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

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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.