On the Variation of Column Density Ratio ΣO/N2 in the Upper Atmosphere Using Principal Component Analysis in 2‐Dimensional Images
The variability of the thermosphere on a daily basis is influenced by a variety of factors, including solar, geomagnetic, and meteorological drivers. The column density ratio of atomic oxygen to molecular nitrogen (ΣO/N2) is a useful parameter for quantifying this variability, and has been shown to...
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description | The variability of the thermosphere on a daily basis is influenced by a variety of factors, including solar, geomagnetic, and meteorological drivers. The column density ratio of atomic oxygen to molecular nitrogen (ΣO/N2) is a useful parameter for quantifying this variability, and has been shown to closely correspond to F‐region electron density, total electron content, and upper atmospheric transport. Despite the significance of the ΣO/N2, the relative contributions of these drivers to thermospheric variability are not well understood. In order to shed light on this issue, principal component analysis was performed in this study to distinguish and rank the various sources of variability in the ΣO/N2. The analysis was based on the ΣO/N2 data from the Global‐scale Observations of the Limb and Disk mission from days 81–135 of 2020. The resulting two‐dimensional eigen spatial patterns reveal the dominant variabilities during the specified period. The first six principal components are reported and associated with the major drivers through their spatial and temporal features. Geomagnetic storms, interhemispheric transport, atmospheric tides, and planetary waves were identified as the drivers of the first, second, third, and fifth components, respectively. The order of these components highlights that geomagnetic activity is the dominant source of daily variability in the ΣO/N2, followed by interhemispheric transport and meteorological drivers from the lower atmosphere.
Plain Language Summary
Day‐to‐day variability in the ionosphere and thermosphere is driven by changes in solar radiation, the solar wind, and by meteorological forcing from the lower atmosphere. The thermospheric ΣO/N2 responds to changes in thermospheric circulation and vertical transport, which are modified by the aforementioned drivers. We use principal component analysis, the same algorithm used in facial recognition technology, to identify the day‐to‐day variability of the thermospheric ΣO/N2 from NASA's Global‐scale Observations of the Limb and Disk mission. With this powerful open‐source tool, we identify the characteristic spatial variations of ΣO/N2. Our study reveals a strong response to a geomagnetic storm and a smaller response to a quasi 6‐day atmospheric wave, demonstrating the relative importance of geomagnetic effects compared to planetary scale waves, present in components 1 and 5, respectively.
Key Points
A principal component analysis for two dimensional images is applied to t |
doi_str_mv | 10.1029/2022JA031037 |
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Plain Language Summary
Day‐to‐day variability in the ionosphere and thermosphere is driven by changes in solar radiation, the solar wind, and by meteorological forcing from the lower atmosphere. The thermospheric ΣO/N2 responds to changes in thermospheric circulation and vertical transport, which are modified by the aforementioned drivers. We use principal component analysis, the same algorithm used in facial recognition technology, to identify the day‐to‐day variability of the thermospheric ΣO/N2 from NASA's Global‐scale Observations of the Limb and Disk mission. With this powerful open‐source tool, we identify the characteristic spatial variations of ΣO/N2. Our study reveals a strong response to a geomagnetic storm and a smaller response to a quasi 6‐day atmospheric wave, demonstrating the relative importance of geomagnetic effects compared to planetary scale waves, present in components 1 and 5, respectively.
Key Points
A principal component analysis for two dimensional images is applied to thermospheric column O/N2 ratio to characterize its variation
64% of the variability in a 55‐day period is captured in first 6 components with highest explained variances
Indications of auroral forcing, seasonal trends, atmospheric tides and planetary waves are identified in components 1, 2, 3, and 5</description><identifier>ISSN: 2169-9380</identifier><identifier>EISSN: 2169-9402</identifier><identifier>DOI: 10.1029/2022JA031037</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Atmosphere ; Atmospheric circulation ; Atmospheric tides ; Atmospheric transport ; Atmospheric waves ; Atomic oxygen ; Density ratio ; Dimensional analysis ; Electron density ; Face recognition ; Geomagnetic activity ; geomagnetic disturbances ; Geomagnetic effects ; Geomagnetic storms ; Geomagnetism ; Ionosphere ; Lower atmosphere ; Magnetic effects ; Magnetic storms ; NASA's GOLD mission ; Oxygen ; O‐to‐N2 ratio ; Planetary waves ; principal component analysis ; Principal components analysis ; Solar radiation ; Solar wind ; Thermosphere ; Thermospheric circulation ; Upper atmosphere ; Variability</subject><ispartof>Journal of geophysical research. Space physics, 2023-06, Vol.128 (6), p.n/a</ispartof><rights>2023 The Authors.</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-8158-5666 ; 0000-0002-9951-9763 ; 0000-0001-5596-4403 ; 0000-0002-2329-5230 ; 0000-0001-5336-0040 ; 0000-0003-2558-448X ; 0000-0002-1293-9379</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2022JA031037$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2022JA031037$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Goel, Divyam</creatorcontrib><creatorcontrib>Wu, Yen‐Jung J.</creatorcontrib><creatorcontrib>Harding, Brian J.</creatorcontrib><creatorcontrib>Triplett, Colin C.</creatorcontrib><creatorcontrib>Immel, Thomas J.</creatorcontrib><creatorcontrib>Cullens, Chihoko</creatorcontrib><creatorcontrib>England, Scott</creatorcontrib><title>On the Variation of Column Density Ratio ΣO/N2 in the Upper Atmosphere Using Principal Component Analysis in 2‐Dimensional Images</title><title>Journal of geophysical research. Space physics</title><description>The variability of the thermosphere on a daily basis is influenced by a variety of factors, including solar, geomagnetic, and meteorological drivers. The column density ratio of atomic oxygen to molecular nitrogen (ΣO/N2) is a useful parameter for quantifying this variability, and has been shown to closely correspond to F‐region electron density, total electron content, and upper atmospheric transport. Despite the significance of the ΣO/N2, the relative contributions of these drivers to thermospheric variability are not well understood. In order to shed light on this issue, principal component analysis was performed in this study to distinguish and rank the various sources of variability in the ΣO/N2. The analysis was based on the ΣO/N2 data from the Global‐scale Observations of the Limb and Disk mission from days 81–135 of 2020. The resulting two‐dimensional eigen spatial patterns reveal the dominant variabilities during the specified period. The first six principal components are reported and associated with the major drivers through their spatial and temporal features. Geomagnetic storms, interhemispheric transport, atmospheric tides, and planetary waves were identified as the drivers of the first, second, third, and fifth components, respectively. The order of these components highlights that geomagnetic activity is the dominant source of daily variability in the ΣO/N2, followed by interhemispheric transport and meteorological drivers from the lower atmosphere.
Plain Language Summary
Day‐to‐day variability in the ionosphere and thermosphere is driven by changes in solar radiation, the solar wind, and by meteorological forcing from the lower atmosphere. The thermospheric ΣO/N2 responds to changes in thermospheric circulation and vertical transport, which are modified by the aforementioned drivers. We use principal component analysis, the same algorithm used in facial recognition technology, to identify the day‐to‐day variability of the thermospheric ΣO/N2 from NASA's Global‐scale Observations of the Limb and Disk mission. With this powerful open‐source tool, we identify the characteristic spatial variations of ΣO/N2. Our study reveals a strong response to a geomagnetic storm and a smaller response to a quasi 6‐day atmospheric wave, demonstrating the relative importance of geomagnetic effects compared to planetary scale waves, present in components 1 and 5, respectively.
Key Points
A principal component analysis for two dimensional images is applied to thermospheric column O/N2 ratio to characterize its variation
64% of the variability in a 55‐day period is captured in first 6 components with highest explained variances
Indications of auroral forcing, seasonal trends, atmospheric tides and planetary waves are identified in components 1, 2, 3, and 5</description><subject>Algorithms</subject><subject>Atmosphere</subject><subject>Atmospheric circulation</subject><subject>Atmospheric tides</subject><subject>Atmospheric transport</subject><subject>Atmospheric waves</subject><subject>Atomic oxygen</subject><subject>Density ratio</subject><subject>Dimensional analysis</subject><subject>Electron density</subject><subject>Face recognition</subject><subject>Geomagnetic activity</subject><subject>geomagnetic disturbances</subject><subject>Geomagnetic effects</subject><subject>Geomagnetic storms</subject><subject>Geomagnetism</subject><subject>Ionosphere</subject><subject>Lower atmosphere</subject><subject>Magnetic effects</subject><subject>Magnetic storms</subject><subject>NASA's GOLD mission</subject><subject>Oxygen</subject><subject>O‐to‐N2 ratio</subject><subject>Planetary waves</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>Solar radiation</subject><subject>Solar wind</subject><subject>Thermosphere</subject><subject>Thermospheric circulation</subject><subject>Upper atmosphere</subject><subject>Variability</subject><issn>2169-9380</issn><issn>2169-9402</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNpNUEtOwzAQjRBIVIUdB7DEOtTf2FlGLZRWFUUVZRs5jdu6Smxjp0LdseAA3IH7cAhOQqKCxGxm5s17T6MXRVcI3iCI0wGGGE8zSBAk_CTqYZSkcUohPv2biYDn0WUIO9iWaCHEetH73IBmq8Cz9Fo22hpg12Boq31twEiZoJsDWHQH8PU5HzxgoI_8pXPKg6ypbXBb5VsgaLMBj16blXayaj1qZ40yDciMrA5Bh06Kv98-RrrujG0Lg0ktNypcRGdrWQV1-dv70fLu9ml4H8_m48kwm8UOCchjxlUpeMlowihRRVlStiog5SmCDKpCpoxingiORQEFJ4lCkpeobJdEULpGpB9dH32dty97FZp8Z_e-_SPkWBBIGOVCtCxyZL3qSh1y53Ut_SFHMO9yzv_nnE_Hi4zxFHLyA7fOciI</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Goel, Divyam</creator><creator>Wu, Yen‐Jung J.</creator><creator>Harding, Brian J.</creator><creator>Triplett, Colin C.</creator><creator>Immel, Thomas J.</creator><creator>Cullens, Chihoko</creator><creator>England, Scott</creator><general>Blackwell Publishing Ltd</general><scope>24P</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-8158-5666</orcidid><orcidid>https://orcid.org/0000-0002-9951-9763</orcidid><orcidid>https://orcid.org/0000-0001-5596-4403</orcidid><orcidid>https://orcid.org/0000-0002-2329-5230</orcidid><orcidid>https://orcid.org/0000-0001-5336-0040</orcidid><orcidid>https://orcid.org/0000-0003-2558-448X</orcidid><orcidid>https://orcid.org/0000-0002-1293-9379</orcidid></search><sort><creationdate>202306</creationdate><title>On the Variation of Column Density Ratio ΣO/N2 in the Upper Atmosphere Using Principal Component Analysis in 2‐Dimensional Images</title><author>Goel, Divyam ; Wu, Yen‐Jung J. ; Harding, Brian J. ; Triplett, Colin C. ; Immel, Thomas J. ; Cullens, Chihoko ; England, Scott</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p1807-57ed87d546543ebdd45cb04791050eba9542768728b08736e1a7d1db086844f13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Atmosphere</topic><topic>Atmospheric circulation</topic><topic>Atmospheric tides</topic><topic>Atmospheric transport</topic><topic>Atmospheric waves</topic><topic>Atomic oxygen</topic><topic>Density ratio</topic><topic>Dimensional analysis</topic><topic>Electron density</topic><topic>Face recognition</topic><topic>Geomagnetic activity</topic><topic>geomagnetic disturbances</topic><topic>Geomagnetic effects</topic><topic>Geomagnetic storms</topic><topic>Geomagnetism</topic><topic>Ionosphere</topic><topic>Lower atmosphere</topic><topic>Magnetic effects</topic><topic>Magnetic storms</topic><topic>NASA's GOLD mission</topic><topic>Oxygen</topic><topic>O‐to‐N2 ratio</topic><topic>Planetary waves</topic><topic>principal component analysis</topic><topic>Principal components analysis</topic><topic>Solar radiation</topic><topic>Solar wind</topic><topic>Thermosphere</topic><topic>Thermospheric circulation</topic><topic>Upper atmosphere</topic><topic>Variability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goel, Divyam</creatorcontrib><creatorcontrib>Wu, Yen‐Jung J.</creatorcontrib><creatorcontrib>Harding, Brian J.</creatorcontrib><creatorcontrib>Triplett, Colin C.</creatorcontrib><creatorcontrib>Immel, Thomas J.</creatorcontrib><creatorcontrib>Cullens, Chihoko</creatorcontrib><creatorcontrib>England, Scott</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of geophysical research. Space physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goel, Divyam</au><au>Wu, Yen‐Jung J.</au><au>Harding, Brian J.</au><au>Triplett, Colin C.</au><au>Immel, Thomas J.</au><au>Cullens, Chihoko</au><au>England, Scott</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the Variation of Column Density Ratio ΣO/N2 in the Upper Atmosphere Using Principal Component Analysis in 2‐Dimensional Images</atitle><jtitle>Journal of geophysical research. Space physics</jtitle><date>2023-06</date><risdate>2023</risdate><volume>128</volume><issue>6</issue><epage>n/a</epage><issn>2169-9380</issn><eissn>2169-9402</eissn><abstract>The variability of the thermosphere on a daily basis is influenced by a variety of factors, including solar, geomagnetic, and meteorological drivers. The column density ratio of atomic oxygen to molecular nitrogen (ΣO/N2) is a useful parameter for quantifying this variability, and has been shown to closely correspond to F‐region electron density, total electron content, and upper atmospheric transport. Despite the significance of the ΣO/N2, the relative contributions of these drivers to thermospheric variability are not well understood. In order to shed light on this issue, principal component analysis was performed in this study to distinguish and rank the various sources of variability in the ΣO/N2. The analysis was based on the ΣO/N2 data from the Global‐scale Observations of the Limb and Disk mission from days 81–135 of 2020. The resulting two‐dimensional eigen spatial patterns reveal the dominant variabilities during the specified period. The first six principal components are reported and associated with the major drivers through their spatial and temporal features. Geomagnetic storms, interhemispheric transport, atmospheric tides, and planetary waves were identified as the drivers of the first, second, third, and fifth components, respectively. The order of these components highlights that geomagnetic activity is the dominant source of daily variability in the ΣO/N2, followed by interhemispheric transport and meteorological drivers from the lower atmosphere.
Plain Language Summary
Day‐to‐day variability in the ionosphere and thermosphere is driven by changes in solar radiation, the solar wind, and by meteorological forcing from the lower atmosphere. The thermospheric ΣO/N2 responds to changes in thermospheric circulation and vertical transport, which are modified by the aforementioned drivers. We use principal component analysis, the same algorithm used in facial recognition technology, to identify the day‐to‐day variability of the thermospheric ΣO/N2 from NASA's Global‐scale Observations of the Limb and Disk mission. With this powerful open‐source tool, we identify the characteristic spatial variations of ΣO/N2. Our study reveals a strong response to a geomagnetic storm and a smaller response to a quasi 6‐day atmospheric wave, demonstrating the relative importance of geomagnetic effects compared to planetary scale waves, present in components 1 and 5, respectively.
Key Points
A principal component analysis for two dimensional images is applied to thermospheric column O/N2 ratio to characterize its variation
64% of the variability in a 55‐day period is captured in first 6 components with highest explained variances
Indications of auroral forcing, seasonal trends, atmospheric tides and planetary waves are identified in components 1, 2, 3, and 5</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2022JA031037</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-8158-5666</orcidid><orcidid>https://orcid.org/0000-0002-9951-9763</orcidid><orcidid>https://orcid.org/0000-0001-5596-4403</orcidid><orcidid>https://orcid.org/0000-0002-2329-5230</orcidid><orcidid>https://orcid.org/0000-0001-5336-0040</orcidid><orcidid>https://orcid.org/0000-0003-2558-448X</orcidid><orcidid>https://orcid.org/0000-0002-1293-9379</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Atmosphere Atmospheric circulation Atmospheric tides Atmospheric transport Atmospheric waves Atomic oxygen Density ratio Dimensional analysis Electron density Face recognition Geomagnetic activity geomagnetic disturbances Geomagnetic effects Geomagnetic storms Geomagnetism Ionosphere Lower atmosphere Magnetic effects Magnetic storms NASA's GOLD mission Oxygen O‐to‐N2 ratio Planetary waves principal component analysis Principal components analysis Solar radiation Solar wind Thermosphere Thermospheric circulation Upper atmosphere Variability |
title | On the Variation of Column Density Ratio ΣO/N2 in the Upper Atmosphere Using Principal Component Analysis in 2‐Dimensional Images |
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