Seasonal variations of gravity wave structures in OH airglow with a CCD imager at Shigaraki
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1880-5981
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Seasonal variations of gravity wave structures in OH airglow with a CCD imager at Shigaraki ; volume:51 ; number:7-8 ; day:26 ; month:6 ; year:2014 ; pages:897-906 ; date:7.1999
Earth, planets and space ; 51, Heft 7-8 (26.6.2014), 897-906, 7.1999
- Creator
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Nakamura, T.
- Contributor
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Higashikawa, A.
Tsuda, T.
Matsushita, Y.
SpringerLink (Online service)
- DOI
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10.1186/BF03353248
- URN
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urn:nbn:de:1111-2016073164333
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:51 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Nakamura, T.
- Higashikawa, A.
- Tsuda, T.
- Matsushita, Y.
- SpringerLink (Online service)
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Development of airglow temperature photometers with cooled-CCD detectors
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A fast SWIR imager for observations of transient features in OH airglow
Analysis of 2D airglow imager data with respect to dynamics using machine learning
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A novel approach for the extraction of cloud motion vectors using airglow imager measurements
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MLT gravity wave climatology in the South America equatorial region observed by airglow imager
Seasonal and local time variability of ripples from airglow imager observations in US and Japan
Development of airglow temperature photometers with cooled-CCD detectors
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Gravity wave instability structures and turbulence from more than 1.5 years of OH* airglow imager observations in Slovenia
Observation of equatorial plasma bubbles by the airglow imager on ISS-IMAP
A fast SWIR imager for observations of transient features in OH airglow
Analysis of 2D airglow imager data with respect to dynamics using machine learning
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