Collaborative Research: CEDAR: Investigation of Gigantic Jets, Their Ionospheric Effects, and How They Couple the Troposphere and Ionosphere

该奖项旨在推进被称为巨型喷流(GJS)的云到电离层的电子放电如何将对流层和电离层结合起来的科学。GJS是从雷暴内部开始，从云层顶部涌出，与较低电离层(高度80-100公里)连接的大电流放电。它们能够在对流层和电离层之间传输数百个库仑的电荷(比典型闪电多10倍)，直接连接这些大气区域。由于过去的观测技术很少观察到这些事件，关于这些事件仍然存在许多谜团，例如它们对全球电路(GEC)的影响，它们如何扰动高层大气，以及它们如何传播到云顶上方如此高的高度。该奖项下的工作将包括使用地球同步闪电映射器(GLM)的光学数据和机器学习技术，以及多步骤验证，在近半个半球的尺度上探测GJS。探测管道每年将有能力识别数千个GJ，比之前的观测多出几个数量级。大规模探测数据库将公开提供并广泛传播，从而对其他研究领域产生重大影响，如空气动力学(GEC)、大气化学和气象学。作为该奖项更广泛的教育推广活动的一部分，来自第一标题学校(贫困儿童比例较高的学校)的K-12教师将通过NSF赞助的现有计划(教师研究经验)在每年夏天从事一个研究项目，并将他们所学的内容纳入他们的课程。研究生也将参与到这个项目中来。这个项目的三个主要目标是：1)开发一个健壮的管道，使用GLM数据和一个机器学习分类器来检测数以千计的GJS气候，然后构建GJS的气候。2)量化GJS是否以及如何扰动D区电离层。3)研究GJS的物理特性。大规模检测将由一条管道执行，该管道将GLM与机器学习算法结合使用，并使用地面无线电网络进行多步验证。多步骤验证包括：将潜在的GJS与空间和时间上的低频(LF)闪电网络相关联，以过滤非闪电事件；使用立体高度GLM模式(作为本工作的一部分开发)进行验证；以及使用ELF无线电模式(作为本工作的一部分开发)进行验证。对现有甚低频无线电网络中通过的事件子集使用甚低频遥感，D区电子密度分布的变化将使用时间-空间映射方法进行量化，并与杜克ELF无线电网络测量的属性相关联。这些探测还将与甚高频(VHF)闪电测绘阵列等地面仪器和大气空间相互作用监测器(ASIM)等低地球轨道上的仪器相关联，以了解事件的放电物理，如放电的前导部分和流光部分，以及它们是否与伽马射线有关。该奖项支持的研究将极大地促进电离层如何对下面的电脉冲做出反应的科学研究。该项目由地球科学局和高级网络基础设施办公室共同资助，以支持人工智能/ML和地球科学领域的开放科学活动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award seeks to advance the science of how cloud-to-ionosphere electrical discharges known as gigantic jets (GJs) couple the troposphere and ionosphere. GJs are large electrical discharges that begin inside a thunderstorm, emerge from the top of the cloud, and connect with the lower ionosphere (80 -100 km altitude). They are capable of transferring hundreds of coulombs of charge (10 times more than typical lightning) between the troposphere and ionosphere, directly coupling these atmospheric regions. Due to infrequent observations from past observational techniques, many mysteries remain regarding these events, such as their effect on the global electric circuit (GEC), how they perturb the upper atmosphere, and how they propagate to such high altitudes above the cloud top. The work under this award will involve detecting GJs on nearly a hemispheric scale using optical data from the Geostationary Lightning Mappers (GLM) and machine learning techniques, in addition to multi-step validation. The detection pipeline will have the capability to identify thousands of GJs per year, orders of magnitude more than previous observations. The large-scale database of detections will be made publicly available and widely disseminated, allowing high impact on other fields of research such as aeronomy (GEC), atmospheric chemistry, and meteorology. As part of the broader educational outreach for this award, K-12 teachers from Title 1 schools (those with high percentages of children in poverty) via an existing NSF-sponsored program (Research Experience for Teachers) will work on a research project each summer and incorporate what they learned in their curriculum. Graduate students will be involved in the project as well. The three main goals of this project are: 1) Construct climatologies of GJs after developing a robust pipeline to detect them by the thousands using GLM data and a machine learning classier. 2) Quantify whether and how GJs perturb the D-region ionosphere. 3) Investigate the physical characteristics of GJs. The large-scale detection will be performed by a pipeline that uses GLM in conjunction with machine learning algorithms, and multi-step validation with ground-based radio networks. The multi-step validation consists of: correlating potential GJs with a low frequency (LF) lightning network in space and time to filter out non-lightning events; validating with a stereo altitude GLM model (developed as part of this work); and validating with an ELF radio model (developed as part of this work). Using VLF remote sensing on a subset of events that pass within an existing VLF radio network, changes to the electron density profile in the D-region will be quantified using temporal-spatial mapping methods and correlated to properties measured by the Duke ELF radio network. The detections will also be correlated with ground-based instruments such as very high frequency (VHF) lightning mapping arrays (LMA) and with instruments in low earth orbit such as the Atmospheric Space Interactions Monitor (ASIM) to understand the discharge physics of the events, such as the leader and streamer portions of the discharge and if they are associated with gamma rays. The research supported by this award will significantly advance the science of how the ionosphere responds to electrical impulses from below.This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.