Estimating Neutral Densities from Energy Sources Using Multiple Linear Regression

Details Estimating Neutral Densities from Energy Sources Using Multiple Linear Regression Estimating Neutral Densities from Energy Sources Using Multiple Linear Regression

Dec 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004agufmsa23a0390c&link_type=abstract

American Geophysical Union, Fall Meeting 2004, abstract #SA23A-0390

Physics

2427 Ionosphere/Atmosphere Interactions (0335), 2447 Modeling And Forecasting, 0358 Thermosphere: Energy Deposition

Scientific paper

Space operations involving satellite tracking requires estimates of the atmosphere's neutral density in order to determine the drag on the satellites. The neutral density models use F10.7 as a proxy for solar EUV input and the ap index as a proxy for the geomagnetic input. These models are typically semi-empirical, models which model the atmosphere as a hydrostatic equilibrium. Some models like the Air Force's High Accuracy Satellite Drag Model (HASDM) try to estimate and predict a dynamically varying high-resolution density field. We present a simple system for predicting neutral density along a satellite track based on solar, Joule, and particle heating. In an earlier study, we used a portion of the CHAMP satellite data set to develop parameters for estimating the neutral density at 450 km, and then used those parameters to estimate the neutral density during a different portion of the CHAMP data set. We developed a skill score to determine how well we were able to predict the neutral density along the CHAMP trajectory. We extend this work to a set of 18 satellites where we know their daily average densities to about 2-3% uncertainty.

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