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

这项研究试图确定与初始和底边界条件有关的不确定性可能限制落基山脉和以东地区对流天气现象在2013年春末夏初举行的中尺度可预报性试验(MPEX)期间0-24小时预报的大气结构和物理特征。MPEX将在高分辨率对流中加入特殊的数据集，使中尺度模式能够测试在0-24小时范围内减少对流风暴预报不确定性的方法。将针对的具体大气结构和物理特征包括：1)高空对流层次天气/中尺度扰动，2)高空和低空急流，3)三维水汽羽流，4)边界层水汽舌，5)对流层中低层切变廓线，6)中层递减率陡峭的大气稳定性，7)低层热量和水分边界，以及低层相对涡度带，8)土壤水分含量和土地利用特征。国际气象中心将在预报部分发挥积极作用，该中心将设法查明潜在的大气结构和物理特征，可作为大气可预报性研究所需的额外落差探测仪观测和微波温度廓线数据复盖的目标。鉴于已知有组织的深层对流对下游高空气流(例如急流/脊流结构)有较大的影响，一个主要的研究重点将是在MPEX期间准备对各种选定的对流天气系统进行天气和中尺度分析，以帮助评估深对流风暴与其环境之间的反馈。这些基于物理的风暴分析将用于与其他MPEX主要研究人员合作，帮助评估模式的可预报性。智力优势：该项目将增强我们对动力和热力学过程的理解，这些过程共同限制了大气可预报性，这是发生在复杂地形上的对流天气现象的一个子集，本质上是难以预测的。这个项目的结果应该有助于建立对流天气现象误差增长率的界限。该项目还将为天文学家和模型师之间的合作研究工作提供机会，以评估主观得出的基于特征的不确定性估计在多大程度上可以用于大气可预报性研究。广泛的影响：这项研究的结果将应用于预报界和普通公众。关于对流天气系统如何在落基山脉和高平原的复杂地形上发展、组织、成熟和消散，我们的核心知识存在重大差距。在这项研究的实地阶段获得的增强数据集应该使研究人员能够对对流风暴现象有更科学的了解。这项研究的结果将广泛传播给研究和业务界。研究生将接受中尺度分析、对流风暴动力学和大气可预报性研究方面的培训。

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.