Space gravity measurements have been mainly used to study the temporal mass variations at the Earth's surface and within the mantle. Nevertheless, mass variations due to the Earth's core might be observable in the gravity field variations as measured by Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On satellites. Earth's core dynamical processes inferred from geomagnetic field measurements are characterized by large‐scale patterns associated with low spherical harmonic degrees of the potential fields. To study these processes, the use of large spatial and inter‐annual temporal filters is needed. To access gravity variations related to the Earth's core, surface effects must be corrected, including hydrological, oceanic or atmospheric loading (Newtonian attraction and mass redistribution). However, these corrections for surface processes add errors to the estimates of the residual gravity field variations enclosing deep Earth's signals. As our goal is to evaluate the possibility to detect signals of core origin embedded in the residual gravity field variations, a quantification of the uncertainty associated with gravity field products and geophysical models used to minimize the surface process signatures is necessary. Here, we estimate the dispersion for GRACE solutions as about 0.34 cm of equivalent water height (EWH) or 20% of the total signal. Uncertainty for hydrological models is as large as 0.89–2.10 cm of EWH. We provide estimates of Earth's core signals whose amplitudes are compared with GRACE gravity field residuals and uncertainties. The results presented here underline how challenging is to get new information about the dynamics of the Earth's core via high‐resolution, high‐accuracy gravity data.
Space gravity measurements have been mainly used to study the temporal mass variations at the Earth's surface and within the mantle. Nevertheless, mass variations due to the Earth's core might be observable in the variations of the gravity field as measured by GRACE and GRACE-FO satellites. Moreover, a possible correlation between the time-variable gravity and magnetic fields has been pointed out at inter-annual time scales. Earth's core dynamical processes inferred from geomagnetic field measurements are characterized by large-scale patterns associated with low spherical harmonic degrees of the potential fields. Studying Earth's core processes via gravity field observations involves the use of large spatial and inter-annual temporal filters. To access gravity variations related to the Earth's core, surface effects must be corrected, including hydrological, oceanic or atmospheric loading. This study estimates the uncertainty associated with gravity-field products and geophysical models used to minimise the surface process signatures in gravity field data. Here, we estimate the dispersion for GRACE solutions as about 0.34 cm of Equivalent Water Height (EWH) or 20% of the total signal. Uncertainty for hydrological models is as large as 0.89 to 2.10 cm of EWH. Loading products contain mostly different signals at inter-annuals time scales. We also show that a remaining hydrological signal in a very localized region can affect the low-degree components of the gravity field. The results presented here underline how challenging is to get new information about the dynamics of the Earth's core via high-accuracy gravity data.
Space gravity measurements have been mainly used to study the temporal mass variations at the Earth’s surface and within the mantle. Nevertheless, mass variations due to the Earth’s core might be observable in the gravity field variations as measured by GRACE(-FO) satellites. Earth’s core dynamical processes inferred from geomagnetic field measurements are characterized by large-scale patterns associated with low spherical harmonic degrees of the potential fields. To study these processes, the use of large spatial and inter-annual temporal filters is needed. To access gravity variations related to the Earth’s core, surface effects must be corrected, including hydrological, oceanic or atmospheric loading (Newtonian attraction and mass redistribution). However, these corrections for surface processes add errors to the estimates of the residual gravity field variations enclosing deep Earth’s signals. As our goal is to evaluate the possibility to detect signals of core origin embedded in the residual gravity field variations, a quantification of the uncertainty associated with gravity field products and geophysical models used to minimise the surface process signatures is necessary. Here, we estimate the dispersion for GRACE solutions as about 0.34 cm of Equivalent Water Height (EWH) or 20% of the total signal. Uncertainty for hydrological models is as large as 0.89 to 2.10 cm of EWH. We provide estimates of Earth’s core signals whose amplitudes are compared with GRACE gravity field residuals and uncertainties. The results presented here underline how challenging is to get new information about the dynamics of the Earth’s core via high-resolution, high-accuracy gravity data.
<p class="western">The GRACE and GRACE Follow-On missions are separated by an 11-month gap between 2017 and 2018 and contain 22 more missing months. These gaps in the time series lead to a difficult recovery of gravity variation signals with pluri-annual temporal scales. In this context, various studies proposed machine learning approaches and decomposition techniques to predict the missing values.</p> <p class="western">&#160;</p> <p class="western">This study summarizes the different approaches that we have implemented and compares their results. We consider both grid and spherical harmonics at global scales. Some gap-filling solutions use an extrapolation of the GRACE products and some others propose to use Swarm gravity field products to reduce the missing data. We tested several methods in terms of their capacity to predict signals on monthly or annual periods, randomly chosen between 2005 and 2010. The Root-Mean Square Error between the predictions and the original solution gives an estimation of the uncertainty associated with each method.</p> <p class="western">&#160;</p> <p class="western">We show that simple methods like &#171;&#160;Constant, Trend, Annual and Semi-annual fit&#160;&#187; do not deliver the complexity of the original signal. We finally conclude that the Singular Spectrum Analysis (SSA) and Multivariate SSA produce the best results at large spatial scales.</p>
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