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.

深层潮湿对流的形成或对流的启动对其发生时的大气状态高度敏感。因此，准确预测对流起始事件的时间和位置对风暴规模的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起始的能力，从而减轻强烈对流事件经常带来的财产和生命损失。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目标天气到中观 α 尺度观测结果对允许对流的实际情况的影响，检验了对流起始的实际和内在可预测性。 -所选对流引发事件的数据数值模拟。研究可预测性如何响应对流前环境的更密集采样表示而演变，将增加对对流启动时天气到中观阿尔法尺度控制的基本理解。智力优点：利用两个集成卡尔曼滤波器初始化的集成，允许对流的真实数据数值模拟，一个包含 MPEX 观测结果，一个不包含 MPEX 观测结果，这项研究测试了以下假设：更密​​集的采样表示对流前大气状态足以提高 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。将利用概率、时间分档的空间验证方法来检验这一假设。利用“完美模型”和“完美观测”方法来研究三个 MPEX 密集观测周期的初始对流起始事件，每个周期的特点是不同的主要天气尺度流动模式、初始条件不确定性和变化的影响研究了基于对流引发的内在可预测性的亚网格尺度行星边界层过程的数值表示。在此过程中，这项研究将通过提供迫切需要的洞察力来扩大理解，了解当前观测限制对对流引发（本质上是多尺度的非线性物理过程）的可预测性所施加的限制。它将进一步阐明对天气和中观阿尔法尺度对对流启动的可预测性的控制，并确定影响其内在可预测性的较大尺度吸引子的存在。 更广泛的影响：深层、潮湿的对流通常会对无论是财产还是生命。通过开发更准确、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发生的能力。该研究提供的对对流起始的基本见解将提高我们预测深层潮湿对流的时间、位置和发生的能力。这些进步有望减少每年由于深层潮湿对流和相关现象而造成的重大财产和生命损失。可预测性、概率和不确定性的跨领域研究主题将通过举办题为“理解和交流大气科学中的概率和不确定性”的本科非专业荣誉研讨会来向非专业人士进行交流。参与研究的研究生将接受指导，了解其研究的内在社会意义，提供向不同的非专业受众传达研究成果的机会，并鼓励他们接受物理科学和社会科学相结合的培训。