Evaluation of Mesoscale Convective System Rainfall Predictability in the Upper Midwest Considering System Morphology

考虑系统形态的中西部上部中尺度对流系统降雨可预测性评估

• 批准号: 0226059
• 负责人: William Gallus
• 金额: $ 28.2万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-11-15 至 2006-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0226059&HistoricalAwards=false
• 关键词: Evaluation; Mesoscale; Convective; System; Rainfall

Prediction of warm season convective rainfall remains a challenge, particularly for systems occurring in the absence of orographic or strong larger-scale forcing. Under a prior award, the Principal Investigator focused on improving numerical model Quantitative Precipitation Forecast (QPF) guidance for summer convection. The present research provides a continuation of this research, with a goal of improved use of mixed-physics ensemble guidance, while examining also physical processes associated with mesoscale convective system (MCS) morphology and evolution in the Upper Midwest.The Principal Investigator will explore MCS evolution and morphology, with primary emphasis on differences between systems with trailing stratiform regions and those with leading stratiform regions, by analyzing the outputs of fine grid spacing versions of the NCEP Eta and WRF (Weather Research and Forecasting) models. The use of different models and convection-related physical parameterizations will allow the following two goals to be pursued. First, careful analysis of observed MCSs and comparison with the different mixed model/mixed physics ensemble members will improve understanding of physical processes important in system evolution. Also detailed spatial and temporal investigations of the realism of the simulated MCSs will be pursued. Second, it is suggested that mixed model/mixed physics ensembles will be one of the most direct means of improving warm season mesoscale QPF. This approach will assist in improving QPF, particularly because the emphasis on different MCS morphology will reveal strengths and weaknesses within model parameterizations of physical processes important in the development and evolution of the different MCS modes. This analysis will additionally help improve understanding of the origins of diversity in model forecasts, which should assist in design of optimal mixed-physics ensembles. The researchers also will run selective cloud-resolving scale simulations for each case to which physical processes and QPF will be compared with the runs using parameterized convection.The Principal Investigator will implement new strategies for verification of high resolution models that will improve understanding about verification/evaluation of rainfall on refined spatial and temporal scales. Completion of the proposed research will improve understanding of the development and evolution of convective systems in the Midwest. In addition, the research will facilitate future advances in mesoscale meteorology by establishing the most beneficial uses of refined resolution ensemble guidance for warm season convective system forecasting.

暖季对流降雨的预测仍然是一个挑战，特别是对于在没有地形或强大尺度强迫的情况下发生的系统。在先前的奖励下，首席研究员专注于改进夏季对流的数值模式定量降水预报（QPF）指导。本研究提供了这一研究的延续，目的是改进混合物理集合制导的使用，同时研究与中西部上游中尺度对流系统（MCS）形态和演化相关的物理过程。首席研究员将通过分析NCEP Eta和WRF（天气研究与预报）模型的细网格间隔版本的输出，探索MCS的演化和形态，主要强调具有尾随层状区域和具有领先层状区域的系统之间的差异。使用不同的模型和与对流相关的物理参数化可以实现以下两个目标。首先，仔细分析观测到的mcs，并与不同的混合模型/混合物理系综成员进行比较，将提高对系统演化中重要物理过程的理解。此外，将对模拟MCSs的真实性进行详细的空间和时间调查。第二，混合模式/混合物理组合将是改善暖季中尺度QPF的最直接手段之一。这种方法将有助于改进QPF，特别是因为对不同MCS形态的强调将揭示在不同MCS模式的发展和进化中重要的物理过程的模型参数化中的优缺点。这一分析还将有助于提高对模型预测中多样性起源的理解，这将有助于设计最佳的混合物理系。研究人员还将对每种情况进行选择性的云分辨尺度模拟，将物理过程和QPF与使用参数化对流的运行进行比较。首席研究员将实施验证高分辨率模型的新策略，这将提高对精确时空尺度上降雨验证/评估的理解。本研究的完成将提高对中西部对流系统发展和演变的认识。此外，该研究将通过建立对暖季对流系统预报最有益的精细分辨率集合指导，促进未来中尺度气象学的发展。