Dynamics of Rotation and Scale Selection in Deep Convective Storms

这项研究的重点是研究深部对流风暴动力学，重点研究三个相互关联但科学上截然不同的问题。第一个问题是一些强(超级单体)风暴以相当规则的间隔发展成一系列中气旋的趋势。在真实风暴中观察到了这种“循环中尺度生成”，并在一些数值模拟中注意到了这一点。建议确定划分周期性风暴和非周期性风暴的条件(环境条件和风暴引起的条件)。此外，首席调查员将把他们以前的工作扩展到龙卷风尺度，以研究周期性龙卷风的发生，重点是龙卷风涡旋环流和母体中气旋环流之间的多尺度联系。第二项研究试图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，在切变和/或稳定性方面存在较大的水平和/或时间变化的环境中移动。虽然已知环境变化在风暴生命周期中起着关键作用，但以前的云模型模拟利用了水平均匀的基态环境。首席调查员将通过进一步调查与风暴环境转变有关的动力学，并结合不稳定和切变的变化以及在环境中施加时间变化，来扩展先前的研究。最后，首席研究人员将在螺旋度动力学和浅对流和深对流的湍流性质方面的最新工作的基础上，努力了解深对流风暴中尺度选择、组织和可预报性的动力学。在以前的研究中，模拟的不同初始强迫尺度的深对流被用来确定对流风暴是在初始强迫的空间尺度上演变，还是演变到其他更好的尺度。这些“尺度强迫”模拟与其他结果进行了比较，在这些结果中，对流是由随机扰动场引发的。基于光谱和其他分析技术的初步发现证实，大多数超级单体风暴中的上升气流往往比不那么严重的风暴的上升气流规模大得多。当最初的风暴以特定的空间尺度被强迫时，它们往往会保留该尺度的记忆，保留时间与强迫的尺度成正比。即使在比“自然或首选尺度”大得多的尺度上强迫，超级单体风暴最终也会形成比弱切变或零切变风暴更大的尺度。首席调查员将扩展模拟数据集，重点确定气流最重要的理论长度尺度，并将其结构和能量与模拟的风暴行为联系起来。此外，首席调查员将通过对误差增长和在不同尺度之间传播的系统分析，试图了解风暴和风暴系统可预测性的基本界限。后者对于未来的业务预测模型特别重要。

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.