Empirical Neutral Thermosphere Models; Then and Now (Invited)

Details Empirical Neutral Thermosphere Models; Then and Now (Invited) Empirical Neutral Thermosphere Models; Then and Now (Invited)

Dec 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufmsa52a..04d&link_type=abstract

American Geophysical Union, Fall Meeting 2010, abstract #SA52A-04

Physics

Geophysics

[0328] Atmospheric Composition And Structure / Exosphere, [0355] Atmospheric Composition And Structure / Thermosphere: Composition And Chemistry, [0394] Atmospheric Composition And Structure / Instruments And Techniques, [3299] Mathematical Geophysics / General Or Miscellaneous

Scientific paper

The empirical Mass Spectrometer Incoherent Scatter (MSIS) upper atmospheric model provides a readily available framework for summarizing the results of five solar cycles of density, composition, and temperature information from multi-agency satellite missions, rocket flights, and ground-based observations. The MSIS versions described in Hedin et al. (1987), Hedin et al. (1991), and Picone et al., (2002) have been cited over 2500 times in the peer reviewed scientific literature. In addition, the recently updated Horizontal Wind Model (HWM07) of Drob et al. (2008) provides a statistical representation of the horizontal wind fields from the ground to the exosphere (> 500 km), representing over 35-years of satellite, rocket, and ground-based wind measurements. Despite the success of these models they are not perfect, typically resulting from the lack of observational data at hand, as well as theoretical understanding. The differences between the model output and observational data can either be systematic (bias) or irregular (observational variance). In the latter case this is simply the natural geophysical variability that the models are not designed to represent. Published research clearly demonstrates that certain aspects of the MSIS (and HWM07) should be updated. Knowledge of these discrepancies, along with supporting observational data sets can be used to update and improve the fidelity of the models.

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