Publications related to the GRACE Missions (no abstracts)

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Can a Persistence–Based Prediction of GRACE Gravity Field Coefficients Reduce Uncertainties at Finer Temporal Scales?

Kankanige, D., Vishwakarma, B. D., Liu, Y. Y., and Sharma, A., 2026. Can a Persistence–Based Prediction of GRACE Gravity Field Coefficients Reduce Uncertainties at Finer Temporal Scales?. Earth and Space Science, 13(8):e2025EA004554, doi:10.1029/2025EA004554.

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@ARTICLE{2026ESS...1304554K,
       author = {{Kankanige}, D. and {Vishwakarma}, B.~D. and {Liu}, Y.~Y. and {Sharma}, A.},
        title = "{Can a Persistence-Based Prediction of GRACE Gravity Field Coefficients Reduce Uncertainties at Finer Temporal Scales?}",
      journal = {Earth and Space Science},
     keywords = {temporal persistence, gravity coefficients, uncertainty, finer temporal resolution},
         year = 2026,
        month = aug,
       volume = {13},
       number = {8},
          eid = {e2025EA004554},
        pages = {e2025EA004554},
     abstract = "{Enhancing the applicability of Gravity Recovery and Climate Experiment
        (GRACE) products poses challenges due to the trade--off between
        the temporal and the spatial resolution within limits posed by
        the mission design. If a Kalman filter framework was adopted to
        characterize the relationship between monthly and finer temporal
        scales, it could facilitate the prediction of uncertainty
        associated with rapid mass changes at the higher temporal
        resolution. This study investigates the following key questions:
        (a) whether a persistence--based prediction of gravity
        coefficients effectively reduces the uncertainties of finer
        temporal scale--gravity coefficients to levels comparable with
        the known uncertainties of monthly gravity coefficients; and (b)
        the spectral degrees at which this consistency is most
        pronounced. The application of the resulting predictive approach
        to GRACE level--2 data demonstrates its effectiveness in
        reducing uncertainty at high spherical harmonic degrees. The
        prediction approach allows for the estimation of sub--monthly
        and weekly gravity coefficients with uncertainties that are
        comparable to the monthly resolution--uncertainties for specific
        ranges of degrees. This is validated by the degree variance of
        the finer resolution--estimates which largely align with those
        of the monthly raw gravity coefficients across the higher
        spherical harmonic degrees for sub--monthly and weekly data.
        Further, this predictive framework, on a univariate basis,
        demonstrates its potential to mitigate the correlated errors to
        a useful extent as observed in the spectral and spatial domains.
        Reducing uncertainties at finer temporal scales for higher
        spherical harmonic degrees to levels comparable with monthly
        data enhances the assessment of rapid mass changes in localized
        hydrological studies.}",
          doi = {10.1029/2025EA004554},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026E&SS...1304554K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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