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）降雨预测仍然是一个艰巨的挑战。 MCS 的准确模拟受到初始化数据和这些系统开发和演化中重要过程的模型参数化方面的缺陷的阻碍。 首席研究员之前的项目研究了几种改进夏季对流数值模型定量降水预报（QPF）指导的方法。 该奖项的研究建立在先前研究的基础上，旨在检验中西部/平原具有不同形态的 MCS 相关的中尺度环流和降雨的可预测性差异。为了对观测到的事件进行全面评估并详细验证模型模拟，MCS 案例将主要从两个最近完成的实地项目中选择。 该研究将追求两个主要目标。 首先，对观测到的 MCS 进行仔细分析将进一步了解对不同形态重要的环流，并允许确定近云解析这些事件的环流、降雨和形态模拟中的系统偏差。 其次，使用天气研究和预报 (WRF) 数值模型的各种配置进行的敏感性测试将有助于建立最佳方法（例如动力学物理设置），以便利用这一新开发但广泛使用的研究和预报成功模拟暖季 MCS 降雨模型。 首席研究员将评估以下假设：混合物理系综可能是改善暖季中尺度 QPF 的最直接方法，因为物理方案的差异会增强传播。 首席研究员将通过在近云解析网格间距处使用不同的微物理方案或其中的参数获得的多样性与使用不同的动态核心和初始条件而发生的扩散进行对比。 此外，在某些情况下，将使用 4 公里到 10 公里的网格间距范围来检查网格间距变化对完全显式云运行的影响。 此外，将进行更粗略的运行，以比较具有许多成员的集合系统的有用性，这些成员需要使用对流参数化（已知具有较大的误差）与单独的完全明确的 4 公里确定性预报或小型集合（4-8成员）这些更精细的网格运行。该研究将实施一些相对较新的方法，包括：（i）验证高分辨率模型的策略，这将提高对精细时空尺度上降雨验证/评估的理解，以及（ii）用于充分解释降雨的因子分离方法敏感性分析。这项努力有几个更广泛的影响。 它重点关注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，并解决了当前的核心 QPF 问题——使用足够细的网格间距来忽略对流参数化。 该研究的完成将增进对中西部/平原对流系统发展和演变的了解，并有助于改进向公众和各行业部门发布的预测。 WRF 模型旨在成为美国未来几年研究和短期业务预测的主要工具，促进研究产生更广泛的影响。 此外，该研究还将支持研究生。 在大气科学期刊上发表研究成果以及在会议和研讨会上发表研究成果将使研究成果直接惠及业务预报人员，并通过他们惠及公众。