Storm-Scale Predictability

随着计算机能力的不断增强，数值天气预报模式的网格间距将相应减少，从而使云解析模式能够实时运行。然而，虽然大范围的可预报性问题已经被广泛研究，但很少有工作探索风暴规模可预报性的本质。这项研究的主要目的是确定在多长时间内可以预期在这些尺度上对大气的各种特征进行有用的预报。这些特征从风暴的确切时间、位置、模式和强度到环境的性质(不稳定、切变等)。其中可能会形成风暴。这些特征的可预测性界限将从大型集合运行中估计出来。作为初步步骤，集合将从理想化的、水平均匀的控制运行中发射，以便可以隔离风暴并严密控制风暴环境，以确定环境特征对风暴可预报性的影响。随后，还将进行水平非均匀数值模拟，因为风暴和风暴环境中的非均匀之间的相互作用可能会影响可预报性。所有的实验都将使用完美的模型假设进行，这样结果就不会受到当前高分辨率数值模式技术的限制。这些实验将提供风暴规模可预测性的上限。了解这些风暴的内在可预测性极限，对于指导未来的研究转向物理上容易处理的问题，以及指导预报员如何解释高分辨率数值模式的输出，是很重要的。

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.