Estimating and predicting empirical corrections for empirical thermospheric density models

Quantifying spatial and temporal changes in thermospheric neutral density is important for various applications such as precise orbit determination, estimating mission lifetime and re-entry prediction of Earth orbiting objects. It is also crucial for analysis of possible collisions between active satellite missions and space debris. Empirical models are frequently applied to estimate neutral densities at the position of satellites. But their accuracy is severely constrained by model simplifications and the sampling limitation of solar and geomagnetic indices used as inputs. In this study, we first estimate thermospheric neutral density by processing the high-accuracy accelerometer measurements on-board of the twin-satellite mission Gravity Recovery And Climate Experiment (GRACE). Daily density corrections (in terms of scales) are then computed for the commonly used NRLMSISE-00 empirical model. The importance of these daily scales is examined within an orbit determination practice. Finally, three data-driven prediction techniques based on Artificial Neural Network (ANN) are applied to forecast the daily density corrections for few days to months. Our numerical results indicate that GRACE derived scales are correlated with solar and geomagnetic indices and can improve the timing (from few hours to days) and magnitude of model simulations (up to 10–100 times) during high solar or geomagnetic activity when they usually perform poorly. We found that the Non-linear Autoregressive with Exogenous (External) Input (NARX) ANN technique performs well in predicting the corrections with an average fit of 0.8 or more in terms of squared correlation coefficients for time-scales of 7–90 days.