide the model with important ground surface properties. Since weather characteristics can be very different across Earth, the ideal combination of these physics packages varies by region. We sought the optimal model setup to predict precipitation in southwest British Columbia where rainfall is strongly influenced by seasons and mountains. This study demonstrates general limitations with precipitation forecasts. For example, short and extreme rainfall is generally difficult to predict. Forecasts in front of and over mountain ranges are often too wet, whereas forecasts behind mountain ranges are often too dry. We identified different model setups that performed best in the summer dry season and the cool wet season. Our results can inform forecasters regarding better model setups in areas with similar weather characteristics to yield better precipitation forecasts. Physics parameterizations in the Weather Research and Forecasting (WRF) Model are systematically varied to investigate precipitation forecast performance over the complex terrain of southwest British Columbia (BC). Comparing a full year of modeling data from over 100 WRF configurations to station observations reveals sensitivities of precipitation intensity, season, location, grid resolution, and accumulation window. The choice of cumulus and microphysics parameterizations is most important. The WSM5 microphysics scheme yields competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although the scale-aware Grell-Freitas cumulus parameterization performs better for summertime convective precipitation, the conventional Kain-Fritsch parameterization better simulates wintertime frontal precipitation, which contributes to the majority of the annual precipitation in southwest BC. Finer grid spacings have lower relative biases and a more realistic spread in precipitation intensity distribution, yet higher relative standard deviations of their errors-they produce finer spatial differences and local extrema. Finer resolutions produce the best fraction of correct-to-incorrect forecasts across all precipitation intensities, whereas the coarser 27-km domain yields the highest hit rates and equitable threat scores. Verification metrics improve greatly with longer accumulation windows-hourly precipitation values are prone to double-penalty issues, while longer accumulation windows compensate for timing errors but lose information about short-term precipitation intensities. This study provides insights regarding WRF precipitation performance in complex terrain across a wide variety of configurations, using metrics important to a range of end users.