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

CEDAR：使用十年观测和电离层模型检查电离层-大气耦合的垂直结构

• 批准号: 2230265
• 负责人: King-Fai Li
• 金额: $ 40.61万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230265&HistoricalAwards=false
• 关键词: CEDAR; Examining; vertical; structures; ionosphere

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.

电离层通过屏蔽宇宙射线、高能粒子、X射线和极端紫外线来保护地球上的生命不受太阳的影响。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化造成的扰动，例如来自上方的太阳周期和太阳耀斑，或者来自下方的潮汐波和人为二氧化碳效应，可能会扰乱电离层的稳定，并干扰民用活动，如导航、紧急服务、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化的影响。该项目研究地面和卫星观测中的电离层可变性，并使用最先进的模型，以更好地了解太阳电磁变化和气候强迫对电离层可变性的可预测性的影响。将聘请一名本科生研究助理使用机器学习工具执行一些数据分析。这项研究的目的是增进我们对太阳和低层大气扰动对全球电离层的影响的理解，特别关注与太阳周期、准两年振荡(QBO)、厄尔尼诺-南方涛动(ENSO)和太平洋年代际振荡(PDO)有关的年际变化。该小组将利用国际参考电离层再分析数据和过去二十年的卫星观测，包括美国国家航空航天局的GRACE、美国国家科学基金会赞助的COSMIC/FORMOSAT-3和COSMIC-2/FORMOSAT-7，以及德国航空航天中心的CHAMP卫星仪器，研究这些变化的垂直结构。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有关的电子密度的垂直结构，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波通量和机器学习工具进行诊断。电离层电子密度的年际变化将使用定制的TIME-GCM和WACCM-X模式进行模拟，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。对化学和动力成分的年际影响的透彻了解有助于改进每月到十年时间尺度的空间天气预报。该项目由地球科学局和高级网络基础设施办公室合作共同资助，以支持地球科学中的人工智能/ML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。