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

该奖项旨在推进云到电离层放电（称为巨型射流（GJ））如何耦合对流层和电离层的科学。GJ是雷暴内部开始的大型放电，从云层顶部出现，并与低电离层（80 - 100公里高度）连接。 它们能够在对流层和电离层之间转移数百个库隆布的电荷（比典型的闪电多10倍），直接耦合这些大气区域。由于过去的观测技术很少观测到这些事件，因此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以上的高空。该奖项下的工作将涉及使用来自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验证来检测几乎半球尺度的GJ。探测管道将有能力每年识别数千个GJ，比以前的观测多几个数量级。大规模的探测数据库将向公众开放并广泛传播，从而对其他研究领域产生重大影响，如高空大气学（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，通过现有的NSF赞助计划（教师研究经验），来自标题1学校（贫困儿童比例高的学校）的K-12教师将每年夏天开展一个研究项目，并将他们学到的东西纳入课程。研究生也将参与该项目。该项目的三个主要目标是：1）在开发一个强大的管道之后构建GJ的气候学，以使用GLM数据和机器学习分类器检测数千个GJ。2)量化GJ是否以及如何扰动D区电离层。3)研究GJ的物理特征。大规模的检测将通过一个管道进行，该管道使用GLM结合机器学习算法，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GJ与空间和时间上的低频（LF）闪电网络相关联，以过滤掉非闪电事件;使用立体高度GLM模型（作为本工作的一部分开发）进行验证;以及使用ELF无线电模型（作为本工作的一部分开发）进行验证。对在现有甚低频无线电网络内经过的事件子集进行甚低频遥感，将利用时空绘图方法量化D区电子密度分布的变化，并将其与杜克极低频无线电网络测量的特性相关联。探测结果还将与甚高频闪电测绘阵列等地面仪器和大气空间相互作用监测器等近地轨道仪器相关联，以了解事件的放电物理学，如放电的先导和流光部分，以及它们是否与伽马射线有关。该奖项支持的研究将大大推进电离层如何响应来自下方的电脉冲的科学。该项目由地球科学理事会和高级网络基础设施办公室合作共同资助，以支持AI/该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.