Dynamics of Rotation and Scale Selection in Deep Convective Storms

本研究主要集中于研究深层对流风暴动力学，并着重于三个相关但科学上不同的问题。第一个问题涉及一些严重的（超级单体）风暴以相当规律的间隔发展一连串中气旋的趋势。这种“循环中气旋形成”已经在真实的风暴中被观察到，并在一些数值模拟中被注意到。提出了识别条件（环境和风暴诱发），划定周期和非周期风暴。此外，主要研究人员将把他们以往的工作扩展到龙卷风尺度，研究龙卷风的循环形成，重点是龙卷风涡旋环流与母中气旋环流之间的多尺度联系。第二项研究旨在了解风暴和风暴系统的动力学，这些风暴和风暴系统在其全部或部分生命周期中，在包含剪切和/或稳定性的大水平和/或时间变化的环境中移动。虽然已知环境变率在风暴生命周期中起着关键作用，但以前的云模式模拟使用的是水平均匀的基态环境。首席研究员将通过进一步调查与风暴环境转变相关的动力学，并结合不稳定性和切变的变化以及环境中施加的时间变化来扩展先前的研究。此外，将对模式结果与实际风暴进行比较，结果表明，由于环境的可变性，风暴的行为发生了明显的变化。最后，首席研究员将以他们最近在螺旋动力学和浅对流和深对流湍流性质方面的工作为基础，努力理解深对流风暴中尺度选择、组织和可预测性的动力学。在以往的研究中，通过模拟不同初始强迫尺度下的深层对流来确定对流风暴是在初始强迫的空间尺度上演化，还是演化到其他一些优选尺度上。这些“尺度强迫”模拟与其他由随机扰动场引发对流的结果进行了比较。基于光谱和其他分析技术的初步发现证实，在大多数超级单体风暴中，上升气流的规模往往比不那么严重的同类风暴大得多。当初始风暴在特定的空间尺度上被强迫时，它们倾向于保留该尺度的记忆，其保留时间与强迫的尺度成正比。即使在比“自然或首选尺度”大得多的尺度上，超级单体风暴最终也会比在弱切变或零切变条件下形成的风暴更大。首席研究员将扩展模拟数据集，重点是确定最重要的理论流长度尺度，并将其结构和能量学与模拟风暴行为联系起来。此外，首席研究人员将通过对尺度间误差增长和传播的系统分析，寻求了解风暴和风暴系统可预测性的基本限制。后者对于未来的业务预测模型尤其重要。

This research is focussed on study of deep convective storm dynamics with emphasis on three related yet scientifically distinct problems. The first concerns the tendency for some severe (supercell) storms to develop a succession of mesocyclones at fairly regular intervals. This "cyclic mesocyclogenesis" has been observed in real storms and noted in some numerical simulations. It is proposed to identify the conditions (both environmental and storm-induced) that delineate cyclic from non-cyclic storms. In addition, the Principal Investigators will extend their previous work down to the tornado scale to study cyclic tornadogenesis with emphasis on multi-scale linkages between the tornado vortex circulation and that of the parent mesocyclone. The second study seeks to understand the dynamics of storms and storm systems which, during all or part of their lifetime, move though environments comprising large horizontal and/or temporal variations in shear and/or stability. Although environmental variability is known to play a key role in storm lifecycles, previous cloud model simulations have utilized a horizontally uniform base state environment. The Principal Investigators will extend prior research by further investigating the dynamics associated with storm environmental transitions and, by combining variations in instability and shear as well as imposing temporal variations in the environment. Additionally, comparisons will be drawn between model results and real storms, which demonstrated clear changes in behavior due to environmental variability.Finally, the Principal Investigators will build upon their recent work in helicity dynamics and the turbulent nature of both shallow and deep convection in an effort to understand the dynamics of scale selection, organization and predictability within deep convective storms. In prior research, simulated deep convection at various scales of initial forcing were used to determine whether convective storms evolve at the spatial scale of the initial forcing or instead evolved to some other preferred scale. These "scale-forced" simulations were compared to other results in which convection was initiated by a field of random disturbances. Preliminary findings, based on spectral and other analysis techniques, confirm that updrafts in the most supercell storms tend to be considerably larger in size then their less severe counterparts. When the initial storms are forced at a particular spatial scale, they tend to retain a memory of that scale, with the retention time proportional to the scale of forcing. Even when forced at scales much larger than the "natural or preferred scale," the supercell storms eventually settle into a scale larger than storms forced in weaker or zero shear. The Principal Investigators will expand the simulation data set with emphasis on identifying the most significant theoretical length scales of the flow and relating their structure and energetics to simulated storm behavior. Further, the Principal Investigators will seek to understand fundamental limits to storm and storm system predictability via a systematic analysis of error growth and propagation among scales. The latter is of particular importance to future operational prediction models.