High Plains Convection: Diurnally Varying Mesoscale-Synoptic Scale Interactions over Complex Terrain during the Mesoscale Predictability Experiment (MPEX)

这项研究旨在确定大气结构和物理特征，与初始和底部边界条件的不确定性可能会限制0-24小时的预测预报的对流天气现象和东部的落基山脉在中尺度可预报性实验（MPEX）将于2013年春末夏初举行。MPEX将把特殊的数据集纳入高分辨率对流，使中尺度模式能够测试减少0-24小时范围内对流风暴预报不确定性的方法。拟针对的具体大气结构和物理特征包括：1）对流层上层次天气/中尺度扰动，2）高层和低空急流，3）三维水分羽流，4）边界层水分舌，5）对流层低层和中层剪切廓线，6）大气稳定性，表现为中层直减率的陡度，7）低层热量和水分边界，和低层相对涡度带; 8）土壤含水量和土地利用特征。PI将积极参与MPEX的预报部分，将寻求确定潜在的大气结构和物理特征，这些结构和物理特征可用于额外的下投式探空仪观测和大气可预测性研究所需的微波温度廓线数据覆盖。一个主要的研究重点将是在MPEX期间对各种选定的对流天气系统进行天气学和中尺度分析，以帮助评估深对流风暴及其环境之间的反馈，因为已知有组织的深对流的大规模影响会改变下游上层气流（例如，射流/脊结构）。这些基于物理的风暴分析将被用来帮助评估模式的可预测性与其他MPEX主要研究人员合作。智力价值：这个项目将提高我们的动力学和热力学过程的理解，合谋限制大气的可预测性一个子集的对流天气现象，发生在复杂的地形和内在的难以预测。这个项目的结果应该有助于建立对流天气现象的误差增长率的界限。该项目还将提供一个机会，合作的研究工作之间的观测和建模，以评估在何种程度上主观派生的功能为基础的不确定性估计可以在大气可预测性study.Broader影响：从这项研究的结果将有应用程序的预测界和公众。 我们的核心知识存在重大差距，对流天气系统如何发展，组织，成熟和消散在落基山脉和高平原的复杂地形。在本研究的实地阶段获得的增强数据集应使研究人员能够对对流风暴现象有更深入的科学了解。这项研究的结果将广泛分发给研究和业务界。研究生将接受中尺度分析、对流风暴动力学和大气可预测性研究方面的培训。

This research seeks to identify atmospheric structures and physical features for which uncertainties associated with initial and bottom boundary conditions are likely to limit the 0-24 h forecast predictability of convective weather phenomena over and east of the Rockies during the Mesoscale Predictability Experiment (MPEX) to be held in late spring and early summer 2013. MPEX will incorporate special datasets into high-resolution convection-allowing mesoscale models to test ways to reduce convective storm forecast uncertainty in the 0-24 h range. Specific atmospheric structures and physical features to be targeted include: 1) upper-tropospheric subsynoptic/mesoscale disturbances, 2) upper- and lower-level jets, 3) three-dimensional moisture plumes, 4) boundary layer moisture tongues, 5) lower and middle troposphere shear profiles, 6) atmospheric stability as manifest by the steepness of midlevel lapse rates, 7) low-level thermal and moisture boundaries, and low-level relative vorticity strips, and 8) soil moisture content and land use characteristics. The PI will be active in the forecasting component of MPEX will seek to identify potential atmospheric structures and physical features that can be targeted for extra dropsonde observations and Microwave Temperature Profiling data coverage needed for atmospheric predictability studies. A primary research focus will be the preparation of synoptic and mesoscale analyses of a variety of selected convective weather systems during MPEX to help assess the feedbacks between deep convective storms and their environment, given that the bulk upscale effects of organized deep convection are known to modify the downstream upper-level flow (e.g., jet/ridge structures). These physically based storm analyses will be used to help assess model predictability in collaboration with other MPEX principal investigators.Intellectual merit: This project will enhance our understanding of the dynamical and thermodynamical processes that conspire to limit the atmospheric predictability a subset of convective weather phenomena that occur over complex terrain and are inherently difficult to predict. The results from this project should help to establish bounds on error growth rates for convective weather phenomena. The project will also provide an opportunity for collaborate research efforts between observationalists and modelers to assess to what extent subjectively derived feature-based uncertainty estimates can be employed in atmospheric predictability studies.Broader impacts: The results from this study will have applications to the forecast community and the general public. Significant gaps in our core knowledge exist about how convective weather systems develop, organize, mature, and dissipate over complex terrain of the Rockies and the High Plains. Enhanced datasets obtained during the field phase of this study should allow researchers to gain a greater scientific understanding of convective storm phenomena. The results from this study will be disseminated widely to the research and operational communities. Graduate students will be trained in mesoscale analyses, convective storm dynamics, and atmospheric predictability studies.