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: 1230114
• 负责人: Michael Coniglio
• 金额: $ 36.79万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230114&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.

多年来，人们已经认识到有组织的深对流区域在空间和时间上对其环境的影响。例如，已知有组织的深对流区通过非绝热加热改变了急流入口区的直接质量循环，从而增强了高层急流条纹。个别雷暴在几小时内改变了附近的质量场和动量场，可能有助于风暴的维持并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近距离和更远的反馈效应，但这项研究是首次尝试将模式模拟的对流反馈与在中尺度可预报性实验(MPEX)期间使用的落差探空仪和微波温度廓线(MTP)观测所诊断的对流反馈进行仔细比较。在对流允许网格间距(1-4公里)的情况下，数值天气预报(NWP)模式能力的提高，以及NCAR GV机载观测系统的可用性，有力地证明了现在是时候更详细地了解深对流如何改变周围环境了。一个具有广泛专业知识的多机构小组已经聚集在一起，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，该小组将寻求：1)将观测到的环境变化和深对流的高层反馈量化，并将这些与对流的特征联系起来；2)利用MPEX观测来评估对深对流的高层反馈的模式模拟；以及3)探索对流扰动大气的可预测性。这些目标将通过应用于水滴探空仪观测的各种诊断方法来实现，包括计算热量和湿度预算；在允许对流的情况下使用集合卡尔曼滤波数据同化进行数值模式模拟；以及在MPEX观测和模式模拟之间进行仔细的比较。来自深对流的高级反馈将首次通过围绕对流区域的独特的MPEX观测进行仔细记录。通过集合卡尔曼滤波方法约束的模式分析，将允许对对流能力的新评估-允许模式模拟重现这些高级反馈。对对流扰动大气可预报性的更好理解，将为研究和运行高对流天气事件的数值天气预报模式的预报技能迅速下降提供新的见解。广泛影响：本项目的结果将产生关于对流区域内和附近所需观测的新信息，以扩大对危险天气事件的数值模式预报的可预报性。它还将提供关于高层反馈在当前深湿对流的模式参数化中的表现如何的洞察，以及这可能如何影响季节性和更长时间尺度的预测。研究成果将被整合到教材中并出版，以满足广大受众的需求。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对三名研究生进行培训。