Introduction -1Coordinate Frames -3 Inertial Frame -3Earth-Centered-Earth-Fixed Frame -6 Navigation Frame -7Transformations -10
S U M M A R YMonthly mass variations within the Earth system produce temporal gravity changes, which are observable by the NASA/GFZ Gravity Recovery and Climate Experiment (GRACE) twinsatellite system. Mass load changes with spatial scales larger than 1000 km have been observed using conventional filters based on a Gaussian smoother, which applies a weight to GRACE spherical harmonic (SH) coefficients depending only on SH degree. This practice is consistent with a degree-dependent error model for GRACE monthly geopotential solutions. The Gaussian filters effectively dampen all power of ill-determined higher-degree components in the estimates. However, the spatial sampling provided by GRACE yields errors that vary with both SH degree and order. The consequence is that maps of spatial loads shall not be smoothed with an isotropic (degree-only) filter, but shall be constructed using anisotropic smoothing thus also yielding better spatial resolution in latitude. We have developed a non-isotropic filter to optimize the smoothing of GRACE temporal gravity observations by considering the degreeand order-dependent quality of GRACE estimates, the latter analysed from the correlation with the predicted signals of hydrologic and ocean models. In order to retain GRACE coefficients in the filtering process that show reasonable correlation with the geophysical (hydrology and ocean) models, we applied Gaussian-type smoothing but with averaging radius depending on the order of the geopotential coefficient estimates. Applied to 2 yr of GRACE data, we showed that the resulting non-isotropic filter yields enhanced GRACE signals with significantly higher resolution in latitude and the same resolution in longitude without reducing the accuracy as compared to the conventional Gaussian smoother.Climate-related mass redistribution on the Earth has been observed in the time-varying gravity components derived from the NASA/GFZ Gravity Recovery and Climate Experiment (GRACE) satellite mission at monthly temporal scales and spatial scales of 1000 km or greater (Tapley et al. 2004a;Wahr et al. 2004). The GRACE twin co-orbiting satellites were launched in 2002 and are in near-circular orbits of 89 • mean inclination and 500 km mean altitude. Monthly time-series of geopotential spherical harmonic (SH) coefficients constitute the GRACE Level-2 (L2) science data product (Tapley et al. 2004b). To obtain reasonable estimates of time-varying gravity signals and related surface load changes due to air and water, L2 coefficients should be filtered because high spatial-frequency (high SH degree) components are poorly determined. The current approach uses degree-dependent filters such as Gaussian filters (Tapley et al. 2004a;Wahr et al. 2004). These produce spatially isotropic smoothing of surface load maps as a function of time. The spatial radius of the smoothing filter is empirically selected by considering the magnitude of the a priori temporal gravity signal (e.g. from hydrological or ocean circulation models) and the GRACE error power spectrum. H...
[1] The Gravity Recovery and Climate Experiment (GRACE) satellite mission will provide new measurements of Earth's static and time-variable gravity fields with monthly resolution. The temporal effects due to ocean tides and atmospheric mass redistribution are assumed known and could be removed using current models. In this study we quantify the aliasing effects on monthly mean GRACE gravity estimates due to errors in models for ocean tides and atmosphere and due to ground surface water mass variation. Our results are based on simulations of GRACE recovery of monthly gravity solution complete to degree and order 120 in the presence of the respective model errors and temporal aliasing effects. For ocean tides we find that a model error in S 2 causes errors 3 times larger than the measurement noise at n < 15 in the monthly gravity solution. Errors in K 1 , O 1 , and M 2 can be reduced to below the measurement noise level by monthly averaging. For the atmosphere, model errors alias the solution at the measurement noise level. The errors corrupt recovered coefficients and introduce 30% more error in the global monthly geoid estimates up to maximum degree 120. Assuming daily CDAS-1 data for continental surface water mass redistribution, the analysis indicates that the daily soil moisture and snow depth variations with respect to their monthly mean produce a systematic error as large as the measurement noise over the continental regions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.