Improvement of Quantitative Precipitation Forecasts Using a New Microphysical Parameterization

使用新的微物理参数化改进定量降水预报

• 批准号: 9908995
• 负责人: Vanda Grubišić
• 金额: $ 28.89万
• 依托单位: Nevada System of Higher Education, Desert Research Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9908995&HistoricalAwards=false
• 关键词: Improvement; Quantitative; Precipitation; Forecasts; Using

Accurate forecasts of precipitation associated with disruptive, high-impact weather events can save lives and money. Current forecast accuracy suffers from poor representation of microphysical processes in numerical weather prediction (NWP) models. The goal of this research is to improve quantitative precipitation forecasts (QPF) in regional scale models through better formulation of microphysical processes. The central hypothesis of this project is that significant improvement in QPF in complex terrain can be reached through a consistent representation of warm, cold, and mixed-phase clouds. Explicit treatment of all growth processes will generate more accurate mass distributions and fall trajectories, leading to more accurate spatial distribution of rain and snowfall. To test the central hypothesis, the investigators will implement an improved microphysical scheme in two advanced, research numerical models, perform rigorous sensitivity studies in idealized simulations, validate forecasts of heavy rain and snowfall events using analyzed data sets, evaluate improvement in QPF and rain-snow line forecasts using statistically meaningful methods, test model performance and investigate the interaction of the dynamics and microphysics of cloud systems in real-time winter storm simulations, and determine the ratio of increase in computational time to forecast improvement.Rain and snowfall totals, Doppler and profiler radar data, as well as in situ dynamic and microphysical aircraft measurements from two already completed field projects will be used in model validation. The new version of the microphysics parameterization will be made available to the scientific community and the local NWS office in Reno, where its performance will be tested in an operational environment.

准确预报与破坏性、高影响力天气事件相关的降水量可以挽救生命和金钱。数值天气预报模式对微物理过程的模拟能力较差，影响了预报的准确性。本研究的目标是通过更好地制定微物理过程，以提高定量降水预报（QPF）在区域尺度模式。该项目的中心假设是，在复杂地形的QPF的显着改善，可以通过一个一致的代表性的温暖，寒冷，和混合相云。所有增长过程的显式处理将产生更准确的质量分布和下降轨迹，从而导致更准确的降雨和降雪的空间分布。 为了检验中心假设，研究人员将在两个先进的研究数值模式中实施改进的微物理方案，在理想化模拟中进行严格的敏感性研究，使用分析的数据集验证大雨和降雪事件的预报，使用统计学上有意义的方法评估QPF和雨雪线预报的改进，在实时冬季风暴模拟中，测试模式性能，研究云系动力学和微物理学的相互作用，并确定计算时间增加与预报改进的比率。降雨和降雪总量，多普勒和剖面仪雷达数据，以及两个已经完成的实地项目的现场动态和微物理飞机测量数据将用于模型验证。 将向科学界和里诺的当地国家气象局办公室提供新版本的微物理参数化，并在操作环境中对其性能进行测试。