Abstract
Abstract. For further improvements of gravity field models based on Gravity Recovery and Climate Experiment (GRACE) observations, it is necessary to identify the error sources within the recovery process. Observation residuals obtained during the gravity field recovery contain most of the measurement and modeling errors and thus can be considered a realization of actual errors. In this work, we investigate the ability of wavelets to help in identifying specific error sources in GRACE range-rate residuals. The multiresolution analysis (MRA) using discrete wavelet transform (DWT) is applied to decompose the residual signal into different scales with corresponding frequency bands. Temporal, spatial, and orbit-related features of each scale are then extracted for further investigations. The wavelet analysis has proven to be a practical tool to find the main error contributors. Besides the previously known sources such as K-band ranging (KBR) system noise and systematic attitude variations, this method clearly shows effects which the classic spectral analysis is hardly able or unable to represent. These effects include long-term signatures due to satellite eclipse crossings and dominant ocean tide errors.
Highlights
For more than 15 years, the Gravity Recovery and Climate Experiment (GRACE) satellite mission measured the time variation of Earth’s gravity field with high temporal and spatial resolutions (Tapley et al, 2004)
To prove whether or not our applied method using the discrete wavelet transform (DWT) is applicable to detect the error sources, we initially focused on the investigation of known issues
The simulated residual signal is decomposed, and its long timescale components are compared to those obtained from real data
Summary
For more than 15 years, the Gravity Recovery and Climate Experiment (GRACE) satellite mission measured the time variation of Earth’s gravity field with high temporal and spatial resolutions (Tapley et al, 2004). The mission was a trailing formation of two satellites, GRACE-A and GRACE-B, and provided the observation signals of intersatellite ranging, GPS tracking, the satellite attitudes, and nongravitational accelerations, which are required for the gravity field parameter estimation. Based on these observations, various time-variable gravity models with monthly resolution were published by different analysis centers (e.g., Bettadpur, 2012; Dahle et al, 2012; Mayer-Gürr et al, 2016). These parameters include gravity parameters in terms of spherical harmonic coefficients as well as orbit and sensor calibration parameters (Mayer-Gürr, 2013)
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