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Journal of geophysical research. Space physics, 2018-12, Vol.123 (12), p.10,200-10,212
2018
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Autor(en) / Beteiligte
Titel
A New Method to Retrieve Thermospheric Parameters From Daytime Bottom‐Side Ne(h) Observations
Ist Teil von
  • Journal of geophysical research. Space physics, 2018-12, Vol.123 (12), p.10,200-10,212
Ort / Verlag
Washington: Blackwell Publishing Ltd
Erscheinungsjahr
2018
Quelle
Wiley Online Library All Journals
Beschreibungen/Notizen
  • A new method to extract neutral composition (O, O2, N2), exospheric temperature Tex, vertical plasma drift, W, and the total solar Extreme Ultraviolet flux with λ ≤ 1050 Å from routine ionosonde bottom‐side electron density, Ne(h), observations has been proposed. The method can be used around noontime hours for all months of the year at middle latitudes where the ionospheric F‐layer is formed by solar Extreme Ultraviolet radiation. The uncertainty of the retrieved neutral gas density coincides with the announced Mean Relative Deviation ±(10‐15%) of CHAMP/STAR neutral gas density observations. The method also provides statistically significant better results in a comparison with modern Mass‐Spectrometer‐Incoherent‐Scatter, Jacchia‐Bowman 2008, and Drag Temperature Model 2013 empirical models. The thermospheric parameters retrieved for the St. Patrick Day magnetic storm and two so‐called Q‐disturbance periods are given as an example of the method application. The retrieved neutral gas densities for the St. Patrick Day storm are compared to Swarm‐B accelerometer observations. The proposed method may be considered as a useful tool for analyses of the state of the upper atmosphere under various geophysical conditions. Key Points A new method to extract a self‐consistent set of aeronomic parameters (Tex, O, O2, N2, W, and EUV flux) from ionosonde observations has been proposed The method is applied for all seasons at middle and lower latitudes where the ionospheric F‐layer is formed by solar EUV radiation Testing of the method on CHAMP/STAR neutral gas density has shown better accuracy than modern empirical models MSISE00, JB2008, DTM2013

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