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

使用中尺度可预测性实验（MPEX）数据对暖季对流引发的实际和内在可预测性进行数值评估

• 批准号: 1347545
• 负责人: Allen Evans
• 金额: $ 45.62万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1347545&HistoricalAwards=false
• 关键词: Numerical; Assessment; Practical; Intrinsic; Predictability

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.

深层湿对流的形成或对流的起始对它所处的大气状态非常敏感。因此，准确预测对流起始事件的时间和位置对风暴尺度数值模拟提出了巨大的挑战。这项研究的最终目标是提高预测对流开始的能力，并随后减轻经常伴随强对流事件的财产和生命损失。为此，本研究探讨了实际的和内在的可预测性的对流启动评估的影响，有针对性的天气到中α尺度观测对流前环境中的中尺度可预报性实验（MPEX）对流允许的真实数据数值模拟选定的对流启动事件。调查可预报性如何演变以响应对流前环境的更密集采样表示，将增加对对流开始时天气到中α尺度控制的基本理解。利用两个集合的Ensemble卡尔曼滤波器初始化，对流允许的真实数据数值模拟，一个包含MPEX观测，一个不包含，这项研究检验了这样一个假设，即对流前大气状态的更密集采样的表示足以在MPEX采样的事件范围内提高原始对流开始时间和位置的实际可预测性。可能会使用时间分组的空间验证方法来检验该假设。利用“完美模式”和“完美观测”的方法，适用于研究的初始对流起始事件从三个MPEX密集观测期间，每个特征在于不同的流行天气尺度流型，初始条件的不确定性和变异的影响，在数值表示的亚网格尺度行星边界层过程对流起始的内在可预测性进行检查。在这样做的过程中，这项研究将扩大理解，通过提供迫切需要的洞察力的限制，目前的观测约束对流启动的可预测性，一个固有的多尺度，非线性的物理过程。它将进一步阐明对流开始施加的天气和中α尺度的可预测性的控制，并确定存在较大尺度的吸引子，影响其内在的predictability.Broader Impacts：深，潮湿的对流通常会对财产和生活造成重大影响。通过发展更准确，更长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启动的能力。这项研究提供的对流开始的基本见解将导致我们预测深层潮湿对流的时间、位置和发生的能力的进步。这些进展有望减少每年发生的深层潮湿对流和相关现象造成的重大财产和生命损失。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“理解和交流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被指导其研究的内在社会意义，提供机会将研究结果传达给不同的非专业受众，并鼓励他们接受整合物理和社会科学的培训。