Improvements in the Monthly Gravity Field Solutions Through Modeling the Colored Noise in the GRACE Data

Abstract

The Gravity Recovery And Climate Experiment (GRACE) mission has achieved a quantum leap in knowledge of the Earth's gravity field. However, current gravity field solutions still cannot reach the prelaunch baseline accuracy. One of the reasons for that is the presence of colored noise in GRACE data, which is typically ignored in the classical dynamic approach to gravity field modeling. In this research, we propose to account for colored noise in the classical dynamic approach by applying the frequency‐dependent data weighting (FDDW) scheme, so that enhanced estimates of gravity field solutions are produced. The monthly solutions are compared with those produced using the standard least squares adjustment without a data weighting scheme. The comparison is performed in both spectral and spatial domains, showing the positive effect of the FDDW scheme in all considered cases. For instance, the cumulative geoid height errors up to degree 96 are reduced by 18%. In the spatial domain, the FDDW scheme lowers noise level in mass changes over the oceans, Mississippi river basin, and Greenland by 20, 38, and 23%, respectively, when compared to the without a data weighting scheme. In addition, the consistency of mass changes over the Mississippi and Congo river basins with those inferred from the state‐of‐the‐art hydrology model WaterGAP is substantially improved when the FDDW scheme is applied. These results indicate that modeling colored noise in the GRACE data allows to significantly improve the recovered monthly solutions. This finding is likely applicable also to the GRACE Follow‐On mission.

A New Approach to Earth’s Gravity Field Modeling Using GPS-Derived Kinematic Orbits and Baselines

“…Data processing in this study is performed with the Position And Navigation Data Analyst (PANDA) software, which is developed at the GNSS Research Center of Wuhan University and has been widely used in precise orbit determination for both GNSS satellites and low Earth orbiters [18,19]. Recently, the dynamic approach to gravity field modeling has been implemented in PANDA and successfully applied to produce GRACE monthly gravity field solutions [16,17,20,21]. In this approach, data processing consists of two steps when the gravity field is estimated from kinematic orbits.…”

“…In this study, we adopt the frequency-dependent data weighting (FDDW) concept proposed by Reference [36] to account for the colored noise during the solution process. Recently, the FDDW concept has successfully been applied to GRACE KBR data processing with the classical dynamic approach and has notably reduced noise in the WHU RL01 monthly gravity solutions [17]. To represent the dependence of noise on frequency, we consider noise power spectral density (PSD), which is estimated from postfit observation residuals.…”

“…Then two sets of monthly gravity field solutions are produced: One is based on kinematic orbits of the twin satellites with the orbit approach (hereafter denoted as the 'orbit solution' where applicable), the other is based on kinematic orbits of GRACE A and kinematic baseline vectors between GRACE A and B with the baseline approach (hereafter denoted as the 'baseline solution' where applicable). To evaluate the quality of the GPS-based solutions, we use as the reference the WHU RL01 monthly gravity field solutions produced at the GNSS Research Center of Wuhan University [16,17]. Those solutions are mainly based on the GRACE KBR measurements and therefore are much more accurate than any GPS-based solutions.…”

An Optimized Short‐Arc Approach: Methodology and Application to Develop Refined Time Series of Tongji‐Grace2018 GRACE Monthly Solutions

“…This is one of the possible reasons for the frequency‐dependent amplitude of the orbit residuals as displayed in Figure a, indicating modeling frequency‐dependent noise for orbits is theoretically necessary. In this paper, the variance‐covariance matrices for orbit and range rate data are constructed rather than doing so only in the range rates (Guo et al, ).…”

“…To account for the effects of the frequency‐dependent noise in the observations on gravity field modeling, empirical parameters are generally introduced (Liu et al, ; Zhao et al, ; Zhou et al, ). The frequency‐dependent noise can also be suppressed by frequency‐dependent data weighting (FDDW) techniques (Farahani et al, ; Guo et al, ; Klees & Ditmar, ). However, most processing centers do not consider the FDDW and less often discuss the noise behaviors of orbit measurements.…”