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的放电（被称为巨大射流（GJs））如何将对流层和电离层耦合的科学。GJs是雷暴内部产生的大型放电，从云层顶部出现，并与较低的电离层（80 -100公里高度）相连。它们能够在对流层和电离层之间传输数百库仑的电荷（比典型闪电多10倍），直接将这些大气区域耦合起来。由于过去的观测技术很少观测，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，它们如何扰乱高层大气，以及它们如何传播到云顶以上的高海拔地区。根据该合同，除了多步骤验证外，还将使用地球同步闪电映射器（GLM）的光学数据和机器学习技术，在近半球范围内探测gj。探测管道每年将有能力识别数千个gj，比以前的观测结果高出几个数量级。大规模的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，如航空学、大气化学和气象学。作为该奖项更广泛的教育推广的一部分，来自一级学校（贫困儿童比例高的学校）的K-12教师将通过现有的国家科学基金会资助的项目（教师研究经验）每年夏天进行一个研究项目，并将他们所学到的知识纳入课程中。研究生也将参与这个项目。该项目的三个主要目标是：1)在开发了一个强大的管道，使用GLM数据和机器学习分类器对数千个gj进行检测后，构建gj的气气学。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。大规模探测将通过一个管道进行，该管道将GLM与机器学习算法结合使用，并通过地面无线电网络进行多步验证。多步骤验证包括：将潜在gj与低频闪电网络在空间和时间上进行关联，以滤除非闪电事件；使用立体高度GLM模型进行验证（作为本工作的一部分开发）；并使用ELF无线电模型进行验证（作为这项工作的一部分开发）。利用VLF遥感对在现有VLF射电网络中传递的事件子集进行测量，将使用时空映射方法量化d区电子密度剖面的变化，并将其与杜克大学ELF射电网络测量的属性相关联。探测还将与地面仪器（如甚高频（VHF）闪电测绘阵列（LMA））和低地球轨道上的仪器（如大气空间相互作用监测器（ASIM））相关联，以了解事件的放电物理，如放电的前导和拖缆部分，以及它们是否与伽马射线有关。该奖项支持的研究将显著推进电离层如何响应来自下面的电脉冲的科学。该项目由地球科学理事会和先进网络基础设施办公室共同资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。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 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.