Publications related to the GRACE Missions (no abstracts)

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Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN

Cao, Qingpeng, Huang, Liupeng, Wei, Chunbo, and Gu, Defen), 2025. Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN. Chinese Journal of Space Science, 45(6):1460–1470, doi:10.11728/cjss2025.06.2024-0179.

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@ARTICLE{2025ChJSS..45.1460C,
       author = {{Cao}, Qingpeng and {Huang}, Liupeng and {Wei}, Chunbo and {Gu}, Defen)},
        title = "{Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN}",
      journal = {Chinese Journal of Space Science},
     keywords = {Atmospheric density, Empirical density model, Neural network, Model calibration, GRACE accelerometer data},
         year = 2025,
        month = nov,
       volume = {45},
       number = {6},
        pages = {1460-1470},
     abstract = "{Atmospheric drag is the largest non-gravitational perturbation
        experienced by low-orbit satellites, and the main source of
        error in calculating atmospheric drag stems from inaccuracies in
        the empirical models of thermospheric density. Currently, these
        empirical models generally exhibit errors exceeding 30\%. To
        enhance the prediction accuracy of these models, a calibration
        method for thermospheric density empirical models based on
        Segment Recurrent Neural Network (SegRNN) is proposed. This
        method employs the segmentation and parallelism strategies of
        SegRNN for model training and inference, mitigating the issues
        of error accumulation and gradient instability that arise from
        excessive iterations in traditional RNN. By analyzing the
        relationship between atmospheric density and external
        environmental parameters such as Ap, F$_{10.7}$, and
        F$_{10.7a}$, an improved neural network architecture named
        SegRNN with Residual Block is proposed. This architecture
        introduces external environmental parameters as dynamic
        covariates and employs a residual block to encode these
        covariates, thereby extracting density-related information for
        the prediction period and further enhancing the prediction
        accuracy of SegRNN. Finally, the density data derived from the
        onboard accelerometer of the GRACE (Gravity Recovery and Climate
        Experiment) satellite is used to calibrate the NRLMSIS 2.0
        model. The results indicate that the original error of the
        NRLMSIS 2.0 model is 31.3\%. After calibration with SegRNN, the
        error was reduced to 8.0\%. By introducing dynamic covariates,
        the model error was further reduced to 7.2\%. Ultimately, the
        error of the final calibrated model decreased by 24.1\%,
        demonstrating significant calibration effects.}",
          doi = {10.11728/cjss2025.06.2024-0179},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025ChJSS..45.1460C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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