Exploring thermospheric disturbance patterns: A weighted accelerometer 1B dataset of GRACE C

Satellite measurements are essential for studying Earth’s environment, but they often lack reliable error estimates, making it harder to interpret the data reliably. In this study, we present a new way to calculate realistic covariance matrices directly from raw satellite data, focusing on the accelerometer measurements from the GRACE C satellite from August 2018 to August 2022, covering the solar minimum and the ascending phase of Solar Cycle 25. The ACC1A dataset (10 Hz sampling rate) is used as the primary data source because it provides the raw measurements suitable for generating experimental covariance matrices directly from the original observations. Using autocorrelation analysis of the ACC1A measurements, we build a block-diagonal covariance matrix and integrate it into the data processing through a low-pass Gaussian filter. This method improves the accuracy of the proposed weighted ACW1B dataset by reducing noise, such as sudden spikes due to thruster activations while preserving important signals.

Our results show that the accelerometer variances depend on the satellite’s position and time in orbit. We observe clear fluctuations during geomagnetic storms, especially near the equinoxes, and during crossings through Earth’s shadow and terminator. These variances are highest in the radial, and smallest in the cross-track direction, due to the accelerometer’s lower sensitivity in the latter. The new ACW1B dataset also shows a strong link between measurement variances and orbital factors like latitude, local time, and β' angle, making it more suitable for studying satellite-environment interactions.