Correlations Between the Thermosphere's Semi-Annual Density Variations and Infrared Emissions Measured with the SABER Instrument
datasetposted on 25.02.2021, 10:04 by Daniel Weimer
These data files provide the processed data shown in Figures 2 through 11 in the publication'Correlations Between the Thermosphere's Semi-Annual Density Variations and Infrared Emissions Measured with the SABER Instrument' in the Journal of Geophysical Research Space Physics. These files are in NetCDF format. The abstract from the publication follows: This paper presents measurements of the amplitudes and timings of the combined, annual and semi-annual variations of thermospheric neutral density, and a comparison of these density variations with measurements of the infrared emissions from carbon dioxide and nitric oxide in the thermosphere. The density values were obtained from measurements of the atmospheric drag experienced by the CHAMP, GRACE A, GOCE, and three Swarm satellites, while the optical emissions were measured with the SABER instrument on the TIMED satellite. These data span a time period of 16 years. A database containing global average densities that were derived from the orbits of about 5000 objects [Emmert, 2009, 2015b] was employed for calibrating these density data. A comparison with the NRLMSISE-00 model was used to derive measurements of how much the density changes over time due to these seasonal variations. It is found that the seasonal density oscillations have significant variations in amplitude and timing. In order to test the practicality of using optical emissions as a monitoring tool, the SABER data were fit to the measured variations. Even the most simple fit that used only filtered carbon dioxide emissions had good correlations with the measured oscillations. However, the density oscillations were also well-predicted by a simple Fourier series, contrary to original expectations. Nevertheless, measurements of the optical emissions from the thermosphere are expected to have a role in future understanding and prediction of the semi-annual variations.