Improving the Understanding and Prediction of Nocturnal Convection through Advance Data Assimilation and Ensemble Simulations for Plains Elevated Convection At Night (PECAN)

通过平原夜间高对流 (PECAN) 的高级数据同化和集合模拟来提高对夜间对流的理解和预测

• 批准号: 1359703
• 负责人: Xuguang Wang
• 金额: $ 60.21万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1359703&HistoricalAwards=false
• 关键词: Improving; Understanding; Prediction; Nocturnal; Convection

Researchers have long established that summer thunderstorms and convective precipitation are most frequent not in the afternoon, but after sunset over a large swath of the Great Plains of North America. Nocturnal rainfall is critical to the hydrology and agriculture of this region, and nocturnal mesoscale organized convection is often accompanied by severe weather. However, a conceptual framework describing how nocturnal convection initiates and evolves at night is still lacking, and the prediction of nocturnal convection still remains a scientific and technical challenge. To improve both our understanding of and our ability to forecast nocturnal convection, the Plains Elevated Convection At Night (PECAN) field experiment is planned to occur across portions of Oklahoma, Kansas and Nebraska from June 1 to 15 July 2015. PECAN promises to collect unprecedented observations that capture the multi-scale aspects of nocturnal convection. Such data will not only advance the understanding of key processes associated with nocturnal convection through observational analysis , but also will enable investigators to use advanced data assimilation (DA) and ensemble modeling systems to significantly advance both the understanding and prediction of nocturnal convective systems. This research focuses on the prediction component of PECAN, but also makes major contributions to the other three components of PECAN. Specifically, this research seeks funding to : 1) Support the dry run and field-phase of PECAN through generation of real-time convection-permitting ensemble forecasts initialized using an advanced ensemble based DA and ensemble forecast system; 2) Assimilate special PECAN observations including ground-based remote sensing and compare these simulations against the real-time simulations and the observations to assess the role that the unique PECAN observations play in improving predictive skill; 3) Comprehensively diagnose the ensemble analyses and simulations to determine the dynamical and physical processes that initiate and maintain nocturnal convection and control their structure and evolution; 4) Comprehensively evaluate ensemble analyses and forecasts against special PECAN observations to determine the appropriate modeling strategies (e.g., grid spacing; physical parameterization) for improved prediction.Intellectual Merit :This effort will advance our knowledge of the processes that control the initiation, maintenance, evolution and structure of elevated nocturnal convective systems. These goals will be accomplished through a focus on the next generation of convection-permitting models and advanced DA techniques. The simulations under this project will play an important role in bridging between efforts of other PECAN investigators focused on observational analysis of PECAN data and on idealized modeling. This effort will also address the inherent role that bores and stable layer disturbances play in the maintenance of elevated nocturnal systems through analysis of simulations and comparison against observations. Ensemble diagnostics will allow for a novel assessment of the capability of a convection-permitting model simulation to reproduce these key processes. The research findings will provide new insights into advancing DA strategies, model physics and observing strategies of high impact convective weather events.Broader Impacts :This research will address one of the most important goals of weather research - to improve our ability to accurately predict intense hazardous weather that causes large monetary loss, injuries and fatalities. It will directly address three established national priorities in weather: Warn on Forecast for High Impact Weather, the Next-Generation Forecast System, as key component of the Next-Generation Air Transportation System, and Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. It will provide much needed education and training in the areas of advanced DA and high-resolution ensemble modeling and numerical prediction and expose the next generation of modeling and DA experts to a major scientific field experiment. The research findings have a direct path to operations because the ensemble DA system to be used is developed based on the new US operational DA system, and because of the significant roles of the PIs in and/or linkage of the PIs have made with NOAA National Severe Storm Lab (NSSL), National Centers for Environmental Prediction (NCEP) Storm Prediction Center (SPC) and Environmental Modeling Center (EMC).

研究人员早已确定，在北美大平原的大片地区，夏季雷暴和对流降水最频繁的不是下午，而是日落之后。夜间降雨对该地区的水文和农业至关重要，夜间中尺度有组织对流往往伴随着恶劣天气。然而，仍然缺乏描述夜间对流如何在夜间启动和演化的概念框架，夜间对流的预测仍然是一个科学和技术挑战。为了提高我们对夜间对流的了解和预测能力，计划于 2015 年 6 月 1 日至 7 月 15 日在俄克拉荷马州、堪萨斯州和内布拉斯加州的部分地区进行平原夜间高对流 (PECAN) 现场实验。PECAN 承诺收集前所未有的数据捕捉夜间对流的多尺度方面的观测。这些数据不仅可以通过观测分析促进对与夜间对流相关的关键过程的理解，而且还使研究人员能够使用先进的数据同化（DA）和集合建模系统来显着促进对夜间对流系统的理解和预测。本研究重点关注 PECAN 的预测组件，但也对 PECAN 的其他三个组件做出了重大贡献。具体来说，本研究寻求资金以： 1) 通过使用基于先进集合的 DA 和集合预报系统初始化实时允许对流的集合预报，支持 PECAN 的试运行和现场阶段； 2）同化包括地面遥感在内的特殊PECAN观测结果，并将这些模拟与实时模拟和观测结果进行比较，以评估独特的PECAN观测结果在提高预测能力方面所发挥的作用； 3）综合诊断系综分析和模拟，以确定引发和维持夜间对流并控制其结构和演化的动力和物理过程； 4) 根据特殊的 PECAN 观测结果全面评估集合分析和预测，以确定适当的建模策略（例如，网格间距；物理参数化）以改进预测。智力价值：这项工作将增进我们对控制启动、维护、夜间高对流系统的演化和结构。这些目标将通过关注下一代对流允许模型和先进的 DA 技术来实现。该项目下的模拟将在其他 PECAN 研究人员专注于 PECAN 数据观测分析和理想化建模的努力之间发挥重要作用。这项工作还将通过模拟分析和与观测结果的比较来解决钻孔和稳定层扰动在维持高架夜间系统中所发挥的固有作用。集成诊断将允许对允许对流模型模拟的能力进行新颖的评估，以重现这些关键过程。研究结果将为推进 DA 策略、模型物理和高影响对流天气事件的观测策略提供新的见解。 更广泛的影响：这项研究将解决天气研究最重要的目标之一 - 提高我们准确预测强烈危险的能力造成大量金钱损失、受伤和死亡的天气。它将直接解决三个既定的国家天气优先事项：高影响天气预报警告、下一代预报系统（作为下一代航空运输系统的关键组成部分）以及从头开始观测天气和气候：全国范围网络的网络。它将提供先进的 DA 和高分辨率集合建模和数值预测领域急需的教育和培训，并使下一代建模和 DA 专家参与重大科学现场实验。研究结果具有直接的应用途径，因为所使用的集合 DA 系统是基于美国新的可操作 DA 系统开发的，并且由于 PI 与 NOAA 国家机构之间的重要作用和/或 PI 的联系强风暴实验室 (NSSL)、国家环境预测中心 (NCEP) 风暴预测中心 (SPC) 和环境模拟中心 (EMC)。