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

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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.