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改进空间气象建模。 目的是开发一个基于物理学的太阳-地球系统基本原理模型，该模型能够对从太阳表面到地球高层大气的空间等离子体进行完整的模拟。 起点将是现有的耦合建模系统，其中磁层和太阳风模型与内磁层模型和高层大气模型耦合。 数据同化将以卡尔曼滤波方法为基础。 初始条件的可变性，不能直接约束的数据将估计从合奏的运行，其特征在于统计子空间识别技术，然后由经验随机模型建模。 随机模型将与确定性模型相结合，以产生一个混合模型，该模型将成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实现，以便它可以在分布式计算网格上运行。 目标之一是开发一个自适应系统，通过根据需要整合额外的计算网格资源，可以对磁层中的重大事件作出反应。 如果成功，这项研究可能会导致更好的预测空间天气的工具。 此外，该项目还包括与正规和非正规教育教育工具开发商建立伙伴关系，以编制供幼儿园至12年级学校和科学博物馆使用的空间气象专题教学资源。 该项目还包括与本科院校教员建立联系，以便将数据和研究成果纳入空间物理学课程。