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均以一定的最小分辨率运行，例如，可以通过空间距离分离点表示，该空间距离分离点明确地预测了可测量的特性（例如温度，水分，风能等）。 这些点之间的空间中固有较小尺度运行的过程称为“子网格尺度”，必须近似近似，以便尽可能准确地考虑其净可分解尺度的影响。云和降水过程近似的一种常见方法称为“大量的微物理参数化”。 该项目将通过最佳包含（或“同化”）将雷达观测的降水场诊断和纠正此类参数化的新方法，以在持续的模型运行中进行诊断和纠正系统错误。尽管传统的改善散装微物理参数化的努力集中在使用存档观测值以更好地调整无数的内部参数，但拟议的方法旨在将可用的实时雷达观察结果投影到大大减少的“贡献系数”上的大幅度减少的外部参数上，从而使每个单个过程的调整均为整个过程。组装研究团队将在国家大气研究中心的四维变异数据同化（4DVAR）系统中开发这种方法。最初，努力将应用于地形（山脉引起的）和正面风暴的结合，其大气循环相对简单，能够被WRF模型很好地捕获。然而，最终，这种方法的好处对于全球气候模型的好处可能是最大的，这些气候模型可能会在相当长的一段时间内禁止明确包含云的微物理过程。这项研究的智力优点取决于发展云的估计值的估计值，以提高云的微生物过程及其对未来的启动性的构成，进一步的启动性，进一步的启动性，并适用于促进的效率，并适用于效率的效率，而有效的效果则有效，有效性地构成了效率的效果，这是有效性的，这些效果是有效的。和改进。最初，这将通过将广泛可用的雷达反射率观察（用于各种风暴位置和类型的云解析模型）中的模型来实现，但是该方法最终将适合包含更先进的“极化”雷达观测值或微物理过程，因为这些过程在未来十年中已广泛使用。在全球模型中更准确但更有效地包含云和降水过程是这项研究的总体目标。这项研究的行为影响包括研究生教育和增强社区大气模型（WRF）（WRF），由美国和国际研究人员使用。 PI还将通过教学课程，个人指导和接待其他机构的访客来培训其他学生和研究人员的数据同化。这项研究的结果有可能改善对降水事件的时间，位置和强度以及相关社会影响的预测。