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首页> 外文期刊>Monthly Weather Review >Predictability Mysteries in Cloud-Resolving Models
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Predictability Mysteries in Cloud-Resolving Models

机译：云解析模型中的可预测性之谜

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The rapid amplification of small-amplitude perturbations by the chaotic nature of the atmospheric dynamics intrinsically limits the skill of deterministic weather forecasts. In this study, limited-area cloud-resolving numerical weather prediction (NWP) experiments are conducted to investigate the role of me-soscale processes in determining predictability. The focus is set on domain-internal error growth by integrating an ensemble of simulations using slightly modified initial conditions but identical lateral boundary conditions. It is found that the predictability of the three investigated cases taken from the Mesoscale Alpine Programme (MAP) differs tremendously. In terms of normalized precipitation spread, values between 0.05 (highly predictable) and 1 (virtually unpredictable) are obtained. Analysis of the derived ensemble spread demonstrates that the diabatic forcing associated with moist dynamics is the prime source of rapid error growth. However, in agreement with an earlier study it is found that the differentiation between convective and stratiform rain is unable to account for the distinctive precipitation spreads of the three cases. In particular, instability indices are demonstrated to be poor predictors of the predictability level. An alternate hypothesis is proposed and tested. It is inspired by the dynamical instability theory and states that significant loss of predictability only occurs over moist convectively unstable regions that are able to sustain propagation of energy against the mean flow. Using a linear analysis of gravity wave propagation, this hypothesis is shown to provide successful estimates of the predictability level for the three cases under consideration.
机译：大气动力学的混沌性质使小振幅扰动迅速放大，从本质上限制了确定性天气预报的技能。在这项研究中，进行了有限区域云解析数值天气预报（NWP）实验，以研究中尺度过程在确定可预测性中的作用。通过集成使用稍微修改的初始条件但相同的横向边界条件的仿真集合，将重点放在域内部错误的增长上。发现从中尺度高山计划（MAP）提取的三个调查案例的可预测性差异很大。根据归一化的降水分布，获得的值介于0.05（高度可预测）和1（几乎不可预测）之间。对派生的整体展布的分析表明，与潮湿动力学相关的非绝热强迫是快速误差增长的主要来源。然而，与较早的研究相一致，发现对流雨和层状雨之间的区别不能解释这三种情况下独特的降水分布。特别是，不稳定指数被证明是可预测性水平的较差的预测指标。提出并检验了另一种假设。它受到动力不稳定性理论的启发，指出可预测性的重大损失仅发生在能够维持能量相对于平均流传播的潮湿对流不稳定区域上。使用重力波传播的线性分析，可以证明该假设可以为三种情况下的可预测性水平提供成​​功的估计。

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