Evaluating Predictability of Mesoscale Circulations, Morphologies, and Rainfall Evolution for Warm Season Convective Systems

评估暖季对流系统中尺度环流、形态和降雨演化的可预测性

• 批准号: 0537043
• 负责人: William Gallus
• 金额: $ 34.52万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0537043&HistoricalAwards=false
• 关键词: Evaluating; Predictability; Mesoscale; Circulations; Morphologies

Warm season mesoscale convective system (MCS) rainfall prediction remains a difficult challenge. Accurate simulation of MCSs is hampered by deficiencies in both initialization data and model parameterizations of processes important in these systems' development and evolution. The Principal Investigator's previous projects have examined several approaches toward improving numerical model Quantitative Precipitation Forecast (QPF) guidance for summer convection. The research under this award builds on prior research to examine differences in the predictability of mesoscale circulations and rainfall associated with MCSs in the Midwest/Plains possessing different morphologies.To allow for thorough evaluation of observed events and detailed verification of model simulations, MCS cases will be primarily chosen from two recently completed field projects. The research will pursue two primary goals. First, careful analysis of observed MCSs will further understanding about the circulations important to different morphologies and allow for determination of systematic biases in the near-cloud resolving simulations of the circulations, rainfall, and morphologies of these events. Second, sensitivity tests performed with various configurations of the Weather Research and Forecasting (WRF) numerical model will help establish optimal methods (e.g., dynamics-physics settings) for successful simulation of warm season MCS rainfall with this newly developed yet heavily used research and forecasting model. The Principal Investigator will evaluate the hypothesis that mixed physics ensembles may be the most direct way to improve warm season mesoscale QPF because differences in physics schemes enhance spread. The Principal Investigator will contrast diversity obtained through the use of different microphysical schemes or parameters within them at near-cloud resolving grid spacings with spread occurring from the use of different dynamic cores and initial conditions. Also, for some cases a range of grid spacings from 4 km to 10 km will be used to examine impacts in fully explicit cloud runs from changes in grid spacing. In addition, coarser runs will be performed to compare the usefulness of ensemble systems with many members that require the use of convective parameterizations (which are known to possess large errors) with individual fully explicit 4 km deterministic forecasts, or small ensembles (4-8 members) of these finer grid runs. The study will implement some relatively new methodologies, including: (i) strategies for verification of high resolution models that will improve understanding about verification/evaluation of rainfall on refined spatial and temporal scales, and (ii) a factor separation approach for adequate interpretation of sensitivity analyses. This effort has several broader impacts. It focuses on one of the most challenging short- range forecast problems -- warm season convective rainfall occurring typically in weakly forced environments, and addresses a present central QPF issue -- the use of fine enough grid spacing to neglect convective parameterizations. Completion of the research will improve understanding of the development and evolution of convective systems in the Midwest/Plains, and assist in improvements of forecasts issued to the public and various industry sectors. The WRF model was designed to become a primary vehicle for both research and short-range operational forecasting in the coming years in the United States, facilitating broader impacts of the research. In addition, the study will support graduate students. Publication of research results in atmospheric science journals and presentation at conferences and workshops will permit research findings to directly benefit operational forecasters and through them, the general public.

暖季中尺度对流系统（MCS）降水预报仍然是一个艰巨的挑战。 MCS的精确模拟受到初始化数据和模型参数化的不足的阻碍，这些系统的发展和演变的重要过程。 首席研究员以前的项目已经研究了几种方法，以改善数值模式定量降水预报（QPF）指导夏季对流。 该奖项下的研究建立在以前的研究，以检查中尺度环流和降雨的可预测性的差异与MCS在中西部/平原拥有不同的morphology.To允许观测事件的全面评估和详细验证模式模拟，MCS的情况下，将主要从两个最近完成的实地项目中选择。 这项研究将追求两个主要目标。 首先，仔细分析所观察到的MCS将进一步了解环流重要的不同形态，并允许确定系统偏差的近云解决模拟的环流，降雨，和这些事件的形态。 其次，对天气研究和预报（WRF）数值模式的各种配置进行的敏感性测试将有助于建立最佳方法（例如，动力学-物理学设置），成功地模拟暖季MCS降雨与这个新开发的，但大量使用的研究和预测模型。 主要研究者将评估混合物理合奏可能是改善暖季中尺度QPF的最直接方法的假设，因为物理方案的差异会增强传播。 主要研究员将对比通过使用不同的微物理方案或参数在近云分辨网格间距获得的多样性，以及使用不同的动态核心和初始条件产生的扩散。 此外，在某些情况下，将使用从4公里到10公里的网格间距范围来研究网格间距变化对完全显式云运行的影响。 此外，将进行较粗的运行，以比较集合系统的有用性，其中许多成员需要使用对流参数化（已知具有较大的误差）与个人完全明确的4公里确定性预报，或这些更精细的网格运行的小集合（4-8个成员）。这项研究将采用一些相对较新的方法，包括：㈠高分辨率模型的核查战略，这将提高对在精确的空间和时间尺度上核查/评估降雨量的理解; ㈡适当解释敏感性分析的因素分离方法。这一努力有几个更广泛的影响。 它侧重于最具挑战性的短期预报问题之一--暖季对流降雨通常发生在弱强迫环境中，并解决了目前的中心QPF问题--使用足够细的网格间距来忽略对流参数化。 研究的完成将提高对中西部/平原对流系统发展和演变的理解，并有助于改进向公众和各行业部门发布的预报。 WRF模式的目的是成为美国未来几年研究和短期业务预报的主要工具，促进研究的更广泛影响。 此外，这项研究将支持研究生。 在大气科学期刊上发表研究成果并在会议和讲习班上介绍研究成果，将使业务预报员直接受益，并通过他们使公众受益。