Collaborative Research: NSWP--Machine Learning and Data Assimilation for Real-Time Radiation Belt Forecasting

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This project is a collaborative effort with scientists at the Los Alamos National Laboratory to develop a real-time model for specifying and forecasting the state of Earth's radiation belt. The project will utilize data assimilation methods in conjunction with physical models and modern machine learning techniques to examine the acceleration, transport and loss processes for energetic electrons in the radiation belt. The resulting model will include confidence limits on the system-wide forecasts, making the results useful for operational space weather predicitions. The project will benefit society by making improved radiation-belt predictions at reduced operational costs and the forecasts will be made freely available online.The project includes a strong educational and teaching component through the participation of graduate students at the University of Colorado. In addition the research will be integrated with the Los Alalmos Space Science Outreach (LASSO) program, which offers teacher training to underrepresented groups and individuals with disabilities.