Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density and fall speed. Most microphysical parameterizations in numerical weather prediction models typically only represent purely solid or liquid hydrometeors. Generally, this has been an acceptable compromise since the melting layer is typically very shallow and adding a mixed solid/liquid particle type would result in increased computational time. This research shows how improvements were made to the treatment of melting snow in a microphysical parameterization within the Weather Research and Forecasting (WRF) model by implementing an approximation of snowflake melted fraction together with a physically-based expression for melting particle terminal velocity. In addition, the more appropriate definition of melting level defined by the wet-bulb temperature was consistently used in various process rates, all while not adding additional prognostic variables that would add computational cost. Multiple events observed during the 2015–2016 Olympic Mountain Experiment (OLYMPEX) were used to compare with the WRF model results. The modified scheme is able to represent disdrometer observations of joint particle size and fall velocity during wet snow events, as well as fall velocity profiles through the melting layer derived from a vertically-pointing radar. The improved scheme removes ‘bulls’ eyes' of snow accumulation in lee-side areas within the melting zone, and should result in better predictions of surface precipitation phase and amount.

落入融化层的雪最终将由一部分融化水组成，因此会改变其大小，形状，密度和下降速度的特征。数值天气预报模型中的大多数微物理参数化通常仅表示纯固体或液体水凝物。通常，这是一个可以接受的折衷方案，因为熔融层通常非常浅，添加固/液混合颗粒类型会导致计算时间增加。这项研究显示了如何通过在气象研究与预报（WRF）模型中的微物理参数化中改进对融雪的处理，方法是实现雪花融化分数的近似值以及基于物理的融化粒子终速度的表达式。此外，湿球温度定义的更合适的熔融水平定义始终用于各种工艺速率，而同时又不添加会增加计算成本的其他预后变量。使用2015–2016年奥林匹克山地实验（OLYMPEX）期间观察到的多个事件与WRF模型结果进行比较。修改后的方案能够代表在湿雪事件期间用联合气象台观测到的联合颗粒大小和下降速度，以及从垂直指向的雷达得出的通过融化层的下降速度剖面。改进后的方案消除了融化带内背风区域积雪的“大头眼”，并可以更好地预测表面降水的阶段和数量。