Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation

Ultra-sensitive space-borne accelerometers on board of low Earth orbit (LEO) satellites are used to measure non-gravitational forces acting on the surface of these satellites. These forces consist of the Earth radiation pressure, the solar radiation pressure and the atmospheric drag, where the first two are caused by the radiation emitted from the Earth and the Sun, respectively, and the latter is related to the thermospheric density. On-board accelerometer measurements contain systematic errors, which need to be mitigated by applying a calibration before their use in gravity recovery or thermospheric neutral density estimations. Therefore, we improve, apply and compare three calibration procedures: (1) a multi-step numerical estimation approach, which is based on the numerical differentiation of the kinematic orbits of LEO satellites; (2) a calibration of accelerometer observations within the dynamic precise orbit determination procedure and (3) a comparison of observed to modeled forces acting on the surface of LEO satellites. Here, accelerometer measurements obtained by the Gravity Recovery And Climate Experiment (GRACE) are used. Time series of bias and scale factor derived from the three calibration procedures are found to be different in timescales of a few days to months. Results are more similar (statistically significant) when considering longer timescales, from which the results of approach (1) and (2) show better agreement to those of approach (3) during medium and high solar activity. Calibrated accelerometer observations are then applied to estimate thermospheric neutral densities. Differences between accelerometer-based density estimations and those from empirical neutral density models, e.g., NRLMSISE-00, are observed to be significant during quiet periods, on average 22 % of the simulated densities (during low solar activity), and up to 28 % during high solar activity. Therefore, daily corrections are estimated for neutral densities derived from NRLMSISE-00. Our results indicate that these corrections improve model-based density simulations in order to provide density estimates at locations outside the vicinity of the GRACE satellites, in particular during the period of high solar/magnetic activity, e.g., during the St. Patrick's Day storm on 17 March 2015

Estimating and predicting corrections for empirical thermospheric 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