A Machine‐Learning Approach to Derive Long‐Term Trends of Thermospheric Density

In this study, we revisit the long‐term trend of thermospheric density by using a Machine‐Learning approach. Our Artificial Neural Network Model (ANNM) can better capture the variations in the satellite drag‐derived densities than earlier empirical models, especially during extremely solar minimum period of 2007–2009. The long‐term trends from ANNM are similar when density data during either 1967–2005 or 1967–2013 intervals are used in the calculation. In addition, our trend estimates relative to the ANNM are −1.5% to −2.0% per decade from 250 to 575 km without obvious solar activity dependence. The Machine‐Learning approach provides a good way to give stable long‐term trend estimates of thermospheric density.

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