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极光降水的全球驱动:1. 源的平衡

Global Driving of Auroral Precipitation: 1. Balance of Sources.

作者信息

Mukhopadhyay Agnit, Welling Daniel, Liemohn Michael, Ridley Aaron, Burleigh Meghan, Wu Chen, Zou Shasha, Connor Hyunju, Vandegriff Elizabeth, Dredger Pauline, Tóth Gabor

机构信息

Climate and Space Sciences and Engineering Department University of Michigan Ann Arbor MI USA.

NASA Goddard Space Flight Center Greenbelt MD USA.

出版信息

J Geophys Res Space Phys. 2022 Jul;127(7):e2022JA030323. doi: 10.1029/2022JA030323. Epub 2022 Jul 11.

DOI:10.1029/2022JA030323
PMID:36248015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9539890/
Abstract

The accurate determination of auroral precipitation in global models has remained a daunting and rather inexplicable obstacle. Understanding the calculation and balance of multiple sources that constitute the aurora, and their eventual conversion into ionospheric electrical conductance, is critical for improved prediction of space weather events. In this study, we present a semi-physical global modeling approach that characterizes contributions by four types of precipitation-monoenergetic, broadband, electron, and ion diffuse-to ionospheric electrodynamics. The model uses a combination of adiabatic kinetic theory and loss parameters derived from historical energy flux patterns to estimate auroral precipitation from magnetohydrodynamic (MHD) quantities. It then converts them into ionospheric conductance that is used to compute the ionospheric feedback to the magnetosphere. The model has been employed to simulate the 5-7 April 2010 space weather event. Comparison of auroral fluxes show good agreement with observational data sets like NOAA-DMSP and OVATION Prime. The study shows a dominant contribution by electron diffuse precipitation, accounting for ∼74% of the auroral energy flux. However, contributions by monoenergetic and broadband sources dominate during times of active upstream solar conditions, providing for up to 61% of the total hemispheric power. The study also finds a greater role played by broadband precipitation in ionospheric electrodynamics which accounts for ∼31% of the Pedersen conductance.

摘要

在全球模型中准确确定极光降水仍然是一个艰巨且相当难以解释的障碍。了解构成极光的多种源的计算和平衡,以及它们最终如何转化为电离层导电率,对于改进空间天气事件的预测至关重要。在本研究中,我们提出了一种半物理全球建模方法,该方法描述了四种类型的降水——单能、宽带、电子和离子扩散——对电离层电动力学的贡献。该模型结合了绝热动力学理论和从历史能量通量模式导出的损失参数,以根据磁流体动力学(MHD)量来估计极光降水。然后将其转换为电离层导电率,用于计算电离层对磁层的反馈。该模型已被用于模拟2010年4月5日至7日的空间天气事件。极光通量的比较表明与诸如NOAA - DMSP和OVATION Prime等观测数据集具有良好的一致性。研究表明电子扩散降水占主导,占极光能量通量的约74%。然而,在活跃的上游太阳条件期间,单能和宽带源的贡献占主导,提供了高达61%的总半球功率。该研究还发现宽带降水在电离层电动力学中发挥了更大作用,占佩德森导电率的约31%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/cad12e79cee2/JGRA-127-e2022JA030323-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/fe0a0218b28d/JGRA-127-e2022JA030323-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/3c98d6eb9f0d/JGRA-127-e2022JA030323-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/748015b06732/JGRA-127-e2022JA030323-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/7ecc060b5ff3/JGRA-127-e2022JA030323-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/4ca53f2de8f4/JGRA-127-e2022JA030323-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/cad12e79cee2/JGRA-127-e2022JA030323-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/fe0a0218b28d/JGRA-127-e2022JA030323-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/7d1555dd36e0/JGRA-127-e2022JA030323-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/6e6fea5c37e6/JGRA-127-e2022JA030323-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/576bd02eeebd/JGRA-127-e2022JA030323-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/3c98d6eb9f0d/JGRA-127-e2022JA030323-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/bc8b059827df/JGRA-127-e2022JA030323-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/748015b06732/JGRA-127-e2022JA030323-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/7ecc060b5ff3/JGRA-127-e2022JA030323-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/fe44fada9034/JGRA-127-e2022JA030323-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/4ca53f2de8f4/JGRA-127-e2022JA030323-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac05/9539890/cad12e79cee2/JGRA-127-e2022JA030323-g008.jpg

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