Publications related to the GRACE Missions (no abstracts)

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Empirical mode decomposition feature based Bi-LSTM and GRU neural network predictions of thermospheric density during rising and minimum solar activity from 2018 to 2022

Panpiboon, Patapong, Noysena, Kanthanakorn, and Yeeram, Thana, 2025. Empirical mode decomposition feature based Bi-LSTM and GRU neural network predictions of thermospheric density during rising and minimum solar activity from 2018 to 2022. Earth Science Informatics, 18(2):218, doi:10.1007/s12145-025-01698-z.

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@ARTICLE{2025EScIn..18..218P,
       author = {{Panpiboon}, Patapong and {Noysena}, Kanthanakorn and {Yeeram}, Thana},
        title = "{Empirical mode decomposition feature based Bi-LSTM and GRU neural network predictions of thermospheric density during rising and minimum solar activity from 2018 to 2022}",
      journal = {Earth Science Informatics},
     keywords = {Thermospheric density, Atmospheric drag, Solar flux, Recurrent neural network, Solar activity},
         year = 2025,
        month = feb,
       volume = {18},
       number = {2},
          eid = {218},
        pages = {218},
     abstract = "{Low Earth orbit satellites are potentially impacted by atmospheric drag
        due to short-term enhancements in thermospheric density induced
        by solar irradiance and solar wind disturbances, affecting the
        design of launch missions to the safe landing of spacecraft on
        Earth. We utilize hourly solar and geomagnetic indices and
        thermospheric density as measured by Gravity Recovery and
        Climate Experiment Follow-On (GRACE-FO) satellites during the
        minimum and rising phases of solar activity from 2018 to 2022.
        Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated
        Recurrent Unit (GRU) neural networks based on empirical mode
        decomposition (EMD) features are used to predict the
        thermospheric density. We found that the EMD of thermospheric
        density provided robust feature for the GRU and the Bi-LSTM
        models. The predictions are more effective in the rising phase
        when the thermospheric density is typically high which is of
        interested in satellite drags. The inputs of thermospheric
        density and its intrinsic mode functions (IMFs) with solar and
        geomagnetic indices improved prediction abilities for the rising
        phase, while only the IMFs of density or the geomagnetic indices
        is sufficient for the minimum phase. For categories based on
        disturbed and quiet geomagnetic conditions, the best prediction
        is for the coronal mass ejection (CME) event. The maximum values
        of R$^{2}$ is in the stream interaction region-high speed solar
        wind event for both Bi-LSTM and GRU models with correlation
        coefficients 0.914 and 0.922, respectively. The Bi-LSTM is more
        suitable for predicting the thermospheric density during
        ``SpaceX storm'' of consecutive CME-CME geomagnetic storms,
        while the temporal-dependent variations in the density are
        accurately predicted by the GRU model. Predictions by both deep
        learning models are more accurate than by the NRLMSIS 2.0 model.
        This study reveals the main physical mechanisms driving the
        short-term variations in the thermospheric density.}",
          doi = {10.1007/s12145-025-01698-z},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025EScIn..18..218P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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