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.

在各种学科中，数值模拟已成为一种基本的科学工具。 一个关键问题是如何使用大量嘈杂的观察结果实时告知或更新此类模拟，尤其是当许多预测变量未观察到或观察到的数量与预测变量具有复杂的关系时。 原则上，贝叶斯方法为这种状态估计问题提供了解决方案，但是在实践中不断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计算压倒性的规模。这些协作调查人员将通过新颖的合奏或蒙特卡洛方法或在数值天气中的上下文（NWP）来解决这些问题。 天气预测是对任何国家估计方法的挑战性测试，因为美国大陆的运营模型很快将获得108度的自由度，并摄取每天超过terabyte的观察数据流。 主要研究者将整体估计技术应用于NWP的应用是由于最近在模拟观察结果的测试问题成功的成功而激发的，从云模型中孤立的雷暴的预测到一般循环模型中的全球大气流，以及在现有的运营数据同化方案中的潜在优势。 特别是，基于合奏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固定的，各向同性预测的不确定性的假设。 直接估计的好处也可能会增加，因为NWP模型的下一代达到约1公里的分辨率，并且使用远程感应的观测值，例如从多普勒雷达的操作网络中增加了这些量表。 因此，这项研究将为天气预报迈出重要的一步，尤其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测（WRF）模型的背景下进行，该模型是下一代NWP模型，该模型旨在在1-10 km的水平度下使用。 WRF模型将用于操作天气预报，并将受到研究社区的支持。 WRF的使用将该项目的教育益处倍增，而不是学生和博士后研究人员的直接参与，并为实施结果提供了清晰的途径，以改善常规天气预测。 该小组信息和技术研究项目中组装的团队包括合奏同化技术的领导者，以及具有数值建模，合奏预测的专业知识的成员，以及对多普勒雷达观察的解释。 该项目将通过联合监督研究生和博士后研究员，联合出版物和年度研讨会来协调。 此外，所有研究都将使用通用软件，从而促进了该组中方法和专业知识的转移。成功完成这项研究的完成可能会在天气数值模型中提供显着改善的能力。 这些改进将允许在预测各种天气现象的预测中取得进步。