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CAO Qingpeng, HUANG Liupeng, WEI Chunbo, GU Defeng. Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1-11 doi: 10.11728/cjss2025.06.2024-0179
Citation: CAO Qingpeng, HUANG Liupeng, WEI Chunbo, GU Defeng. Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1-11 doi: 10.11728/cjss2025.06.2024-0179
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Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN

doi: 10.11728/cjss2025.06.2024-0179 cstr: 32142.14.cjss.2024-0179
  • 1.

    School of Artificial Intelligence, Sun Yat-Sen University, Zhuhai 519082

  • 2.

    National Time Service Center, Chinese Academy of Sciences, Xi’an 710600

  • 3.

    Sun Yat-sen University, MOE Key Laboratory of TianQin Mission/ Frontiers Science Center for TianQin / CNSA Research Center for Gravitational Waves, Zhuhai 519082

  • Received Date: 2024-12-05
  • Rev Recd Date: 2025-05-25
    • Available Online: 2025-05-26
    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 RNNs. By analyzing the relationship between atmospheric density and external environmental parameters such as Ap, F10.7, and F10.7a, 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.

     

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