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

合作研究：NSWP--用于实时辐射带预报的机器学习和数据同化

• 批准号: 1131869
• 负责人: Xinlin Li
• 金额: $ 17万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1131869&HistoricalAwards=false
• 关键词: Collaborative; Research; NSWP; Machine; Learning

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.

该项目是与洛斯阿拉莫斯国家实验室（Los Alamos National Laboratory）的科学家进行的合作努力，以开发一种实时模型，以指定和预测地球辐射带的状态。 该项目将与物理模型和现代机器学习技术一起利用数据同化方法来检查辐射带中能量电子的加速，运输和损失过程。 最终的模型将包括对整个系统预测的置信度限制，从而使结果对操作空间天气的倾向有用。 该项目将通过在降低运营成本下进行改进的放射线预测来使社会受益，并在网上免费提供预测。该项目包括科罗拉多大学的研究生参与，包括强大的教育和教学组成部分。 此外，该研究将与Los Alalmos太空科学外展（Lasso）计划集成，该计划为代表性不足的群体和残疾人提供教师培训。