Numerical Assessment of the Practical and Intrinsic Predictability of Warm-Season Convection Initiation Using Mesoscale Predictability Experiment (MPEX) Data

深层潮湿对流的形成或对流的启动对其发生的大气状态高度敏感。因此，准确预测对流起生事件的时间和位置是风暴尺度数值模拟的一个巨大挑战。本研究的最终目标是提高对流开始的预测能力，并随后减轻经常伴随强对流事件的财产和生命损失。为此，本研究通过评估中尺度可预测性实验（MPEX）在对流前环境中获得的有针对性的天气尺度到中尺度观测对选定对流启动事件的允许对流的实际数据数值模拟的影响，考察对流启动的实际和内在可预测性。研究可预测性是如何响应对流前环境的更密集采样表示而演变的，将增加对对流开始时天气尺度到中尺度控制的基本理解。知识价值：利用两组集成卡尔曼滤波初始化、对流允许的实际数据数值模拟，一个包含MPEX观测，另一个不包含MPEX观测，本研究验证了一个假设，即对流前大气状态的更密集采样表示足以提高MPEX采样事件范围内原始对流起始时间和位置的实际可预测性。概率的，时间分类的空间验证方法将被用来检验这一假设。利用“完美模式”和“完美观测”的方法研究了MPEX三个密集观测期的初始对流起爆事件，每个观测期都具有不同的天气尺度主流流型特征，研究了亚网格尺度行星边界层过程数值表示中的初始条件不确定性和变率对对流起爆内在可预测性的影响。在这样做的过程中，这项研究将通过提供急需的洞察力来扩大对对流开始的可预测性的限制的理解，对流开始是一个固有的多尺度，非线性物理过程。它将进一步阐明天气尺度和中尺度对对流开始的可预测性的控制，并确定影响其内在可预测性的更大尺度吸引子的存在。更广泛的影响：深层潮湿对流通常会对财产和生命造成重大影响。通过发展更准确、更长期的深层潮湿对流预报来减轻这些影响的能力取决于我们更好地预测其开始的能力。这项研究提供的对对流起始的基本认识将使我们能够预测深层潮湿对流的时间、位置和发生。这些进步提供了减少由于每年发生的深层潮湿对流和相关现象而造成的重大财产和生命损失的希望。可预测性、概率和不确定性的交叉研究主题将通过一个名为“理解和传达大气科学中的概率和不确定性”的本科生、非专业荣誉研讨会的发展，传达给非专业人士。参与研究的研究生将得到指导，了解其研究的内在社会意义，提供机会向不同的非专业受众交流研究成果，并鼓励他们接受整合自然科学和社会科学的培训。

The formation of deep, moist convection, or convection initiation, is highly sensitive to the atmospheric state in which it occurs. Consequently, accurately predicting the timing and location of convection initiation events poses a formidable challenge for storm-scale numerical simulations. The ultimate goal of this research is to improve the ability to predict convection initiation and, subsequently, mitigate the loss of property and life that often accompany intense convective events. To that end, this study examines the practical and intrinsic predictability of convection initiation by assessing the impact of targeted synoptic- to meso-alpha-scale observations obtained in the pre-convective environment by the Mesoscale Predictability Experiment (MPEX) upon convection-permitting real-data numerical simulations of selected convection initiation events. Investigating how predictability evolves in response to a more intensively-sampled representation of the pre-convective environment will increase fundamental understanding regarding the synoptic- to meso-alpha-scale controls upon convection initiation.Intellectual Merit: Utilizing two ensembles of Ensemble Kalman filter-initialized, convection-permitting real-data numerical simulations, one incorporating MPEX observations and one not, this research tests the hypothesis that a more intensively-sampled representation of the pre-convective atmospheric state is sufficient to improve the practical predictability of pristine convection initiation timing and location over the range of events sampled by MPEX. Probabilistic, temporally-binned spatial verification methods will be utilized to test this hypothesis. Utilizing "perfect model" and "perfect observations" approaches applied to the study of the initial convection initiation event from three MPEX intensive observation periods, each characterized by a different prevailing synoptic-scale flow pattern, the influences of initial condition uncertainty and variability in the numerical representation of sub-grid-scale planetary boundary layer processes upon the intrinsic predictability of convection initiation are examined. In so doing, this research will expand understanding through the provision of critically-needed insight into the limits imposed by current observational constraints upon the predictability of convection initiation, an inherently multi-scale, non-linear physical process. It will further illuminate the controls upon the predictability of convection initiation exerted by the synoptic- and meso-alpha-scales and identify the presence of larger-scale attractors that influence its intrinsic predictability.Broader Impacts: Deep, moist convection routinely poses significant impacts to both property and life. The ability to mitigate these impacts through the development of more accurate, longer-lead forecasts of deep, moist convection hinges upon our ability to better predict its initiation. Basic insight into convection initiation provided by the research will lead to advances in our ability to predict the timing, location, and occurrence of deep, moist convection. Such advances offer the promise of reducing the substantial losses of property and life due to deep, moist convection and associated phenomena incurred annually. The cross-cutting research themes of predictability, probability, and uncertainty will be communicated to non-specialists through the development of an undergraduate, non-majors Honors seminar titled "Understanding and Communicating Probability and Uncertainty in the Atmospheric Sciences." Graduate students involved with the research will be mentored as to the inherent societal significance of their research, afforded opportunities to communicate research findings to diverse, non-specialist audiences, and encouraged to acquire training in integrating the physical and social sciences.