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A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling

机译:天气和空气质量建模的规模和气溶胶意识到随机对流参数

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A convective parameterization is described and evaluated that may be used in high resolution non-hydrostatic mesoscale models as well as in modeling system with unstructured varying grid resolutions and for convection aware simulations. This scheme is based on a stochastic approach originally implemented by Grell and Devenyi (2002). Two approaches are tested on resolutions ranging from 20 km to 5 km. One approach is based on spreading subsidence to neighboring grid points, the other one on a recently introduced method by Arakawa et al. (2011). Results from model intercomparisons, as well as verification with observations indicate that both the spreading of the subsidence and Arakawa's approach work well for the highest resolution runs. Because of its simplicity and its capability for an automatic smooth transition as the resolution is increased, Arakawa's approach may be preferred. Additionally, interactions with aerosols have been implemented through a cloud condensation nuclei (CCN) dependent autoconversion of cloud water to rain as well as an aerosol dependent evaporation of cloud drops. Initial tests with this newly implemented aerosol approach show plausible results with a decrease in predicted precipitation in some areas, caused by the changed autoconversion mechanism. This change also causes a significant increase of cloud water and ice detrainment near the cloud tops. Some areas also experience an increase of precipitation, most likely caused by strengthened downdrafts.
机译:描述和评估对流参数化,可用于高分辨率非静水压Mescule模型以及具有非结构化变化的网格分辨率的建模系统和对流意识仿真。该方案基于由Grell和Devenyi(2002)实施的目前的随机方法。两种方法在20公里到5公里的决议上进行了测试。一种方法是基于扩散沉降到邻近网格点,另一个在Arakawa等人最近引入的方法上。 (2011)。型号离法的结果,以及观测结果的验证表明,沉降和阿拉川的方法都适用于最高分辨率运行。由于其简单性和其用于分辨率的自动平滑过渡的能力,因此Arakawa的方法可能是优选的。另外,通过云凝结核(CCN)依赖于云水的云和雨水以及云层的气溶胶依赖性蒸发来实现与气溶胶的相互作用。具有这种新实施的气溶胶方法的初始测试表明,由改变的自动变频机制引起的一些区域的预测降水量减少了合理的结果。这种变化也会导致云层附近的云水和冰碎屑增加。有些地区也经历了降水量的增加,最有可能由加强的下降过程引起。

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