Improving Prediction of Precipitation by Objective Estimation of Bulk Effects of Cloud and Precipitation Microphysical Processes

通过客观估计云和降水微物理过程的整体效应来改进降水预测

• 批准号: 0754998
• 负责人: Tomislava Vukicevic
• 金额: $ 53.42万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0754998&HistoricalAwards=false
• 关键词: Improving; Prediction; Precipitation; Objective; Estimation

All numerical models of the atmosphere operate at some set minimum resolution which, for example, may be represented by the spatial distance separating points at which measurable properties (e.g., temperature, moisture, wind etc.) are explicitly predicted. Processes operating at inherently smaller scales in the spaces between these points are termed "sub-grid scale", and must be approximated so that their net resolvable-scale impact is accounted for as accurately as possible. One common approach to this approximation for cloud and precipitation processes is termed "bulk microphysical parameterization." This project will investigate a new method for diagnosing and correcting systematic errors in such parameterizations through optimal inclusion (or "assimilation") of radar-observed precipitation fields into an ongoing model run. While traditional efforts to improve bulk microphysical parameterizations have centered on use of archived observations to better tune a myriad of internal parameters, the proposed approach aims to project available real-time radar observations onto a greatly reduced number of external parameters termed "contribution coefficients", allowing modulation of each individual process as a whole. The assembled research team will develop this approach in the context of a 4-dimensional variational data assimilation (4DVAR) system operating in junction with the widely distributed Weather Research and Forecasting (WRF) model at the National Center for Atmospheric Research. Efforts will initially be applied to a combination of orographic (mountain-induced) and frontally-forced storms whose atmospheric circulations are relatively simple capable of being well captured by the WRF model. Ultimately, however, the benefits of such an approach are likely to be greatest for global climate models whose large areal coverage will likely prohibit explicit inclusion of cloud microphysical processes for quite some time.The intellectual merit of this study rests on developing improved estimates of cloud microphysical processes and their contributions to evolving storm structures, which will in turn allow a more objective assessment of the efficacy and suitability of individual bulk parameterization schemes for future use and improvement. This will initially be accomplished through assimilating widely available radar reflectivity observations into a cloud-resolving model for a variety of storm locations and types, but the approach will ultimately be amenable to inclusion of more advanced "polarimetric" radar observations or microphysical processes as those become widely available over the next decade. More accurate yet desirably efficient inclusion of cloud and precipitation processes in global models is an overarching goal of this research.Broader impacts of this research include graduate student education and enhancements to community-based atmospheric model (WRF) used by a wide variety of U.S. and international investigators. The PI will also be training other students and researchers in data assimilation through teaching courses, individual mentoring, and hosting visitors from other institutions. Results of this research hold the potential to improve forecasts of the timing, location and intensity of precipitation events and associated societal impacts.

大气的所有数值模型都以某种设定的最小分辨率运行，例如，可以用空间距离分隔点来表示，在这些点上可测量的特性（例如温度、湿度、风等）被明确预测。 在这些点之间的空间中以固有较小尺度运行的过程被称为“子网格尺度”，并且必须进行近似，以便尽可能准确地考虑它们的净可解析尺度影响。云和降水过程的这种近似的一种常见方法被称为“整体微物理参数化”。 该项目将研究一种新方法，通过将雷达观测的降水场最佳包含（或“同化”）到正在进行的模型运行中来诊断和纠正此类参数化中的系统误差。虽然改进批量微物理参数化的传统努力集中在使用存档的观测数据来更好地调整无数的内部参数，但所提出的方法旨在将可用的实时雷达观测数据投影到数量大大减少的称为“贡献系数”的外部参数上，允许将每个单独的过程作为一个整体进行调整。组建的研究团队将在 4 维变分数据同化 (4DVAR) 系统的背景下开发这种方法，该系统与国家大气研究中心广泛分布的天气研究和预报 (WRF) 模型结合运行。最初将致力于地形（山脉引起的）和锋面强迫风暴的组合，其大气环流相对简单，能够被 WRF 模型很好地捕获。然而，最终，这种方法的好处可能是最大的全球气候模型，其大面积覆盖可能会在相当长的一段时间内禁止明确包含云微物理过程。这项研究的智力价值在于开发改进的云估计微物理过程及其对演变的风暴结构的贡献，这反过来将允许对单个批量参数化方案的功效和适用性进行更客观的评估，以供未来使用和改进。这最初将通过将广泛可用的雷达反射率观测数据同化到各种风暴位置和类型的云解析模型中来实现，但该方法最终将适合包含更先进的“极化”雷达观测数据或微物理过程，因为这些观测数据或微物理过程变得更加先进。并在未来十年内广泛应用。这项研究的首要目标是在全球模型中更准确但更有效地纳入云和降水过程。这项研究的更广泛影响包括研究生教育以及对美国和世界各地使用的基于社区的大气模型 (WRF) 的增强。国际调查人员。 PI 还将通过教学课程、个人指导和接待来自其他机构的访客，对其他学生和研究人员进行数据同化方面的培训。这项研究的结果有可能改善对降水事件的时间、地点和强度以及相关社会影响的预测。