Abstract:

Monthly solutions of the deviation of a time specific gravity field adjusted against a background mean field affords the opportunity to monitor global mass flux as an additional form of remote sensing. The GRACE Mission, to date, is unique in offering this capability. It is common in GRACE analyses to forward model the more transient mass flux signals arising from tides and atmospheric pressure variations, which are large, in order to eliminate them to reveal the very interesting hydrological and oceano-graphic mass changes. In practice, imperfect forward modeling and GRACE data errors (both noise and systematic errors) coupled with whatever geographic sampling is achieved monthly, are solution limitations. As a means to verify our GRACE data processing for local mass anomaly solutions, we have undertaken a new analysis approach to produce monthly spherical harmonic solutions using only the GRACE KBRR data as a composite of individual daily orbits. This is accomplished by using the GPS-data to establish a 2-3 cm-level orbital reference frame and by performing an unconstrained orbit adjustment using only those components of the orbit that are sensed by the KBRR data. The resulting gravity models, complete to degree and order 60, are completely unconstrained. This approach shows great promise. Whereas the monthly solutions produced to date show considerable noise in their power spectrum and significantly depart from the expected hydrological signal above degree 15, these KBRR solutions more closely resemble the expected geophysical signal to approximately degree 30, which is consistent with the level of resolution we are seeing for direct mass anomaly solutions. Of particular interest, problems with specific coefficients in the model which have been identified in past solutions, like those seen in C(2,0) are dramatically reduced. In this presentation we will show results for over 1 year of monthly spherical harmonic solutions and compare them with those released by the GRACE Project and those expected from hydrology.