Statistics – Applications
Scientific paper
Dec 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufmsa13a..03f&link_type=abstract
American Geophysical Union, Fall Meeting 2010, abstract #SA13A-03
Statistics
Applications
[3369] Atmospheric Processes / Thermospheric Dynamics, [7959] Space Weather / Models, [7969] Space Weather / Satellite Drag
Scientific paper
The largest uncertainty in determining orbits for satellites operating in low Earth orbit is the atmospheric drag. Drag is the most difficult force to model mainly because of the complexity of neutral atmosphere variations driven by solar radiative power, magnetospheric energy inputs, and the propagation from below of lower atmosphere waves. Neutral density models used routinely in orbit determination applications are mainly empirical. These models are based on a database of historical observations, to which parametric equations have been fitted, representing the known thermospheric variations with local time, latitude, season, solar and geomagnetic activity. Changes in solar and geomagnetic activity are represented by their proxies F10.7 or extreme (EUV) and far (FUV) ultraviolet solar indices, and geomagnetic indices Ap, Kp, or Dst with model specific combination of lag-times, interpolation and smoothing applied. Upper atmospheric neutral density estimates with accuracies below the 15% barrier of traditional empirical models have been recently obtained through correction parameters determined from assimilated daily drag data. However, these data assimilation systems have been limited by the empirical model description of the upper atmospheric nonlinear dynamics and the scalar index description of the varied forcing. Empirical models cannot completely describe the complex chain of events that connect the heating and the complex atmospheric response, especially during long duration geomagnetic storms. Furthermore, the use of scalar geomagnetic indices to describe the greatly varying heating distributions is insufficient. Small inconsistencies in the heating location and magnitude can lead to vastly different conclusions since the upper atmosphere is strongly externally driven. Physical models are valuable tools in the task to understand and forecast complex non-linear systems. They have reached a level of maturity such that many of the physical processes controlling the neutral and ionized upper atmosphere's structure are included. In this work, results from a self-consistent physics-based coupled model of the thermosphere, ionosphere, plasmasphere and electrodynamics (CTIPe) are used along with Challenging Minisa-tellite Payload (CHAMP) and Gravity Recovery And Climate Experiment (GRACE) neutral density observations to show how the model captures the daily space weather and the year-long climatology not only in a qualitative but in a quantitative way. The assessment of CTIPe model capabilities in simulating the upper atmosphere's climatology, day-to-day-variability, and weather, as well as the identification of areas that need to be improved in the model, are a necessary step towards a deeper understanding of the internal and external physical processes driving neutral density variability, which can help improving the specification and prediction of drag forces on satellites.
Codrescu Mihail
Fedrizzi Michele
Fuller-Rowell Tim J.
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