基于SWARM-C卫星数据对HASDM模型的热层大气密度误差分析

    Error Analysis of Thermosphere Atmospheric Density for HASDM Method Based on SWARM-C Satellite Data

    • 准确计算大气密度对卫星及空间碎片的精密轨道预报至关重要. 基于2014-2019年SWARM-C卫星加速度计反演的大气密度数据, 分析高精度大气模型(High Accuracy Satellite Drag Model, HASDM)的误差特性, 及其在不同空间环境下的性能差异. 结果显示, 太阳活动对HASDM影响显著, 中高太阳活动年模型平均偏差约为12.5%, 标准差约为0.2; 低太阳活动年偏差增大至约18.7%, 标准差增大至约0.4; 地磁活动期间, 模型整体偏差稳定在17%左右, 标准差约达0.4; 纬度分布上, 极区偏差最低, 为5%~10%, 但南极高纬标准差高于北极; 赤道区域偏差最大, 为20%~30%; 地方时分布上, 03:00-06:00 LST与18:00-24:00 LST的误差峰值达20%; 磁暴期间, HASDM在初相易高估密度, 主相误差波动剧烈, 恢复相逐渐趋稳. 本研究为改进大气密度模型的太阳活动参数化和区域性校准提供了关键依据.

       

      Abstract: Accurate thermosphere density modeling is a prerequisite for reliable orbit prediction of satellites and space debris, particularly under the growing demands of modern space traffic management in low Earth orbit. This study systematically evaluates the performance of the High Accuracy Satellite Drag Model (HASDM) using thermosphere density data retrieved from SWARM-C satellite accelerometer measurements spanning the period 2014-2019. The analysis investigates model bias and variability in response to different solar and geomagnetic activity levels, as well as latitude and local time dependencies. Results indicate that solar activity exerts a marked influence on model performance: during moderate to high solar activity years, HASDM exhibits a mean bias of approximately 12.5% with a standard deviation near 0.2, whereas under low solar activity conditions, the bias increases to 18.7% and the standard deviation rises to 0.4. During geomagnetic disturbances, the model maintains an average bias about 17%, though with an elevated standard deviation, particularly during the main phase of storms. In terms of spatial distribution, polar regions demonstrate the lowest bias (5%~10%), with relatively larger variability in the southern hemisphere; conversely, equatorial regions present the highest biases, ranging between 20% and 30%. The diurnal pattern further reveals peak modeling errors during 03:00-06:00 LST and 18:00-24:00 LST, highlighting limitations in representing nighttime density variations. Additionally, during geomagnetic storms, HASDM tends to overestimate density in the initial phase, displays significant fluctuations in the main phase, and gradually stabilizes during recovery. These findings highlight systematic deficiencies in existing empirical parameterizations and suggest the necessity of incorporating enhanced solar-geophysical proxies and regionally adaptive corrections.

       

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