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发的一个实时模型，用于指定和预测地球辐射带的状态。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、输运和损失过程。由此产生的模型将包括全系统预报的置信限，使结果对实际空间天气预报有用。该项目将以降低运营成本的方式改进辐射带预测，从而造福社会，预测结果将在网上免费提供。该项目通过科罗拉多大学研究生的参与，包括一个强大的教育和教学组成部分。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群体和残疾个人提供教师培训。

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.