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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