Collaborative Research: ITR--Ensemble-Based State Estimation for a Next-Generation Weather Forecasting Model

合作研究：ITR——基于集合的下一代天气预报模型状态估计

• 批准号: 0205648
• 负责人: Gregory Hakim
• 金额: $ 28.77万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205648&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Ensemble; Based

In a variety of disciplines large, numerical simulations have become a fundamental scientific tool. A key problem is how to inform or update such simulations in real time with large numbers of noisy observations, especially when many of the predicted variables are unobserved or the observed quantities bear a complex relation to the predicted variables. In principle, Bayesian methods provide a solution to this state-estimation problem, but evolving and updating the required probability distributions are problematic in practice, as the most straightforward approaches require computations of overwhelming size.These collaborative investigators will address these issues through the use of novel ensemble-based or Monte Carlo approaches and within the context of numerical weather prediction (NWP). Weather prediction is a challenging test of any approach to state-estimation, as operational models for the continental United States will soon have of the order of 108 degrees of freedom and ingest an observational data stream of more than a terabyte per day. The Principal Investigators' application of ensemble state-estimation techniques to NWP is motivated by recent success in test problems with simulated observations, ranging from the prediction of isolated thunderstorms in a cloud model to global atmospheric flow in a general circulation model, and by potential advantages over existing operational data assimilation schemes. In particular, ensemble-based techniques directly estimate the uncertainty of the prior prediction and thereby avoid the assumption of stationary, isotropic forecast uncertainty made in most existing schemes. The benefits of this direct estimation will also likely increase as next-generation of NWP models reach resolutions of about 1 km and the use of remotely-sensed observations, such as from the operational network of Doppler radars, increases at those scales. Thus, this research will lay the foundation for a significant step forward in weather forecasting, especially at the scales where most severe and disruptive weather occurs.The proposed work will be carried out within the context of the Weather Research and Forecasting (WRF) model, which is a next-generation NWP model designed for use at the horizontal resolutions of 1-10 km. The WRF model will be employed in operational weather forecasting and also will be supported for use by the research community. Use of WRF multiplies the educational benefits of this project beyond the direct involvement of students and postdoctoral researchers and provides a clear path to the implementation of results to improve routine weather forecasts. The team assembled within this group Information and Technology Research project includes leaders in ensemble assimilation techniques as well as members with expertise in numerical modeling, ensemble forecasting, and the interpretation of Doppler radar observations. The project will be coordinated through joint supervision of graduate students and postdoctoral fellows, joint publications and annual workshops. In addition, common software will be used in all the research, thus facilitating the transfer of methodologies and expertise within the group.Successful completion of this research potentially will provide significantly improved capabilities in weather numerical models. These improvements will allow advances to be made in the forecasting of a variety of weather phenomena.

在各种大的学科中，数值模拟已经成为一种基本的科学工具。 一个关键的问题是如何通知或更新这样的模拟在真实的时间与大量的噪声观测，特别是当许多预测变量是未观察到的或所观察到的数量承担一个复杂的关系，预测变量。 原则上，贝叶斯方法提供了一个解决这个状态估计问题的方法，但在实践中，发展和更新所需的概率分布是有问题的，因为最直接的方法需要压倒性的计算量。这些合作研究人员将通过使用新的基于集合或蒙特卡罗方法和数值天气预报（NWP）的背景下解决这些问题。 天气预测是对任何状态估计方法的挑战性测试，因为美国大陆的业务模型很快将拥有108个自由度的数量级，每天摄取超过1 TB的观测数据流。 主要研究人员将集合状态估计技术应用于数值预报的动机是最近在模拟观测的测试问题中取得的成功，从云模型中孤立雷暴的预测到大气环流模型中的全球大气流动，以及现有业务数据同化方案的潜在优势。 特别是，基于集成的技术直接估计的不确定性的先验预测，从而避免了假设的平稳，各向同性的预测不确定性在大多数现有的计划。 随着下一代数值预报模式的分辨率达到约1公里，以及在这些尺度上更多地使用遥感观测，如多普勒雷达业务网络的观测，这种直接估算的好处也可能增加。 因此，这项研究将为天气预报，特别是在最严重和破坏性天气发生的尺度上向前迈出重要一步奠定基础。拟议的工作将在天气研究和预报（WRF）模式的范围内进行，这是一个设计用于1-10公里水平分辨率的下一代数值预报模式。 WRF模型将用于业务天气预报，也将支持研究界使用。 WRF的使用成倍增加了该项目的教育效益，超出了学生和博士后研究人员的直接参与，并提供了一个明确的路径，以实施结果，以改善日常天气预报。 该小组信息和技术研究项目中的团队包括集合同化技术的领导者以及在数值建模，集合预报和多普勒雷达观测解释方面具有专业知识的成员。 将通过联合监督研究生和博士后研究员、联合出版物和年度讲习班来协调该项目。 此外，所有的研究都将使用通用软件，从而促进小组内部方法和专业知识的转移。这项研究的成功完成可能会大大提高天气数值模式的能力。 这些改进将使各种天气现象的预报取得进展。