Improving Microphysics Parameterizations and Quantitative Precipitation Forecast through Optimal Use of Video Disdrometer, Profiler and Polarimetric Radar Observations

通过优化使用视频测距仪、剖面仪和偏振雷达观测来改进微物理参数化和定量降水预报

• 批准号: 0608168
• 负责人: Guifu Zhang
• 金额: $ 46.46万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0608168&HistoricalAwards=false
• 关键词: Improving; Microphysics; Parameterizations; Quantitative; Precipitation

The objective of this work is to improve precipitation estimation and numerical forecasts of quantitative precipitation with the optimal use of observations from advanced instruments. A two-dimensional video disdrometer will provide detailed information about the size, shape and density of precipitating particles at a fixed location on the ground. A vertically pointing wind profiler will measure precipitation (or clear air) characteristics vertically over a given point. Polarization radar measurements will allow classification of hydrometeor types and retrieval of hydrometeor drop size distributions over the depth of the troposphere out to a range of about 120 km. The combination of these measurements makes it possible map detailed precipitation characteristics through a volume of the atmosphere. These observations will be used to refine and verify microphysical parameterizations in numerical weather prediction models. They also will enable the project team to develop forward observation operators for polarization radar variables, improve quantitative precipitation estimation algorithms through optimal use of polarimetric radar data, and assimilate radar observations into numerical weather prediction models for optimal retrieval of microphysical parameters and for model initialization and forecasting. Error structures of radar measurements and retrievals will be quantified. Observation-based and model-based microphysical retrievals will be cross-validated and verified with in-situ measurements. The project will help satisfy increasing needs for improved microphysics parameterization in research and operational numerical weather prediction models as they start to resolve convection and precipitation explicitly. It also will position operational weather forecasters to make optimum use of new observational capabilities that will result from the planned upgrade of the US national network of operational weather radars to dual- polarization capability in the next five years.The broader impacts of this research include more accurate quantitative precipitation estimation and better short-term quantitative precipitation forecasting. Better forecasting also will result from better initialization of numerical weather prediction models via advanced data assimilation. The improved precipitation estimates will greatly improve our ability to assess the risks of flooding and to improve the prediction capabilities of hydrological models. The improved microphysical parameterization schemes can be implemented and tested within community numerical weather prediction models. Research results will be disseminated through seminars, conference presentations and formal publications in the scientific literature to ensure a broad impact. The activity will also provide two graduate students with the opportunity to participate in a field program and gain research experiences with disdrometer and radar observations, numerical weather prediction, and advanced data assimilation.

这项工作的目的是通过最佳利用先进仪器的观测值来改进定量降水的降水量估计和数值预报。一个二维视盘测量仪将提供关于地面上固定位置的沉淀颗粒的大小、形状和密度的详细信息。垂直指向的风廓线仪将测量给定点的垂直降水(或晴朗空气)特征。偏振雷达测量将能够对水流星类型进行分类，并恢复对流层深度范围内约120公里范围内的水流星水滴大小分布。这些测量结果的结合使得绘制大气体积的详细降水特征图成为可能。这些观测将用于改进和验证数值天气预报模式中的微物理参数。它们还将使项目组能够开发偏振雷达变量的正演观测算符，通过最佳利用偏振雷达数据改进定量降水估计算法，并将雷达观测同化到数值天气预报模型中，以便最佳地恢复微物理参数以及进行模型初始化和预报。雷达测量和检索的误差结构将被量化。将对基于观测和基于模型的微物理反演进行交叉验证，并通过现场测量进行验证。该项目将有助于满足在研究和业务数值天气预报模式中改进微物理参数化的日益增长的需求，因为它们开始明确地解决对流和降水问题。它还将使业务天气预报员最佳利用美国国家业务天气雷达网络计划在未来五年升级为双极化能力所产生的新观测能力。这项研究的更广泛影响包括更准确的定量降水估计和更好的短期定量降水预报。通过先进的数据同化，数值天气预报模式的更好初始化也将带来更好的预报。改进的降雨量估计将极大地提高我们评估洪水风险的能力，并改善水文模型的预测能力。改进的微物理参数化方案可以在社区数值天气预报模式中实施和检验。研究成果将通过研讨会、会议报告和科学文献中的正式出版物传播，以确保产生广泛的影响。该活动还将为两名研究生提供参加实地项目的机会，并获得在视盘测量仪和雷达观测、数值天气预报和高级数据同化方面的研究经验。