Warm Season Convective Weather Systems over the Great Lakes and Vicinity

这项研究的重点是继续研究有组织的对流天气系统，主要发生在温暖的季节在附近的五大湖。 具体来说，这项研究的目标是中尺度对流系统（MCS）和嵌入式中尺度对流涡（MCV）以及强烈的准线性对流系统称为derechos。 Derecho经常出现在业务多普勒雷达图像的弓形线段。 Derechos和MCV是弓形回波和中尺度对流涡旋实验（BAMEX）的主要焦点。 该研究基于BAMEX的机会。 具体研究任务包括：1）确定与MCV和触发高层扰动相关的涡度场如何在深对流开始后重新组织和向上扩展，2）评估MCS/derecho生命周期对触发高层强迫扰动结构的敏感性以及预先存在和对流驱动的地面边界相互作用的范围，3）通过新的诊断分析研究MCV/MCS/derecho结构和生命周期，这些分析利用现有的更高空间和时间分辨率的网格数据集4）采用最先进的天气研究和预报（WRF）模式来模拟MCV的生命周期，并将MCV的发展与热带气旋的初期发展进行比较，5）对真实的和理想化的MCS/derecho事件进行WRF模式模拟，以评估在什么时候大尺度强迫变得不重要，以及风暴尺度动力学何时成为主导，6）扩大正在进行的研究工作，旨在更好地了解控制五大湖上空暖季恶劣天气（对流）事件的物理机制。业务预报中心提供的高分辨率、全球、网格化分析将促进研究。 还将利用诺阿区域更新周期系统的13公里网格分析。 北美上空每小时地面和高空RUC分析的可用性将允许对长寿命MCV和derechos的生命周期进行调查。 这些分析与特殊的BAMEX数据集和WRF模式的模拟相结合，将允许通过多尺度案例研究对重要的对流天气事件进行比以前更全面的研究调查。就更广泛的影响而言，现有的天气预测模型在正确模拟对流模式和生命周期方面做得相对较差。 在这方面，业务天气预报模型在预报恶劣天气方面受到限制。 研究结果将增加对恶劣天气事件的新知识。在本项目下接受培训和支助的研究生将在毕业后从事该领域的职业时促进研究知识向业务的转移。 该项目还提供了与各种政府机构合作的机会（例如，NOAA风暴预测中心）和其他教育机构，以帮助促进研究知识的业务和学生的转移。

The focus of this research is on continued studies of organized convective weather systems that occur primarily during the warm season in the vicinity of the Great Lakes. Specifically, the research is targeted at mesoscale convective systems (MCS) and embedded mesoscale convective vortices (MCV) as well as intense quasi-linear convective systems know as derechos. Derechos often appear as bowing line segments in operational Doppler radar imagery. Derechos and MCVs were the major focus of the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX). The research builds on opportunities from BAMEX. Specific research tasks include: 1) establish how the vorticity fields associated with the MCV and the triggering upper-level disturbance reorganize and scale upward after deep convection begins, 2) assess MCS/derecho life cycle sensitivity to the structure of the triggering upper-level forcing disturbances and the extent of surface boundary, pre-existing and convectively driven, interactions, 3) investigate MCV/MCS/derecho structure and life cycles through new diagnostic analyses that take advantage of available higher spatial and temporal resolution gridded datasets 4) employ the state-of-the-art Weather Research and Forecasting (WRF) model to simulate MCV life cycles and compare MCV development with incipient tropical cyclone development, 5) perform WRF model simulations of real and idealized MCS/derecho events to assess at what point larger scale forcing becomes unimportant and when storm-scale dynamics becomes dominant, and 6) expand an ongoing research effort targeted at developing a better understanding of the physical mechanisms that govern warm season severe weather (convection) events that occur over the Great Lakes. The research will be facilitated by the availability of high resolution, global, gridded analyses from operational forecast centers. Advantage will also be taken of the gridded 13 km analyses form the NOAA Regional Update Cycle (RUC) system. The availability of hourly surface and upper air RUC analyses over North America will permit an investigation of the life cycles of long-lived MCVs and derechos. These analyses when coupled with the special BAMEX datasets and simulations from the WRF model will permit more comprehensive research investigations of important convective weather events through multiscale case studies than has been previously possible. In terms of broader impacts, existing weather prediction models do a relatively poor job of simulating convective mode and life cycles properly. In this regard, the operational weather prediction models are limited in how well they can forecast severe weather. The results from the research will add to new knowledge of severe weather events. The graduate students trained and supported under this project will facilitate the transfer of research knowledge to operations as they pursue careers in the field after graduation. This project also presents the opportunity to work with various government agencies (e.g., the NOAA Storm Prediction Center) and other educational institutions to help facilitate the transfer of research knowledge to operations and students.