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

该项目是与洛斯阿拉莫斯国家实验室的科学家合作开展的一项工作，目的是开发一个实时模型，用于确定和预测地球辐射带的状况。 该项目将利用数据同化方法结合物理模型和现代机器学习技术来研究辐射带中高能电子的加速、传输和损失过程。 由此产生的模型将包括全系统预报的置信限，从而使其结果对实际空间气象预报有用。 该项目将通过减少业务费用改进辐射带预测，造福社会，预测将在网上免费提供，该项目包括一个强有力的教育和教学部分，由科罗拉多大学的研究生参与。 此外，该研究将与洛斯阿拉莫斯空间科学推广（LASSO）计划相结合，该计划为代表性不足的团体和残疾人提供教师培训。

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.