Publications related to the GRACE Missions (no abstracts)

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An Empirical Atmospheric Density Calibration Model Based on Long Short–Term Memory Neural Network

Zhang, Yan, Yu, Jinjiang, Chen, Junyu, and Sang, Jizhang, 2021. An Empirical Atmospheric Density Calibration Model Based on Long Short–Term Memory Neural Network. Atmosphere, 12(7):925, doi:10.3390/atmos12070925.

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@ARTICLE{2021Atmos..12..925Z,
       author = {{Zhang}, Yan and {Yu}, Jinjiang and {Chen}, Junyu and {Sang}, Jizhang},
        title = "{An Empirical Atmospheric Density Calibration Model Based on Long Short-Term Memory Neural Network}",
      journal = {Atmosphere},
     keywords = {atmospheric density, calibration model, LSTM, empirical density model},
         year = 2021,
        month = jul,
       volume = {12},
       number = {7},
          eid = {925},
        pages = {925},
     abstract = "{The accuracy of the atmospheric mass density is one of the most
        important factors affecting the orbital precision of spacecraft
        at low Earth orbits (LEO). Although there are a number of
        empirical density models available to use in the orbit
        determination and prediction of LEO spacecraft, all of them
        suffer from errors of various degrees. A practical way to reduce
        the error of a particular model is to calibrate the model using
        precise density data or tracking data. In this paper, a long
        short-term memory (LSTM) neural network is proposed to calibrate
        the NRLMSISE-00 density model, in which the densities derived
        from spaceborne accelerometer data are the main input. The
        resulted LSTM-NRL model, calibrated using the accelerometer data
        from Challenging Minisatellite Payload (CHAMP) satellite, is
        extensively experimented to evaluate the calibration
        performance. With data in one month to train the LSTM-NRL model,
        the model is shown to effectively reduce the root mean square
        error of the model densities outside the training window by more
        than 40\% in various time spans and space weather environment.
        The LSTM-NRL model is also shown to have remarkable transferring
        performance when it is applied along the GRACE satellite orbits.}",
          doi = {10.3390/atmos12070925},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021Atmos..12..925Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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