The Gravity Recovery and Climate Experiment (GRACE) Mission is designed to monitor local, regional, and global changes in the Earth’s gravity field. The changes observed in the gravity field are a manifestation of mass being transported in the Earth’s oceans, atmosphere, and on land surfaces. Analysis of the data delivered by GRACE yields a direct measure of mass flux with high spatial resolution on the Earth's surface. This is accomplished at one month intervals through the estimation of monthly gravity fields [Wahr et al., 2004; Tapley et al., 2004a]. Although this approach has been largely successful, information at submonthly time scales are lost or even aliased through the estimation of static monthly harmonic field parameters. At NASA Goddard Space Flight Center we have utilized an alternative approach that recovers submonthly mass flux at a high spatial resolution over regions of interest. Our mass concentration (mascon) gravity representation largely mitigates the spatial and temporal aliasing problems encountered with monthly GRACE solutions using Stokes coefficients. Through support of NASA’s ACCESS Program, we have developed this portal to provide our continental mascon solutions to the wider science community.

On this site we are providing discrete 10-day estimates of the mass change with respect to a multi-year averaged gravity model in a 4x4 degree equal angle grid over all of the Earth’s continents [Figure 2]. To estimate these values, we have forward modeled highly accurate models of atmospheric pressure changes and solid Earth and ocean tides. This is done to eliminate these sources of flux in order to isolate the hydrological signal over these regions (see Methodology for more details). Shown below in Figure 3 is a one month time history of these mascon solutions over the Indian subcontinent where the values shown are the mass change mapped into an equivalent change in a surface layer of water in units of cm. To accompany the 10-day, 4° gridded mass anomaly fields derived from GRACE, numerically modeled soil moisture and snow mass fields from the Global Land Data Assimilation System (GLDAS; Rodell et al., 2004a) are provided with identical spatial and temporal characteristics. The Global Land Data Assimilation System (GLDAS) has ingested satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface’s hydrological state and its fluxes (Rodell et al., 2004a). Figure 3 shows a comparison of GRACE mascons and the corresponding mass flux estimates from GLDAS.