A Machine‐Learning Approach to Derive Long‐Term Trends of Thermospheric Density
Libin Weng, Jiuhou Lei, Jiahao Zhong, Xiankang Dou, Hanxian Fang
Abstract
Key Points The Artificial Neural Network Model (ANNM) has good capability of characterizing the variability of the satellite drag‐derived densities The density data during either 1967–2005 or 1967–2013 intervals are used to give a similar long‐term trend from the ANNM The thermospheric density trend relative to the ANNM from 250 to 575 km is about −1.5% to −2.0% per decade without obvious solar flux dependence
Topics & Concepts
Term (time)SatelliteEnvironmental scienceArtificial neural networkDragFlux (metallurgy)MeteorologyThermosphereAtmospheric sciencesStatistical physicsPhysicsComputer scienceGeophysicsIonosphereMachine learningMaterials scienceAstronomyMetallurgyThermodynamicsIonosphere and magnetosphere dynamicsSolar and Space Plasma DynamicsAtmospheric Ozone and Climate