Storm-Scale Predictability

风暴规模的可预测性

• 批准号: 0432232
• 负责人: Steven Mullen
• 金额: $ 28.71万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-11-15 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0432232&HistoricalAwards=false
• 关键词: Storm; Scale; Predictability

As computer power continues to increase, the grid spacing of numerical weather prediction models correspondingly will decrease to the point that cloud-resolving models will be run on a real-time basis. However, whereas large-scale predictability issues have been studied extensively, very little work has explored the nature of storm-scale predictability. The main objective of this research is to ascertain the length of time for which useful forecasts of various features of the atmosphere at these scales can be expected. These features range from the exact timing, location, mode, and intensity of storms to the nature of the environment (instability, shear, etc.) in which storms may form. The predictability limits for these features will be estimated from large ensemble runs. As a preliminary step the ensembles will be launched from an idealized, horizontally homogeneous control run so that storms may be isolated and the storm environment closely controlled to determine the impact of environmental characteristics on storm predictability. Following that, horizontally nonhomogeneous numerical simulations will also be performed as the interaction between storms and inhomogeneities in the storm environment are likely to affect predictability. All experiments will be performed using the perfect model assumption so that the results will not be limited by the current skill of high-resolution numerical models. These experiments will provide an upper bound on storm-scale predictability. Knowledge of the inherent predictability limits at these storms is important for directing future research toward physically tractable problems and for instructing forecasters on how to interpret the output of high-resolution numerical models.

随着计算机功率继续增加，数值天气预测模型的网格间距相应地减小到将实时运行云的模型。 但是，尽管已经对大规模可预测性问题进行了广泛的研究，但很少的工作探索了风暴规模可预测性的性质。 这项研究的主要目的是确定可以预期在这些尺度上对大气中各种特征有用的预测的时间。 这些特征范围从确切的时间，位置，模式和风暴强度到可能形成风暴的环境性质（不稳定性，剪切等）。 这些功能的可预测性限制将从大型集合运行中估算。 作为初步步骤，将从理想化的水平均匀控制运行中启动合奏，以便可以隔离风暴，并密切控制风暴环境，以确定环境特征对风暴可预测性的影响。 随后，由于风暴环境中风暴与不均匀性之间的相互作用可能会影响可预测性，因此还将进行水平非均匀的数值模拟。 所有实验都将使用完美的模型假设进行，以免结果高分辨率数值模型的当前技能限制。 这些实验将在风暴规模的可预测性上提供上限。 了解这些风暴中固有的可预测性限制对于将未来的研究引导到物理上可牵引的问题以及指导预报员如何解释如何解释高分辨率数值模型的输出。