Evaluation and Improvement of Microphysical Parameterizations in Mesoscale Models

介观模型中微物理参数化的评估和改进

• 批准号: 0504028
• 负责人: Clifford Mass
• 金额: --
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0504028&HistoricalAwards=false
• 关键词: Evaluation; Improvement; Microphysical; Parameterizations; Mesoscale

The use of high-resolution weather prediction models has become the cornerstone of operational forecasting of regional weather and precipitation. But even with steady advances in model resolution and physics, precipitation forecasts have been slow to improve. It has become evident that there are substantial deficiencies in model bulk microphysical parameterizations (BMP) of cloud and precipitation processes, that is, the model descriptions of moist processes. Underlying these deficiencies are considerable uncertainties in many of the assumptions on which BMPs are based. One avenue to improve the performance of a BMP is to compare microphysical processes and predicted cloud/precipitation distributions from model simulations with in situ (mainly airborne) and remotely sensed (e.g., radar) observations. In addition, it is critically important that the microphysical measurements be obtained concurrently with observations of wind, temperature and humidity, so that errors in the simulated microphysics can be isolated from errors in other predicted fields. In response to these problems, UW researchers initiated a study entitled Improvement of Microphysical PaRameterization Through Observational Verification Experiment (IMPROVE) to acquire the observations required to compare cloud and precipitation processes in current forecast and research models with detailed measurements and observations from a variety of weather systems. Two field studies were conducted: the Washington Offshore Frontal Study (IMPROVE-1), which examined frontal systems as they approached the Washington coast from 4 January to 14 February 2001; and, the Oregon Cascades Orographic Study (IMPROVE-2), which examined the orographic modulation of clouds and precipitation across the Oregon Cascades between 26 November and 22 December 2001. Making use of a comprehensive array of observing platforms, both field studies were highly successful in obtaining data for evaluating the performance of BMPs in mesoscale models, with twenty-six Intensive Observing Periods (IOPs) encompassing a wide variety of frontal and orographic precipitation systems. Analysis of this data has documented significant problems with the most sophisticated microphysics scheme in the MM5 model, including excessive snow amounts over the windward slopes and mountain crest, excessive snow blow-over to the lee slopes, too much cloud liquid water over the lower windward slopes and too little over the crest, problematic snow size distributions, and unrealistic graupel amounts at mid-levels. This research will determine if these and other microphysical problems are revealed in other cases. Specifically, observational data from additional IMPROVE IOPs will be analyzed to ascertain the physical processes leading to the development of clouds/precipitation in the observed cases, followed by a comparison with mesoscale model simulations down to resolutions of approximately 1 km. These comparisons will provide the basis for modifications to BMPs in order to better represent the development of clouds and precipitation. In addition, the dual-Doppler radar data from NOAA P3 aircraft flights during IMPROVE will be used to evaluate the fidelity of mountain waves and other key mesoscale structures in the model. Finally, based on the above evaluations, modifications in the model moist physics will be evaluated for a wide variety of storm systems studied during IMPROVE, as well as in daily operational forecast runs of the University of Washington's real-time MM5/WRF regional forecast system. Regarding intellectual merit, the Principal Investigators will analyze probably the most comprehensive data set in existence dealing with the flow and moist physics over a topographic barrier. This study will provide the best evaluation to date of the fidelity of mesoscale model structures and moist physics over terrain and should lead to improved moist process parameterizations in weather prediction models. The research has the potential for broad benefits to society. The identification and correction of deficiencies in the moist physics of weather forecast models should result in improved understanding and prediction of clouds and precipitation, with the attendant societal and economic benefits. The physical understanding and model improvements resulting from this project will be widely disseminated for use by operational forecasting centers and other groups. This effort will also have substantial educational benefits, creating a group of graduate students knowledgeable in this critical area, and will expose a number of undergraduates to this important research topic.

高分辨率天气预报模式的使用已成为区域天气和降水业务预报的基石。但是，即使在模型分辨率和物理学方面有了稳定的进步，降水预报的改进也很慢。云和降水过程的模型体微物理参数化（BMP），即湿润过程的模型描述，存在着明显的缺陷。在这些缺陷的基础上，bmp所依据的许多假设存在相当大的不确定性。改善BMP性能的一个途径是将微物理过程和预测的云/降水分布与现场（主要是机载）和遥感（如雷达）观测进行比较。此外，至关重要的是，微物理测量必须与风、温度和湿度的观测同时获得，以便将模拟微物理的误差与其他预测场的误差隔离开来。针对这些问题，华盛顿大学的研究人员发起了一项名为“通过观测验证实验改进微物理参数化”（IMPROVE）的研究，以获取所需的观测数据，将当前预测和研究模型中的云和降水过程与来自各种天气系统的详细测量和观测进行比较。进行了两项实地研究：华盛顿近海锋面研究（改进-1），在2001年1月4日至2月14日锋面系统接近华盛顿海岸时进行了检查；以及俄勒冈瀑布地形研究（IMPROVE-2），研究2001年11月26日至12月22日期间俄勒冈瀑布云和降水的地形变化。利用一系列全面的观测平台，这两项实地研究都非常成功地获得了评估中尺度模式中bmp性能的数据，其中26个密集观测周期（IOPs）涵盖了各种各样的锋面和地形降水系统。对这些数据的分析记录了MM5模式中最复杂的微物理方案存在的重大问题，包括迎风坡和山顶上的雪量过多、背风坡上的雪量过多、较低迎风坡上的云液态水过多而山顶上的雪量过少、有问题的雪大小分布以及中层的霰量不现实。这项研究将确定这些和其他微物理问题是否在其他情况下也会出现。具体而言，将分析来自额外的改进IOPs的观测数据，以确定在观测情况下导致云/降水发展的物理过程，然后与中尺度模式模拟进行比较，分辨率约为1公里。这些比较将为修改bmp提供基础，以便更好地代表云和降水的发展。此外，改进期间NOAA P3飞机飞行的双多普勒雷达数据将用于评估模式中山波和其他关键中尺度结构的保真度。最后，在上述评价的基础上，将对改进期间研究的各种风暴系统以及华盛顿大学实时MM5/WRF区域预报系统的日常业务预报运行中对模式潮湿物理的修改进行评估。在智力方面，首席研究员将分析可能是目前最全面的数据集，处理地形障碍上的流动和潮湿物理。这项研究将提供迄今为止对中尺度模式结构和地形湿物理保真度的最佳评估，并将改进天气预报模式中的湿过程参数化。这项研究有可能为社会带来广泛的利益。识别和纠正天气预报模式在潮湿物理方面的不足，将有助于提高对云和降水的理解和预测，从而带来社会和经济效益。这个项目产生的物理理解和模式改进将广泛传播，供业务预报中心和其他团体使用。这一努力也将产生巨大的教育效益，培养一批在这一关键领域知识渊博的研究生，并将使一些本科生接触到这一重要的研究课题。