<formula alphabet="latin">He(23S) densities in the upper thermosphere: Updates in modeling capabilities and comparisons with midlatitude observations

Details <formula alphabet="latin">He(23S) densities in the upper thermosphere: Updates in modeling capabilities and comparisons with midlatitude observations <formula alphabet="latin">He(23S) densities in the upper thermosphere: Updates in modeling capabilities and comparisons with midlatitude observations

Aug 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999jgr...10417157b&link_type=abstract

Journal of Geophysical Research, Volume 104, Issue A8, p. 17157-17172

Physics

2

Atmospheric Composition And Structure: Thermosphere-Composition And Chemistry, Atmospheric Composition And Structure: Airglow And Aurora

Scientific paper

Metastable He(23S) atoms in the upper day-lit thermosphere can attain sufficiently high densities to be observed from the ground at twilight via resonant scattering of solar 1083 nm photons. Modeling the He(23S) density profile requires knowledge of neutral helium abundances, rates for excitation to the 23S metastable state, and transport coefficients for diffusion through the ambient atmosphere. In this paper, updates in the authors' modeling procedures are described. In particular, collision cross sections (total elastic scattering and momentum transfer) have been computed for He(23S)+He(11S) via partial wave expansion using realistic interaction potentials, from which the binary collision parameter has been calculated. He(23S) production via e--He+ radiative recombination is also reassessed and compared with production by photoelectron impact. The role of diffusive transport in determining He(23S) density profiles is illustrated via comparative modeling of midlatitude, winter hemisphere data sets obtained under solar maximum and minimum conditions. These data-model comparisons reveal the sensitivity of the modeling technique to geophysical quantities of interest (upper atmospheric He densities and photoelectron flux magnitudes) and identify remaining modeling uncertainties.

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