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: 0205612
• 负责人: Xuguang Wang
• 金额: $ 22.07万
• 依托单位: University of Colorado at Boulder
• 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 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205612&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，数值模拟已成为基本的科学工具。 一个关键问题是如何利用大量噪声观测来实时通知或更新此类模拟，特别是当许多预测变量未被观测到或观测到的量与预测变量具有复杂关系时。 原则上，贝叶斯方法提供了这种状态估计问题的解决方案，但演变和更新所需的概率分布在实践中是有问题的，因为最直接的方法需要大量的计算。这些协作研究人员将通过使用基于集合或蒙特卡罗的新颖方法以及数值天气预报（NWP）的背景。 天气预报对于任何状态估计方法来说都是一项具有挑战性的考验，因为美国大陆的运行模型很快将具有 108 个自由度，并且每天吸收的观测数据流将超过 1 TB。 首席研究员将集合状态估计技术应用于数值天气预报的动机是最近在模拟观测测试问题上取得的成功，范围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的预测，以及潜在的优势超越现有的操作数据同化方案。 特别是，基于集合的技术直接估计先前预测的不确定性，从而避免了大多数现有方案中做出的平稳、各向同性预测不确定性的假设。 随着下一代 NWP 模型的分辨率达到约 1 公里，并且遥感观测（例如来自多普勒雷达操作网络的遥感观测）的使用在这些尺度上增加，这种直接估计的好处也可能会增加。 因此，这项研究将为天气预报的重大进步奠定基础，特别是在最严重和破坏性天气发生的范围内。拟议的工作将在天气研究和预报（WRF）模型的背景下进行，这是下一代 NWP 模型，设计用于水平分辨率 1-10 km。 WRF 模型将用于业务天气预报，也将得到研究界的支持。 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，并为实施结果以改进常规天气预报提供了明确的途径。 该小组信息和技术研究项目中组建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。 该项目将通过研究生和博士后研究员的联合监督、联合出版物和年度研讨会进行协调。 此外，所有研究都将使用通用软件，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显着提高天气数值模型的能力。 这些改进将使各种天气现象的预报取得进展。