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

该项目支持基于电离层闪烁源调查的职业发展计划，使用结合基于物理的建模和机器学习（ML）的方法，并进一步预测空间天气影响，同时支持和加强学生的研究，教学和公众宣传在安柏瑞德航空大学（ERAU）。空间天气可影响地球上的不同技术，例如，地磁暴可改变全球导航卫星系统的信号（称为闪烁），降低其准确性和可靠性。该项目的主要教育目标将通过以下方式实现：指导普通大学生和研究生，开发一门新课程“数据科学和机器学习在空间气象预测中的应用”，以及在ERAU暑期学校和妇女参与科学Embry-Riddle（WiSER）方案中纳入一个新的空间气象部分。将通过为ERAU航空专业学生组织一次“空间天气对空中交通通信的影响”讲习班，并与代托纳海滩业余无线电俱乐部合作，激发学生和当地社区对空间天气及其对无线电通信的影响的热情，实现宣传目标。在利用全球导航卫星系统数据和建模来了解和预测造成高纬度电离层不规则现象的基本机制方面，所做的工作很少。 本研究的主要目标是使用逆方法，一个完整的三维前向传播模型沿着与数据集从几个已建立的GNSS接收器来训练机器学习（ML）算法聚类的不规则性的基础上，他们的来源，并协助预测在不同的时间和频率的闪烁。 因此，该项目将开发一种ML方法，并在预测空间天气影响方面使用最先进的基于物理的模型和反演方法对其进行增强。 无线电通信对许多民用和军事事业至关重要，因此通过不规则物理预测空间天气对通信的影响将被证明具有巨大的社会效益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.