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    Improving the observation of time-variable gravity using GRACE RL04 data

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    Date
    2010-12
    Author
    Bonin, Jennifer Anne
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    Abstract
    The Gravity Recovery and Climate Experiment (GRACE) project has two primary goals: to determine the Earth’s mean gravitational field over the lifetime of the mission and to observe the time-variable nature of the gravitational field. The Center for Space Research's (CSR) Release 4 (RL04) GRACE solutions are currently created via a least-squares process that assimilates data collected over a month using a simple boxcar window and determines a spherical harmonic representation of the monthly gravitational field. The nature of this technique obscures the time-variable gravity field on time scales shorter than one month and spatial scales shorter than a few hundred kilometers. A computational algorithm is developed here that allows increased temporal resolution of the GRACE gravity information, thus allowing the Earth's time-variable gravity to be more clearly observed. The primary technique used is a sliding-window algorithm attached to a weighted version of batch least squares estimation. A number of different temporal windowing functions are evaluated. Their results are investigated via both spectral and spatial analyses, and globally as well as in localized regions. In addition to being compared to each other, the solutions are also compared to external models and data sets, as well as to other high-frequency GRACE solutions made outside CSR. The results demonstrate that a GRACE solution made from at least eight days of data will provide a well-conditioned solution. A series of solutions made with windows of at least that length is capable of observing the expected near-annual signal. The results also indicate that the signals at frequencies greater than 3 cycles/year are often smaller than the GRACE errors, making detection unreliable. Altering the windowing technique does not noticeably improve the resolution, since the spectra of the expected errors and the expected non-annual signals are very similar, leading any window to affect them in the same manner.
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    http://hdl.handle.net/2152/9991
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