Analysis of Predictability of Convective Initiation and Morphological Evolution Using Near-Cloud Permitting Grid Spacing Models

使用近云允许网格间距模型分析对流起始和形态演化的可预测性

• 批准号: 0848200
• 负责人: William Gallus
• 金额: $ 45.59万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0848200&HistoricalAwards=false
• 关键词: Analysis; Predictability; Convective; Initiation; Morphological

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Accurate computer-based numerical simulations of the initiation and morphological evolution of mesoscale convective systems (MCSs), which embody lines and/or clusters of thunderstorms and attendant lighter precipitation accounting for the bulk of growing-season rainfall over the midwestern U.S., are important for forecasts of both quantitative precipitation amounts and severe weather risks. Increased computational speed has allowed operational model grid-mesh spacing to extend to scales capable of resolving individual cloud features within these storm systems. Even so, the initiation of these clouds occurs on spatial scales so small (and is moreover sufficiently dependant on imperfectly-measured initial atmospheric conditions at these fine scales) as to defy proper representation in these models. Subsequent modeled evolution of the intensity and horizontal patterning of larger mature thunderstorm elements may be inaccurately portrayed for similar reasons, and is further impacted by deficiencies in cloud microphysical schemes that in-turn influence simulated cold pools/gust fronts and resultant low-level cloud forcing.This study will exploit an extensive archive of observed and simulated MCS events, including observations from the IHOP and BAMEX field experiments and NOAA Hazardous Weather Testbed project, to accomplish three primary objectives: (1) Complete detailed analyses of archived simulations and corresponding observations of convective initiation and MCS evolution to identify key processes controlling these attributes; (2) perform systematic tests to determine sensitivity of such simulations to both representations of cloud microphysical processes and novel parameterization methods appropriate to fine-mesh scales characteristic of state-of-the-art atmospheric models; and (3) develop methods for improved QPF (Quantitative Precipitation Forecast) guidance making use of entity-based verification techniques and ensembles of numerical simulations. The intellectual merit of this research rests in achieving a fuller and more accurate understanding of numerical simulation dependencies on complex relationships between model resolution and those specific parameterization schemes being employed, which in some cases trace back to far-coarser models developed a decade or more ago. Broader impacts will emerge through support of the education of several graduate students, through inclusion of emerging research themes in classroom and student project-oriented undergraduate studies, via interactions with National Weather Service forecasters and public media interviews, and through contributions to improved methodologies for forecasts of quantitative precipitation amounts/timing and severe weather impacting the public.

该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资金。对中尺度对流系统 (MCS) 的启动和形态演化进行精确的计算机数值模拟，其中中尺度对流系统体现了雷暴线和/或雷暴簇以及随之而来的降雨量减少，占美国中西部生长季降雨量的大部分，对于定量降水量和恶劣天气风险的预测非常重要。 计算速度的提高使得操作模型网格间距能够扩展到能够解析这些风暴系统内的单个云特征的规模。即便如此，这些云的形成发生在如此小的空间尺度上（而且充分依赖于在这些精细尺度上不完全测量的初始大气条件），以至于无法在这些模型中正确表示。 由于类似的原因，较大的成熟雷暴元素的强度和水平模式的后续模拟演化可能会被不准确地描述，并进一步受到云微物理方案缺陷的影响，进而影响模拟冷池/阵风锋和由此产生的低层云强迫这项研究将利用大量观测和模拟 MCS 事件档案，包括 IHOP 和 BAMEX 现场实验以及 NOAA 危险天气试验台项目的观测，以实现三个主要目标： (1) 完成对已存档的模拟以及对流起始和 MCS 演化的相应观测的详细分析，以确定控制这些属性的关键过程； (2) 进行系统测试，以确定此类模拟对云微物理过程表示和适合最先进大气模型的细网格尺度特征的新颖参数化方法的敏感性； (3) 利用基于实体的验证技术和数值模拟集合，开发改进 QPF（定量降水预报）指导的方法。这项研究的智力价值在于更全面、更准确地理解数值模拟对模型分辨率和所采用的特定参数化方案之间复杂关系的依赖关系，在某些情况下，这些模型可以追溯到十年或更早开发的更粗略的模型。 通过支持几名研究生的教育，通过将新兴研究主题纳入课堂和以学生项目为导向的本科学习，通过与国家气象局预报员的互动和公共媒体采访，以及通过为改进预报方法做出贡献，将产生更广泛的影响定量降水量/时间和影响公众的恶劣天气。