Simulation of wet snow during winter orographic precipitation using the Predicted Particle Properties (P3) microphysics scheme

Forecasting winter precipitation types and their transitions is challenging because the type can vary (e.g., rain, snow, freezing rain, ice pellets, wet snow, graupel) and multiple phases can be involved. In this study, simulations of snow, including wet snow, using the Predicted Particle Properties (P3) bulk microphysics scheme that can predict the evolution of mixed-phase particles through the representation of the bulk liquid mass fraction, are analyzed and compared with observational data from the International Collaborative Experiments for Pyeongchang 2017–2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign that took place in South Korea. Simulations with and without predicted liquid fraction are compared. Predicting the liquid fraction improves the representation of precipitation phases by shifting liquid (rain) to mixed (wet snow) in all six cases where wet snow was observed. Mean simulated liquid mass fractions are higher than the mean retrieved values while the snow densities are smaller. However, both simulated quantities improve in simulations using the predicted liquid fraction. The trend obtained in the retrievals consisting of higher densities by a factor of 2–3 between the supersites near the coast and the supersites located at higher elevation is well captured by the simulations using the predicted liquid fraction. Differences in the representation of melting with and without the predicted liquid fraction are responsible for the changes in particle densities and the precipitation phase at the surface. This study demonstrates the capability of the P3 scheme with prediction of liquid fraction to forecast events with dense, wet snow.