Calibration of Thermospheric Atmospheric Density Empirical Model Based on SegRNN
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摘要: 大气阻力是低轨卫星受到的最大非引力摄动,大气阻力的计算误差主要来源于热层大气密度经验模型误差,目前经验模型的误差较大普遍在30%以上。为了提高经验模型的预报精度,提出了一种基于SegRNN(Segment Recurrent Neural Network)的热层大气密度经验模型校准方法。该方法使用SegRNN的分块和并行策略进行模型训练和推理,规避了传统RNN因迭代次数过多而引发的误差累积与梯度不稳定的问题。通过分析大气密度与Ap、F10.7和F10.7a外部环境参数的变化关系,提出了一种改进的神经网络架构“SegRNN with Residual Block”。该架构通过引入外部环境参数作为动态协变量(Covariates),并使用Residual Block提取预报时段的密度相关信息,从而进一步提高了SegRNN的预报精度。最后利用GRACE(gravity recovery and climate experiment)星载加速度计反演得到的密度数据对NRLMSISE 2.0进行了校准实验。结果表明:NRLMSIS 2.0模型原始误差为31.3%,经SegRNN校准后,误差降低至8.0%,引入动态协变量后,模型误差进一步降低至7.2%,最终校准模型误差下降了24.1%,校准效果显著。
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关键词:
- 大气密度 /
- 经验密度模型 /
- 神经网络 /
- 模型校准 /
- GRACE卫星加速度计数据
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 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 NRLMSISE 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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