The atmospheric drag and the Radiation Pressure are the dominant forces acting on LEO satellites. Many different approaches have been followed for the modelling of these non-gravitational forces, based on the physics and the satellite characteristics, but in many cases large inconsistencies are present between the models and the accelerometer measurements. Atmospheric drag is considered as the most difficult force to model, and the Radiation Pressure models show large deviations from the measurements depending on the b′ angle and the position of the satellite near the entrance and the exit from the Earth’s shadow. Numerous models have been presented for GRACE satellites but none for GRACE-FO. The innovation of this study is the development of an atmospheric drag and a Radiation Pressure data-driven model based only on the accelerometer measurements of GRACE-C satellite, using least squares principles. The atmospheric drag is modelled using accelerometer measurements from the shadow segment of the orbit. An additional weighted constraint is that near the middle of the sun segment of the orbit, the drag in the x-direction should be equal to the actual measurements due to Radiation Pressure being nearly zero. Subsequently, we subtract the modelled drag from the real measurements in order to estimate the Radiation Pressure which, consequently, is modelled using a least squares frequency-domain analysis. The residual series proceeded from the subtraction of these two models from the actual measurements of GRACE-C accelerometer, are analyzed by taking into consideration the local time, the spatial information and the variations of b΄ angle, as well as their connection with electromagnetic changes in the upper atmosphere. The proposed models have been tested for different time periods in the last three years of GRACE C and the rms of the residual series along the x and the z axes of the accelerometer is ~2.5 nm/s2, while the y-axis exhibits an rms of ~1 nm/s^2.

GRACE-FO (GFO) and Swarm are two LEO missions that, among others, provide non-gravitational acceleration measurements required for geopotential model development and modelling of non-gravitational forces acting upon them. Unfortunately, the performance of the accelerometers on board for both missions is not the expected. Measurements from both missions present dominant bias jumps that occur on all accelerometer axes and they have been linked to the satellites’ entrance to and exit from the Earth’s shadow. These jumps are estimated and corrected at Level 1A of GFO C and at Level 2 of Swarm C in an optimal way using Least Squares methodology. The corresponding variances of the jumps are also calculated. Furthermore, the measurements contain spurious signals and dominant spikes mostly connected with thruster activation, mainly in the equatorial region or high temperature sensitivity. These disturbances have a significant impact on the data analysis. We propose an alternative weighting filter methodology to generate Level 1B data from Level 1A for GFO C that includes the attenuated spikes and their corresponding variances and does not involve the removal of the spikes nor does it include any interpolation to fill data gaps. This methodology is used for Swarm C accelerometer Level 2 dataset as well. Using spectral domain methods, we show that the newly generated GFO Level 1B and Swarm Level 2 data are not contaminated by the presence of spikes and data jumps. In the polar regions, mostly at the South pole, spikes in the measurements are connected to magnetic disturbances when the satellites enter these regions. Our proposed methodology contains an optimal and unbiased dataset of non-gravitational acceleration measurements that can be used for the estimation of geopotential models and also for the investigation of the accelerometer’s response to electromagnetic disturbances and the modelling of other non-gravitational accelerations to derive thermospheric neutral densities.