Collaborative Research: ITR: Prototype, High-Performance, Threat-Adaptive, Space Storm Simulation and Forecast Model Supported by a Data Assimilative, Grid Computing Infrastructure

合作研究：ITR：由数据同化、网格计算基础设施支持的原型、高性能、威胁自适应、空间风暴模拟和预测模型

• 批准号: 0325255
• 负责人: Richard Wolf
• 金额: $ 26万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325255&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Prototype; Performance

This project involves research on how to improve space weather modeling by incorporating data assimilation and on-demand grid computing capability. The aim is to develop a first principles, physics-based model of the Sun-Earth system with the capability of making complete space plasma simulations, from the Sun's surface to the upper atmosphere of the Earth. The starting point will be an existing coupled modeling system in which a model of the magnetosphere and solar wind is coupled to a model of the inner magnetosphere and an upper atmosphere model. Data assimilation will be based on a Kalman filter approach. Variability in initial conditions that cannot be directly constrained by data will be estimated from ensembles of runs, characterized by a statistical sub-space identification technique, and then modeled by an empirical stochastic model. The stochastic model will be combined with the deterministic model to produce a hybrid model that will then be the basis of the main data assimilative model. The modeling system will be implemented following Open Grid Services Architecture design protocols so that it can be run on a distributed computational grid. One of the goals is to develop an adaptive system that can respond to significant events in the magnetosphere by incorporating additional computational grid resources as required. If successful, this research could lead to better tools for forecasting space weather. In addition, the project includes partnerships with developers of educational tools for formal and informal education to create teaching resources on the topic of space weather for use in K-12 schools and science museums. The project also includes links with teaching faculty at undergraduate institutions so that data and research results can be integrated into courses on space physics.

该项目涉及研究如何通过结合数据同化和按需网格计算能力来改进空间天气建模。其目的是建立一个基于第一性原理的太阳-地球系统的物理模型，该模型具有从太阳表面到地球上层大气进行完整空间等离子体模拟的能力。起点将是一个现有的耦合模拟系统，其中一个磁层和太阳风的模型与一个内磁层和上层大气的模型耦合。数据同化将基于卡尔曼滤波方法。不能直接受数据约束的初始条件的变异性将从运行的集合中估计，以统计子空间识别技术为特征，然后通过经验随机模型建模。随机模型将与确定性模型相结合，产生一个混合模型，然后作为主要数据同化模型的基础。建模系统将按照开放网格服务体系结构设计协议来实现，这样它就可以在分布式计算网格上运行。目标之一是开发一种自适应系统，该系统可以根据需要结合额外的计算网格资源来响应磁层中的重大事件。如果成功，这项研究可能会为预测太空天气提供更好的工具。此外，该项目还包括与正规和非正规教育教育工具的开发人员建立伙伴关系，以创建有关空间天气主题的教学资源，供K-12学校和科学博物馆使用。该项目还包括与本科院校的教学人员建立联系，以便将数据和研究成果纳入空间物理学课程。