Collaborative Research: Improved Understanding of Convective-Storm Predictability and Environment Feedbacks from Observations during the Mesoscale Predictability Experiment (MPEX)

合作研究：提高对中尺度可预测性实验（MPEX）期间观测的对流风暴可预测性和环境反馈的理解

• 批准号: 1542312
• 负责人: Robert Trapp
• 金额: $ 27.51万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2014-08-16 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1542312&HistoricalAwards=false
• 关键词: Collaborative; Research; Improved; Understanding; Convective

The influence of organized regions of deep convection on its environment in both space and time has been recognized for many years. For example, organized deep convective regions are known to enhance upper-level jet streaks through modification of the direct mass circulation in jet entrance regions through diabatic heating. Individual thunderstorms modify the nearby surrounding mass and momentum fields within a few hours, likely assisting in storm maintenance and influencing storm severity. While past observational and modeling studies have documented these nearby and more distant feedback effects, this research represents the first attempt to conduct a careful comparison of model-simulated convective feedbacks with those diagnosed from dropsonde and Microwave Temperature Profiling (MTP) observations taken during the Mesoscale Predictability Experiment (MPEX). The improved capability of numerical weather prediction (NWP) models at convection-allowing grid spacing (1-4 km), and the availability of the NCAR GV airborne observing systems, argues strongly that it is time to understand how deep convection modifies the surrounding environment in much greater detail.A multi-institutional team with broad expertise has been assembled to pursue the fundamental scientific questions of convective storm-environmental feedbacks and predictability. In particular, the team will seek to: 1) quantify the observed environmental modifications and upscale feedbacks from deep convection, and relate these back to the characteristics of the convection; 2) evaluate model simulations of upscale feedbacks from deep convection with MPEX observations; and 3) explore the predictability of convectively disturbed atmospheres. These objectives will be met using various diagnostic approaches applied to the dropsonde observations, including calculation of heat and moisture budgets; numerical model simulations with ensemble Kalman filter data assimilation at convection-allowing resolutions; and careful comparisons between MPEX observations and model simulations.Intellectual merit: Results from this project will lead to a much better understanding of the convective storm-environmental feedback. Upscale feedbacks from deep convection will be documented carefully for the first time with the unique MPEX observations that will surround the convective region. Model analyses, constrained via the ensemble Kalman filter approach, will allow for a novel assessment of the capability of a convection-allowing model simulation to reproduce these upscale feedbacks. Improved understanding of the predictability of convectively disturbed atmospheres will provide new insight into the rapid decrease of forecast skill in research and operational numerical weather prediction models of high-impact convective weather events.Broader impacts: Results from this project will yield new information on the observations needed within and nearby convective regions to extend the predictability of numerical model forecasts of hazardous weather events. It will also provide insight on how well upscale feedbacks are represented in current model parameterizations of deep moist convection, and how this might affect predictions on seasonal and longer time scales. Research results will be integrated into teaching materials and published to reach broad audiences. Three graduate students will be trained through participation in MPEX data collection, research and teaching activities, and participation at conference and workshops.

深对流的有组织区域在空间和时间上对其环境的影响已被认识多年。 例如，已知有组织的深对流区通过非绝热加热改变喷流入口区的直接物质环流，从而增强上层喷流条纹。 个别雷暴会在数小时内改变附近的质量和动量场，可能有助于风暴的维持和影响风暴的严重程度。 虽然过去的观测和模拟研究已经记录了这些附近和更远的反馈效应，但这项研究代表了第一次尝试将模型模拟的对流反馈与中尺度可预报性实验（MPEX）期间从下投式探空仪和微波温度廓线（MTP）观测中诊断的对流反馈进行仔细比较。 数值天气预报（NWP）模式在允许对流的网格间距（1-4 km）上的改进能力，以及NCAR GV机载观测系统的可用性，强烈认为，现在是更详细地了解深对流如何改变周围环境的时候了。一个具有广泛专业知识的多机构团队已经组建起来，以追求对流风暴的基本科学问题-环境反馈和可预测性。 特别是，该团队将寻求：1）量化观察到的环境变化和深对流的高档反馈，并将其与对流的特征联系起来; 2）评估MPEX观测的深对流高档反馈的模型模拟; 3）探索对流扰动大气的可预测性。 这些目标将通过应用于下投式探空仪观测的各种诊断方法来实现，包括计算热量和水分收支;在允许对流的分辨率下用集合卡尔曼滤波数据同化进行数值模型模拟;以及仔细比较MPEX观测和模型模拟。 来自深对流的大尺度反馈将首次通过围绕对流区域的独特MPEX观测进行仔细记录。 模型分析，约束通过集合卡尔曼滤波方法，将允许一个新的评估能力的对流允许模型模拟再现这些高档反馈。 提高对流扰动大气的可预测性的理解将提供新的洞察力的快速下降的预测技能的研究和业务数值天气预报模型的高影响力对流天气事件。更广泛的影响：从这个项目的结果将产生新的信息的观测需要内和附近的对流区域，以扩大可预测性的数值模型预测的危险天气事件。 它还将提供深入了解如何以及高档反馈代表在当前模式参数化的深层湿对流，以及这可能会影响预测的季节性和较长的时间尺度。 研究成果将纳入教材并出版，以广泛传播。 三名研究生将通过参加MPEX数据收集、研究和教学活动以及参加会议和讲习班接受培训。