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Thermospheric mass density: A review

Advances in Space Research, 2015
Abstract The mass density of Earth’s thermosphere (∼90–600 km altitude) is a critical parameter for low Earth orbit prediction because of the atmospheric drag on satellites in this region. In this review, we first survey techniques for measuring thermospheric density, empirical models that provide a synthesis of historical data, and physical models ...
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Density and composition of the lower thermosphere

Journal of Geophysical Research, 1973
This paper presents a new median model of the lower thermosphere. It is based on a critical evaluation of all the measurements of density and composition in this region and carries forward the process of re-examination begun by von Zahn in this 1970 paper.
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Semi-annual variation of the thermospheric density

Advances in Space Research, 1987
Abstract Thermospheric densities were determined from the orbital data of four satellites covering a height range of 266 to 500 km during 1975 to 1982. Using three different thermospheric models (DTM, MSIS, C), the index characterizing the semi-annual density variation was computed and analysed with respect to height and time using a regression by ...
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A Blending Technique in Thermospheric Density Modeling

AIAA/AAS Astrodynamics Specialist Conference and Exhibit, 2008
Uncertainty in the atmospheric density is a crucial error source for the propagation of satellites in low earth orbit (LEO). As a result, establishing accurate thermospheric neutral density models are important to predict the motion of these satellites.
Jung Soo Kim   +3 more
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Thermosphere Density Model Calibration

2007
Thermospheric density models are a main source of error in the orbit determination and prediction of low Earth satellites. The empirical models that are in wide use today show large systematic errors when compared with data derived from accelerometers and spacecraft tracking. This accuracy limit is inherent in their model formulation, which is based on
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Thermospheric density cells at high latitudes

Advances in Space Research, 1999
Abstract NCAR-TIGCM simulations predict mesoscale cellular structures in the high latitude neutral density which could serve as a unifying framework for the analysis and interpretation of measurements in this region (e.g. Crowley et al., 1996; Schoendorf et al., 1996a). Here we compare these model predictions with ESRO 4 and DE 2 satellite data.
T. Caspers, G.W. Prölss
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The JB2006 empirical thermospheric density model

Journal of Atmospheric and Solar-Terrestrial Physics, 2008
Abstract A new empirical atmospheric density model is developed using the CIRA72 (Jacchia 71) model as the basis for the diffusion equations. New solar indices based on orbit-based sensor data are used for the solar irradiances in the extreme and far ultraviolet wavelengths.
Bruce R. Bowman   +3 more
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A North–South asymmetry in thermospheric density

Advances in Space Research, 2006
Abstract Utilizing the opportunity that our previously analyzed drag material covers the whole latitude interval between 84°N and 84°S, we have reanalyzed our 16 thousand observed total density data comparing them with model densities as a function of different parameters.
E. Illés-Almár, I. Almár
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Machine Learning Algorithms for Improved Thermospheric Density Modeling

2020
Accurate estimation and prediction of the thermospheric density is crucial for accurate low Earth orbit prediction. Recently, Reduced-Order Models (ROMs) were developed to obtain accurate quasi-physical dynamic models for the thermospheric density. In this paper we explore the use of deep neural networks and autoencoders to improve the reduced-order ...
Herbert Turner   +3 more
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Magnetic storm response of lower thermosphere density

Journal of Geophysical Research: Space Physics, 1996
Measurements of atmospheric density near 200 km from the Satellite Electrostatic Triaxial Accelerometer (SETA) experiment are used to delineate the temporal, seasonal‐latitudinal, and day/night dependences of the response to magnetic storm‐related perturbations in high‐latitude energetic inputs.
J. M. Forbes   +4 more
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