Abstract A realistic representation of mixed-phase clouds in weather and climate models is essential to accurately simulate the model’s radiative balance and water cycle. In addition, it is important for providing downstream applications with physically realistic model data for computation of, for instance, atmospheric icing on societal infrastructure and aircraft. An important quantity for forecasts of atmospheric icing is to model accurately supercooled liquid water (SLW). In this study, we implement elements from the Thompson cloud microphysics scheme into the numerical weather prediction model HARMONIE-AROME, with the aim to improve its ability to predict SLW. We conduct an idealised process-level evaluation of microphysical processes, and analyse the water phase budget of clouds and precipitation to compare the modified and original schemes, and also identify the processes with the most impact to form SLW. Two idealised cases representing orographic lift and freezing drizzle, both known to generate significant amounts of SLW, are setup in a 1 D column version of HARMONIE-AROME. The experiments show that the amount of SLW is largely sensitive to the ice initiation processes, snow and graupel collection of cloud water, and the rain size distribution. There is a doubling of the cloud water maximum mixing ratio, in addition to a prolonged existence of SLW, with the modified scheme compared with the original scheme. The spatial and temporal extent of cloud ice and snow are reduced, due to stricter conditions for ice nucleation. The findings are important as the HARMONIE-AROME models is used for operational forecasting in many countries in northern Europe having a colder climate, as well as for climate assessments over the Arctic region.

中文翻译：

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改进 HARMONIE-AROME 天气预报模型中过冷液态水的表​​示

摘要 天气和气候模型中混合相云的真实表现对于准确模拟模型的辐射平衡和水循环至关重要。此外，重要的是为下游应用程序提供物理逼真的模型数据，用于计算例如社会基础设施和飞机上的大气结冰。预测大气结冰的一个重要量是准确模拟过冷液态水 (SLW)。在这项研究中，我们将汤普森云微物理方案中的元素应用到数值天气预报模型 HARMONIE-AROME 中，旨在提高其预测 SLW 的能力。我们对微物理过程进行了理想化的过程级评估，并分析了云和降水的水相收支，以比较修改后的方案和原始方案，并确定对形成 SLW 影响最大的过程。在 HARMONIE-ARME 的 1D 列版本中设置了两个代表地形升力和冻毛毛雨的理想化案例，两者都已知会产生大量的 SLW。实验表明，SLW 的数量对冰的形成过程、云水的雪和霰收集以及雨的大小分布很敏感。与原方案相比，修改方案的云水最大混合比增加了一倍，而且SLW的存在时间更长。由于更严格的冰成核条件，云冰雪的空间和时间范围减少。这些发现很重要，因为 HARMONIE-AROME 模型被用于气候较冷的北欧许多国家的业务预测，