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: 0205599
• 负责人: Fuqing Zhang
• 金额: --
• 依托单位: Texas A&M Research Foundation
• 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 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205599&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.

在各种大型学科中，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在大量噪声观测的情况下实时通知或更新这种模拟，特别是当许多预测变量没有被观测到或观测量与预测变量具有复杂的关系时。原则上，贝叶斯方法为状态估计问题提供了解决方案，但在实践中进化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。这些合作的调查人员将通过使用新的基于集合的或蒙特卡罗方法并在数值天气预报(NWP)的背景下解决这些问题。天气预报是对任何状态估计方法的一次具有挑战性的测试，因为美国大陆的业务模型很快就会有108个自由度的量级，每天接收超过1TB的观测数据流。主要研究人员将集合状态估计技术应用到数值预报中，是因为最近在模拟观测的测试问题上取得了成功，从预报云模式中的孤立雷暴到大气环流模式中的全球大气流动，以及与现有业务数据同化方案相比的潜在优势。特别是，基于集成的技术直接估计先验预测的不确定性，从而避免了大多数现有方案中所做的平稳、各向同性预测不确定性的假设。随着下一代净水预报模式达到约1公里的分辨率，以及对诸如来自多普勒雷达业务网络的遥感观测的使用在这些尺度上的增加，这种直接估计的好处也可能增加。因此，这项研究将为天气预报方面的重大进展奠定基础，特别是在发生最严重和破坏性天气的尺度上。拟议的工作将在天气研究和预报(WRF)模式的背景下进行，该模式是为在1-10公里的水平分辨率上使用而设计的下一代NWP模式。WRF模式将用于业务天气预报，并将得到支持，供研究界使用。WRF的使用使该项目的教育效益倍增，超越了学生和博士后研究人员的直接参与，并为实施改善常规天气预报的成果提供了一条明确的途径。在该小组信息和技术研究项目范围内组建的小组包括集合同化技术的领导者以及在数值模拟、集合预报和对多普勒雷达观测的解释方面具有专门知识的成员。该项目将通过研究生和博士后研究员的联合监督、联合出版物和年度讲习班进行协调。此外，所有研究将使用通用软件，从而促进小组内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工作取得进展。