Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign

This study evaluates the performance of four bulk-type microphysics schemes, Weather Research and Forecasting (WRF) double-moment 6-class (WDM6), WRF double-moment 7-class (WDM7), Thompson, and Morrison, focusing on hydrometeors and microphysics budgets in the WRF model version 4.1.3. Eight snowstorm cases, which can be sub-categorized as cold-low, warm-low, and air–sea interaction cases are selected, depending on the synoptic environment during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign. All simulations present a positive bias in the simulated surface precipitation for cold-low and warm-low cases. Furthermore, the simulations for the warm-low cases show a higher probability of detection score than simulations for the cold-low and air–sea interaction cases even though the simulations fail to capture the accurate transition layer for wind direction. WDM6 and WDM7 simulate abundant cloud ice for the cold-low and warm-low cases, and thus snow is mainly generated by aggregation. Meanwhile, Thompson and Morrison schemes simulate insignificant cloud ice amounts, especially over the lower atmosphere, where cloud water is simulated instead. Snow in the Thompson and Morrison schemes is mainly formed by the accretion between snow and cloud water and deposition. The melting process is analyzed as a key process to generate rain in all schemes. The discovered positive precipitation bias for the warm-low and cold-low cases can be mitigated by reducing the melting efficiency in all schemes. The contribution of melting to rain production is reduced for the air–sea interaction case with decreased solid-phase hydrometeors and increased cloud water in all simulations.