Journal of Geophysical Research Space Physics · 2018 · 16 citations · 51 references
Natural SciencesEarth SciencesGeophysicsAtmospheric ScienceClimatologyGeomagnetismForecast VerificationSatellite Data ValidationSpace Weather EffectsEmpirical Thermosphere ModelsHigh-latitude Ionospheric ConvectionIonospheric Plasma DynamicsOrbit PredictionPlasma ConvectionSpace Weather ForecastingSpace Weather Prediction ModelsThermospheric Density Variations
Abstract The first systematic comparison between Swarm‐C accelerometer‐derived thermospheric density and both empirical and physics‐based model results using multiple model performance metrics is presented. This comparison is performed at the satellite's high temporal 10‐s resolution, which provides a meaningful evaluation of the models' fidelity for orbit prediction and other space weather forecasting applications. The comparison against the physical model is influenced by the specification of the lower atmospheric forcing, the high‐latitude ionospheric plasma convection, and solar activity. Some insights into the model response to thermosphere‐driving mechanisms are obtained through a machine learning exercise. The results of this analysis show that the short‐timescale variations observed by Swarm‐C during periods of high solar and geomagnetic activity were better captured by the physics‐based model than the empirical models. It is concluded that Swarm‐C data agree well with the climatologies inherent within the models and are, therefore, a useful data set for further model validation and scientific research.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Scikit-learn: Machine Learning in Python
PedregosaFabian, VaroquauxGaël, GramfortAlexandre et al. · Journal of Machine Learning Research · 2011 · 8.2K citations
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