CAREER: Characterization of Sources of Ionospheric Scintillation and Space Weather Prediction through Analytics and Machine Learning

GSTDTAP

项目编号	1848207
	CAREER: Characterization of Sources of Ionospheric Scintillation and Space Weather Prediction through Analytics and Machine Learning
	Kshitija Deshpande (Principal Investigator)
主持机构	Embry-Riddle Aeronautical University
项目开始年	2019
	2019-07-15
项目结束日期	2024-06-30
资助机构	US-NSF
项目类别	Continuing grant
项目经费	68617(USD)
国家	美国
语种	英语
英文摘要	This project supports a CAREER development plan based upon an investigation of the sources of ionospheric scintillation using an approach that combines physics-based modeling and machine learning (ML), and further predicts space weather effects, while supporting and enhancing student research, teaching, and public outreach at Embry-Riddle Aeronautical University (ERAU). Space weather can affect different technologies on the Earth, for example, geomagnetic storms can alter the signals from Global Navigation Satellite Systems (GNSS) (called scintillation) and degrade their accuracy and reliability. The main educational goals of this project will be achieved through mentoring UG and graduate students, development of a new course "Application of data science and machine learning to space weather prediction," and including a new space weather component in the ERAU summer school and Women in Science Embry-Riddle (WiSER) program. The outreach goals will be achieved through organizing a workshop "Space weather effects on Air Traffic communication" for ERAU Aviation majors and collaborating with the Daytona Beach HAM radio club to enthuse students and local community on space weather and its effect on radio communications. While many attempts are underway to predict the global ionospheric state using Total Electron Content (TEC) and GNSS signal scintillation indices, little is done to understand and predict the local ionosphere in terms of the underlying mechanisms responsible for producing high latitude ionospheric irregularities using GNSS data and modeling. The principal objectives of this study are to use the inverse method, a full 3D forward propagation model along with datasets from several established GNSS receivers to train machine learning (ML) algorithms in clustering of irregularities based on their sources and to assist with predicting the scintillation at a different time and frequency. Thus, this project will develop an ML approach, and augment it with the state-of-the-art physics-based models and inversion method in predicting space weather effects. Radio communication is critical to many civilian and military endeavors, therefore predicting the space weather effects on communication through irregularity physics will prove to be of great societal benefit. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/213090
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Kshitija Deshpande .CAREER: Characterization of Sources of Ionospheric Scintillation and Space Weather Prediction through Analytics and Machine Learning.2019.