Abstract. Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitee Adaptation Dynamique Developpement International) model with a parameterization of deep convection. This configuration is also run with the deep-convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep-convection parameterization and the target model without deep-convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that the total mass, heat and momentum are conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep-convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread, leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

中文翻译：

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通过对流尺度的模型误差采样来模拟亚网格尺度过程的模型不确定性

摘要。理想情况下，集合预报中的扰动方案应基于模型误差的统计特性。然而，这些模型误差的统计特性通常是未知的。在实践中，对扰动进行了务实的建模和调整，以最大限度地提高集合预报的技能。在本文中，基于目标和参考模型之间的比较，开发了一种通用方法来诊断与特定物理过程相关的模型错误。此处，参考模型是具有深度对流参数化的 ALADIN（Aire Limitee Adaptation Dynamique Developpement International）模型的配置。这种配置也会在关闭深对流参数化方案的情况下运行，从而降低预测技能。然后将模型误差定义为具有尺度感知深对流参数化的参考模型与没有深对流参数化的目标模型之间的能量和质量通量的差异。在论文的第二部分，诊断出的模型误差特征用于通过从训练周期采样模型误差来随机扰动目标模型的通量，使得网格内的分布以及垂直和多元相关性列被保留。通过扰动通量，可以保证总质量、热量和动量守恒。在 2009 年 4 月 11 日至 20 日期间进行的测试表明，具有随机通量扰动与初始条件扰动相结合的系综系统不仅优于目标系综，其中深对流没有参数化，但对于许多变量，它甚至比参考系综（具有尺度感知深对流方案）表现更好。随机通量扰动的引入减少了小规模的错误传播，同时增加了整体传播，导致更熟练的集成。与其他最先进的随机扰动方案相比，对流层上层的影响最大，具有实质性的改进。在较低水平上，改进较小或中性，但预测技能降低的温度除外。随机通量扰动的引入减少了小规模的错误传播，同时增加了整体传播，导致更熟练的集成。与其他最先进的随机扰动方案相比，对流层上层的影响最大，具有实质性的改进。在较低水平上，改进较小或中性，但预测技能降低的温度除外。随机通量扰动的引入减少了小规模的错误传播，同时增加了整体传播，导致更熟练的集成。与其他最先进的随机扰动方案相比，对流层上层的影响最大，具有实质性的改进。在较低水平上，改进较小或中性，但预测技能降低的温度除外。