Publications related to the GRACE Missions (no abstracts)

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Four–Dimensional Topside Electron Density Modeling Using Multi–Stage Deep Learning Approaches

He, Changyong, Hu, Andong, Cai, Han, Xiong, Zhaohui, and Zheng, Dunyong, 2026. Four–Dimensional Topside Electron Density Modeling Using Multi–Stage Deep Learning Approaches. Remote Sensing, 18(12):2002, doi:10.3390/rs18122002.

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@ARTICLE{2026RemS...18.2002H,
       author = {{He}, Changyong and {Hu}, Andong and {Cai}, Han and {Xiong}, Zhaohui and {Zheng}, Dunyong},
        title = "{Four-Dimensional Topside Electron Density Modeling Using Multi-Stage Deep Learning Approaches}",
      journal = {Remote Sensing},
     keywords = {topside ionosphere, electron density, artificial neural network (ANN), COSMIC, incoherent scatter radar (ISR)},
         year = 2026,
        month = jun,
       volume = {18},
       number = {12},
          eid = {2002},
        pages = {2002},
     abstract = "{What are the main findings? The new L2-ANN model outperforms IRI-2016 by
        35--53\% across three independent datasets (COSMIC-1, GRACE, and
        ISR). The model accurately reproduces key ionospheric features,
        including the EIA and MSNA, under different solar conditions.
        The new L2-ANN model outperforms IRI-2016 by 35--53\% across
        three independent datasets (COSMIC-1, GRACE, and ISR). The model
        accurately reproduces key ionospheric features, including the
        EIA and MSNA, under different solar conditions. What are the
        implications of the main findings? The data-driven framework
        provides a more accurate alternative to empirical models for
        GNSS positioning and upper-atmosphere research. The model
        remains robust even when direct NmF2/hmF2 measurements are
        unavailable, improving applicability in data-sparse regions. The
        data-driven framework provides a more accurate alternative to
        empirical models for GNSS positioning and upper-atmosphere
        research. The model remains robust even when direct NmF2/hmF2
        measurements are unavailable, improving applicability in data-
        sparse regions. Accurate modeling of topside ionospheric
        electron density is essential for improving GNSS positioning and
        understanding upper-atmosphere dynamics. A new four-dimensional
        (spatial and temporal) topside electron density model is
        developed using global GNSS radio occultation data within an
        L2-regularized artificial neural network framework. The model
        combines both empirical and physical variables, including
        geomagnetic coordinates, temporal parameters, solar flux
        (F10.7), geomagnetic activity index (Kp), and key ionospheric
        parameters (NmF2 and hmF2). To support the modeling framework,
        two sub-models are first constructed to estimate NmF2 and hmF2
        when direct measurements are unavailable. The full model is
        trained using COSMIC-1 data and evaluated against independent
        datasets, including COSMIC-1, GRACE, and incoherent scatter
        radar (ISR). The results show that the proposed sub-models
        reduce relative errors by 4.5\% for hmF2 and 11.0\% for NmF2
        compared with IRI-2016. For the full topside Ne modeling, the
        proposed approach achieves improvements of 35\%, 36\%, and 53\%
        relative to IRI-2016 when evaluated against COSMIC-1, GRACE, and
        ISR datasets, respectively. A systematic analysis of input
        variables further indicates that both physical drivers and
        ionospheric structural parameters play essential roles in
        determining model performance. The new model incorporated with
        NmF2 and hmF2 sub-models still achieves a 16\% improvement over
        IRI-2016 based on ISR data. In addition to statistical
        improvements, the model reproduces key ionospheric features,
        including the equatorial ionization anomaly (EIA) and the
        midlatitude summer nighttime anomaly (MSNA), under different
        solar activity conditions. These results demonstrate that the
        proposed model captures not only the statistical variability but
        also the underlying physical behavior of the topside ionosphere.}",
          doi = {10.3390/rs18122002},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.2002H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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