Open AccessNeural networks to predict exosphere temperature corrections
Author(s) -
Choury Anna,
Bruinsma Sean,
Schaeffer Philippe
Publication year - 2013
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1002/2013sw000969
Subject(s) - exosphere , thermosphere , artificial neural network , satellite , computer science , orbit (dynamics) , drag , meteorology , artificial intelligence , physics , ionosphere , aerospace engineering , geophysics , mechanics , engineering , ion , quantum mechanics
Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low‐orbiting satellite Gravity Field and Steady‐State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.