CEDAR: Examining the vertical structures of ionosphere-atmosphere coupling using decadal observations and ionospheric models

电离层通过屏蔽来自太阳的宇宙射线、高能粒子、X射线和极端紫外线来保护地球上的生命。电离层还通过反射无线电高频传输实现通信。然而，由于不同时间尺度上的自然变率所造成的干扰，如来自上方的太阳周期和太阳耀斑，或来自下方的潮汐波和人为二氧化碳效应，可能会破坏电离层的稳定性，并干扰民用活动，如导航、应急服务、精准农业和人造卫星系统。因此，准确的空间天气预报必须能够预测自然变率的影响。本项目通过地基和卫星观测研究电离层变化，并利用最先进的模式更好地了解太阳电磁变化和气候强迫对电离层变化可预测性的影响。将聘请一名本科生研究助理使用机器学习工具进行一些数据分析。为了向南加州代表性不足的群体推广航空科学，每年将为本科生到洛杉矶当地的国家实验室设施进行实地考察。本研究的目的是提高我们对太阳和低层大气扰动对全球电离层的影响的认识，特别关注与太阳周期、准两年一次振荡（QBO）、El Niño-Southern振荡（ENSO）和太平洋年代际振荡（PDO）相关的年际变化。该团队将利用国际参考电离层（IRI）再分析数据和过去20年的卫星观测来研究这些变化的垂直结构，包括NASA的GRACE， nsf赞助的COSMIC/FORMOSAT-3和COSMIC-2/FORMOSAT-7，以及德国航空航天中心的CHAMP卫星仪器。他们将利用IRI再分析和卫星数据得出与太阳周期、QBO、ENSO和PDO相关的电子密度垂直结构，并将这些观测结果与NCAR的TIME-GCM和WACCM-X标准模拟进行比较。观测到的年际变化将使用潮汐波通量和机器学习工具进行诊断。利用自定义的TIME-GCM和WACCM-X模式模拟电离层电子密度的年际变化，以阐明电离层与太阳电磁变化和气候强迫的化学动力学联系。深入了解年际对化学和动力分量的影响，有助于改进月至年代际的空间天气预报。该项目由地球科学理事会和先进网络基础设施办公室合作共同资助，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。

The ionosphere protects life on Earth by shielding cosmic rays, energetic particles, X rays, and extreme UV from the Sun. The ionosphere also enables communication by reflecting radio high-frequency transmissions. However, disturbances due to natural variabilities on vastly different time scales, such as the solar cycles and the solar flares from above, or the tidal waves and the anthropogenic CO2 effects from below, may disrupt the stability of the ionosphere, and interfere with civilian activities, such as navigation, emergency services, precision farming, and artificial satellite systems. Thus, an accurate space weather prediction must be able to predict the impacts of the natural variabilities. This project studies the ionospheric variabilities in ground-based and satellite observations and uses state-of-the-art models to better understand the impacts of the solar electromagnetic changes and the climate forcings on the predictability of the ionospheric variabilities. An undergraduate research assistant will be hired to perform some of the data analyses using machine-learning tools. Annual field trips to a local national lab facility in Los Angeles for undergraduates will be taken to promote aeronomy sciences among under-represented groups in Southern California.The goal of this investigation is to advance our understanding of the influence of solar and lower atmospheric disturbances on the global ionosphere, with particular focus on interannual variabilities related to the solar cycles, the quasi-biennial oscillation (QBO), the El Niño-Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). The team will study vertical structures of these variabilities using International Reference Ionosphere (IRI) reanalysis data and satellite observations from the last two decades, including NASA's GRACE, NSF-sponsored COSMIC/FORMOSAT-3 and COSMIC-2/FORMOSAT-7, and German Aerospace Center's CHAMP satellite instruments. They will derive the vertical structures of electron density related to the solar cycles, QBO, ENSO, and PDO using IRI reanalysis and satellite data and compare these observations with NCAR's TIME-GCM and WACCM-X standard simulations. The observed interannual variabilities will be diagnosed using tidal wave fluxes and machine-learning tools. The interannual variabilities in ionospheric electron density will be simulated using customized TIME-GCM and WACCM-X models to elucidate the chemo-dynamical connections of the ionosphere with solar electromagnetic changes and climate forcing. Thorough knowledge of the interannual impacts on the chemical and dynamical components can help to improve the space weather forecasts on monthly to decadal timescales.This project is co-funded through 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.