Putting the morph into CoMorph: Adapting convection parametrisation for the hard grey zone

将变形放入 CoMorph：针对硬灰色区域调整对流参数化

• 批准号: NE/X018512/1
• 负责人: Robert Plant
• 金额: $ 116.52万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX018512%2F1
• 关键词: Putting; morph; into; CoMorph; Adapting

Society benefits enormously from operational weather forecasts which inform decision making by individual members of the public through to weather-sensitive business activities, and the energy and broader industrial sectors. Operational forecasts are produced using numerical weather prediction (NWP) models that represent key atmospheric processes. Many of the most intense rainfall events (and hence impacts such as flash flooding) are produced by convective processes active on relatively small scales of km to 10s of km, but their formation is also dependent on processes at both larger and smaller scales. In their efforts to forecast such events, operational centres continue to push the limits of the most powerful high-performance super computers to enable forecasting at finer resolutions, allowing processes on km scales to be explicitly simulated. However, this strategy is not sufficient on its own to produce more accurate forecasts of rainfall. NWP models at km scales operate in a regime that we call the "hard grey zone," where the horizontal scale of individual convective storms are similar to the numerical model grid. The convection is not properly resolved, but also cannot be parametrised using conventional approaches, which assume that an unresolved process occurs on scales very much smaller than the grid scale. We currently have no clear modelling strategy for this regime, and in consequence face significant systematic issues with the timing, intensity and spatial patterns of convection. To realise the full value of km scale simulations, our project seeks an improved representation of convective processes at just these most problematic scales. Our hypothesis is that it is useful to retain a convection parametrisation but that key aspects must be rethought. We especially target the representation of in-cloud vertical velocity, the need to specify and make self-consistent use of key length scales, and the representation of unresolved variability. An important guiding principle is that the physical processes have much better constrained representations outside of the hard grey zone and hence that any solidly-based representation of the physics within the hard grey zone should match with these in the appropriate limits. The key length scales are needed in part to identify the correct limits, in part as controlling parameters for the variability and in part to rescale various aspects of the parametrisation. The representation of in-cloud vertical motion will be important for the matching process and is also key to ensuring appropriate couplings to other aspects of the model physics. The representation of variability will be stochastic, which is a natural approach in the hard grey zone where parametrised motions are imperfectly constrained by grid-scale variables, and so are very uncertain.We will critically assess our model developments by performing simulations of cases from an observational field campaign over the UK in summer 2023. Comparison with observations will help us to establish whether our developments impact the simulations for the right physical reasons. Our assessments will draw on novel diagnostic measures which are designed to highlight the upscale impacts of model changes. To avoid any danger of "overfitting" our developments towards these cases we will also assess simulations of tropical convection over south-east Asia.

天气预报能为社会带来巨大的利益，因为天气预报为市民个人、对天气敏感的商业活动、能源和更广泛的工业部门的决策提供了信息。业务预报是使用代表关键大气过程的数值天气预报（NWP）模式制作的。许多最强烈的降雨事件（以及因此产生的影响，如山洪暴发）是由活跃在相对较小尺度（公里至几十公里）上的对流过程产生的，但它们的形成也取决于较大和较小尺度的过程。在努力预测这些事件的过程中，操作中心继续推动最强大的高性能超级计算机的极限，以更精细的分辨率进行预测，从而可以明确地模拟千米尺度的过程。然而，这种策略本身并不足以产生更准确的降雨预报。千米尺度的NWP模式在一个我们称之为“硬灰色地带”的区域中运行，在那里单个对流风暴的水平尺度与数值模式网格相似。对流没有得到正确的解析，但也不能使用传统方法进行参数化，传统方法假设一个未解析的过程发生在比网格尺度小得多的尺度上。我们目前还没有明确的建模策略，因此面临着对流的时间、强度和空间模式的重大系统问题。为了实现千米尺度模拟的全部价值，我们的项目寻求在这些最有问题的尺度上改进对流过程的表示。我们的假设是，保留对流参数化是有用的，但关键方面必须重新考虑。我们特别针对云内垂直速度的表示，指定和使用自一致的键长度尺度的需要，以及未解决的可变性的表示。一个重要的指导原则是，物理过程在硬灰色区域之外有更好的约束表示，因此硬灰色区域内任何基于实体的物理表示都应该在适当的范围内与这些表示相匹配。关键长度尺度部分是为了确定正确的限制，部分是作为可变性的控制参数，部分是为了重新调整参数化的各个方面。云内垂直运动的表示对于匹配过程非常重要，也是确保与模型物理的其他方面适当耦合的关键。可变性的表示将是随机的，这是硬灰色地带的自然方法，其中参数化运动不完全受网格尺度变量的约束，因此非常不确定。我们将通过对2023年夏季英国观测现场活动的案例进行模拟，严格评估我们的模型发展。与观测结果的比较将帮助我们确定我们的发展是否出于正确的物理原因影响了模拟。我们的评估将借鉴新的诊断措施，旨在突出模型变化的高档影响。为了避免我们对这些情况的发展出现“过度拟合”的危险，我们还将评估东南亚热带对流的模拟。