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

有组织的深对流区在空间和时间上对其环境的影响已被认识多年。例如，已知有组织的深层对流区域通过非绝热加热改变急流入口区域的直接质量环流，从而增强了高层急流条纹。个别雷暴会在几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。虽然过去的观测和建模研究已经记录了这些近距离和更远的反馈效应，但这项研究首次尝试将模式模拟的对流反馈与中尺度可预测性实验（MPEX）期间从落差探空仪和微波温度剖面（MTP）观测中诊断出来的对流反馈进行仔细比较。数值天气预报（NWP）模式在对流允许的网格间距（1-4公里）上的能力的提高，以及NCAR GV航空观测系统的可用性，都有力地表明，现在是时候更详细地了解深层对流如何改变周围环境了。一个拥有广泛专业知识的多机构团队已经成立，以研究对流风暴的基本科学问题——环境反馈和可预测性。特别是，团队将寻求：1)量化观察到的环境变化和深层对流的高级反馈，并将其与对流特征联系起来；2)利用MPEX观测对模式模拟的深层对流高阶反馈进行评价；3)探讨对流扰动大气的可预测性。这些目标将通过应用于落差探空仪观测的各种诊断方法来实现，包括计算热量和水分预算；在允许对流的分辨率下采用集合卡尔曼滤波同化数据的数值模型模拟；以及MPEX观测和模型模拟之间的仔细比较。知识价值：这个项目的结果将有助于更好地理解对流风暴-环境反馈。来自深层对流的高级反馈将首次通过独特的MPEX观测仔细记录，该观测将围绕对流区域。通过集合卡尔曼滤波方法约束的模型分析，将允许对对流能力进行新颖的评估，允许模型模拟再现这些高级反馈。提高对对流扰动大气可预测性的认识，将为高影响对流天气事件的研究和操作数值天气预报模式的预报技能迅速下降提供新的见解。更广泛的影响：该项目的结果将产生对流区域内部和附近观测所需的新信息，以扩大数值模式对危险天气事件预报的可预测性。它还将提供关于高级反馈在当前深湿对流模式参数化中的表现，以及这可能如何影响季节性和更长的时间尺度的预测。研究成果将纳入教材并出版，使广大读者了解。三名研究生将通过参与MPEX数据收集、研究和教学活动以及参加会议和讲习班来接受培训。

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.