Stochastic Parameterization of Deep Convection in Short-Range Ensemble Weather Forecasts

用于进行天气预报和模拟地球气候的数值模式不能明确地表示所有空间和时间尺度上的运动。相反，那些短尺度的运动必须通过使用参数化方案来考虑。一个模型将包含许多这样的方案，每一个都与一个被忽略的特定小尺度物理过程有关。这些将包括大气边界层中的湍流、重力波和深层潮湿对流。对流方案是该项目的重点，因为已知现有的深对流表示法是天气预报和气候模拟中一些最大和最顽固的系统误差的原因。参数化方案传统上被假定为是确定性的。因此，输入到一个计划是从当前状态的本地，解决规模流和输出是唯一的一个给定的输入。其原理是，小尺度运动可以被认为是统计和估计他们的整体平均效应反馈到大尺度。近年来，这种确定性假设受到了挑战：忽略小尺度运动的波动分量可能是无效的，它能够与分解尺度流强烈相互作用。有很好的证据表明，忽略这种波动不仅在理论上不令人满意，而且可能对模型性能产生重大影响。一个有吸引力的替代方法是使用随机动态参数化方法，其目的是考虑波动。随机方案的哲学是反馈特定小尺度状态的影响。根据小尺度运动的统计模型随机选择状态。2005年6月，欧洲中期天气预报中心组织了一次关于这一主题的讲习班。在会议录（第vii页）中，目前的情况是这样总结的：“随机动力参数化是一个相对较新的概念，但有可能对天气和气候预报的所有领域产生重大影响。“迄今为止，关于随机方法的研究一直令人鼓舞，并倾向于分为两个不同的类别。在一类中，随机分量被相对简单地处理，但被包括作为完整预测系统的一部分，并受到广泛的测试。例如，目前正在研究纳入气象局全球和区域天气预报系统（MOGREPS）的方法，这是一个新的业务天气预报系统。在另一个类别中，为随机分量构建详细的模型，但是测试通常是相当有限的，并且在某种程度上理想化的配置中发生。一个例子是Plant &克雷格的随机对流参数化。这个项目的关键问题，也是社区的一个主要问题，是是否值得努力构建随机变异的详细模型，或者简单的处理是否足够。为了回答这个问题，有必要首先实施一个详细的，国家的最先进的随机参数化到一个完整的业务预报系统，第二，比较其性能与更简单的处理变异。在此，Plant &克雷格方案将作为MOGREPS的一部分实施，其性能将与运行系统同时进行评估。当实施该计划，并允许适当的比较，有必要确定的长度和时间尺度上的随机变异是相关的。该项目将确定这些尺度及其在一般情况下的敏感性（即，这些结果将不特定于Plant &克雷格方案）。这是因为相关尺度的知识对于使用任何随机动态模型都是重要的。

The numerical models that are used to perform weather forecasts and to simulate the earth's climate are incapable of representing explicitly the motions on all space and time scales. Rather, those motions with short scales must be taken into account by using parameterization schemes. A model will contain a number of such schemes, each relating to a particular small-scale physical process that has been omitted. These will include turbulence in the atmospheric boundary layer, gravity waves and deep, moist convection. The convection scheme is the focus in this project since existing representations of deep convection are known to be responsible for some of the largest and most stubborn systematic errors in weather forecasting and climate modelling. Parameterization schemes have traditionally been assumed to be deterministic. Thus, the input to a scheme is taken from the current state of the local, resolved-scale flow and the output is unique for a given input. The philosophy is that the small-scale motions can be considered statistically and an estimate of their ensemble-mean effect is fed back to the large scale. In recent years, this deterministic assumption has been challenged: it may not be valid to neglect the fluctuating component of the small-scale motions, which is capable of interacting strongly with the resolved-scale flow. There is good evidence to suggest that neglect of such fluctuations is not just theoretically unsatisfactory but that it may have significant impacts on model performance. An attractive alternative is to use a stochastic-dynamic parameterization method, which aims to account for the fluctuations. The philosophy of a stochastic scheme is to feed back the effects of a particular small-scale state. The state is chosen at random based on a model for the statistics of the small-scale motions. In June 2005, a workshop on the subject was organized by the European Centre for Medium-range Weather Forecasts. In the Proceedings (p. vii) the current situation is summarized thus: 'Stochastic-Dynamic Parameterization is a relatively new concept, yet one that has potential to impact significantly on all areas of weather and climate forecasting.' Studies to date on the stochastic approach have been consistently encouraging and have tended to fall into two distinct categories. In one category, the stochastic component is treated in relatively simply, but is included as part of a full forecast system and subject to extensive testing. Examples include methods currently being investigated for inclusion in MOGREPS (Met Office Global and Regional Ensemble Prediction System), a new system for operational weather forecasting. In another category, detailed models are constructed for the stochastic component, but testing is typically rather limited and occurs in somewhat idealized configurations. An example is the stochastic convective parameterization of Plant & Craig. The key question for this project, and a major issue for the community, is whether efforts to construct detailed models of the stochastic variability are worthwhile, or whether a simple treatment might be sufficient. In order to answer that question, it is necessary first to implement a detailed, state-of-the-art stochastic parameterization into a full operational forecast system, and second to compare its performance with simpler treatments of variability. Here, the Plant & Craig scheme will be implemented as part of MOGREPS and its performance assessed in parallel with the operational system. When implementing the scheme, and to allow for appropriate comparisons, it is necessary to determine the length and time scales over which the stochastic variability is to be correlated. The project will establish these scales and their sensitivities in a generic context (i.e., these results will not be specific to the Plant & Craig scheme). This is because knowledge of the correlation scales is important for the use of any stochastic-dynamic model.