Open Access
Space Weather Modeling Capabilities Assessment: Neutral Density for Orbit Determination at low Earth orbit
Author(s) -
Bruinsma S.,
Sutton E.,
Solomon S. C.,
FullerRowell T.,
Fedrizzi M.
Publication year - 2018
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2018sw002027
Subject(s) - orbit determination , space weather , geocentric orbit , orbit (dynamics) , meteorology , thermosphere , geomagnetic storm , empirical modelling , storm , computer science , numerical weather prediction , earth's magnetic field , environmental science , remote sensing , aerospace engineering , satellite , geography , simulation , geology , physics , geophysics , ionosphere , engineering , quantum mechanics , magnetic field
Abstract The specification and prediction of density changes in the thermosphere is a key challenge for space weather observations and modeling, because it is one result of complex interactions between the Sun and the terrestrial atmosphere and also because it is of operational importance for tracking objects orbiting in near‐Earth space. For low Earth orbit, neutral density variation is the most important uncertainty for propagation and prediction of orbital elements. A recent international conference conducted under the auspices of the National Aeronautics and Space Administration Community Coordinated Modeling Center included a workshop on neutral density modeling, using both empirical and numerical methods, and resulted in organization of an initial effort in model comparison and evaluation. Here we report on the exploitable density data sets available, the selected years and storm events, and the metrics for complete model assessment. Comparisons between five models (three empirical and two numerical) and neutral density data sets that include measurements by the CHAMP, GRACE, and GOCE satellites are presented as examples of the assessment procedure that will be implemented at Community Coordinated Modeling Center. The models in general performed reasonably well, although seasonal errors sometimes are present, and impulsive geomagnetic storm events remain challenging. Numerical models are still catching up to empirical methods on a statistical basis, but hold great potential for describing these short‐term variations.