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A New Framework to Compare Mass-Flux Schemes Within the AROME Numerical Weather Prediction Model

机译:在AROME数值天气预报模型中比较质量通量方案的新框架

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In the Application of Research to Operations at Mesoscale (AROME) numerical weather forecast model used in operations at M,t,o-France, five mass-flux schemes are available to parametrize shallow convection at kilometre resolution. All but one are based on the eddy-diffusivity-mass-flux approach, and differ in entrainment/detrainment, the updraft vertical velocity equation and the closure assumption. The fifth is based on a more classical mass-flux approach. Screen-level scores obtained with these schemes show few discrepancies and are not sufficient to highlight behaviour differences. Here, we describe and use a new experimental framework, able to compare and discriminate among different schemes. For a year, daily forecast experiments were conducted over small domains centred on the five French metropolitan radio-sounding locations. Cloud base, planetary boundary-layer height and normalized vertical profiles of specific humidity, potential temperature, wind speed and cloud condensate were compared with observations, and with each other. The framework allowed the behaviour of the different schemes in and above the boundary layer to be characterized. In particular, the impact of the entrainment/detrainment formulation, closure assumption and cloud scheme were clearly visible. Differences mainly concerned the transport intensity thus allowing schemes to be separated into two groups, with stronger or weaker updrafts. In the AROME model (with all interactions and the possible existence of compensating errors), evaluation diagnostics gave the advantage to the first group.
机译:在中尺度作战研究中的应用(AROME)中,法国,法国北部的作战中使用的数值天气预报模型提供了五种质量通量方案,用于以千米分辨率对浅层对流进行参数化。除了一个以外,其他所有模型都是基于涡-扩散-质量-通量方法,并且在夹带/夹带,上升气流垂直速度方程式和闭合假设方面有所不同。第五种基于更经典的质量通量方法。使用这些方案获得的屏幕级别分数显示出很少的差异,不足以突出行为差异。在这里,我们描述并使用了一个新的实验框架,该框架能够在不同方案之间进行比较和区分。一年来,每天在法国五个大都市无线电发声地点附近的小范围内进行每日预报实验。将云基,行星边界层高度和比湿度,势能温度,风速和云凝结水的归一化垂直剖面与观测值进行了比较,并相互比较。该框架允许对边界层内和边界上不同方案的行为进行特征化。特别是,清楚地看到了夹带/夹带配方,封闭假设和云方案的影响。差异主要涉及运输强度,因此可以将方案分为两组,上流或强或弱。在AROME模型中(具有所有交互作用并且可能存在补偿错误),评估诊断为第一组提供了优势。

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