通过全面回测验证莱昂德空间卫星寿命预测
Validation of Satellite Lifetime Predictions at Leonid Space
AI总结：
本文通过回测934颗非机动卫星的数据，验证了莱昂德空间卫星寿命预测管道的准确性，展示了预测空间天气条件下的预测性能，并比较了不同工具链的精度。
摘要：
我们通过全面回测934颗非机动卫星的数据，验证了莱昂德空间卫星寿命预测管道的准确性。该研究首次利用预测的空间天气条件而非历史经验进行大规模验证。我们的工具链结合了轨道数据中的球面系数估计与在不同环境条件下进行的概率轨道传播。使用涵盖六个太阳周期的TLE数据和空间天气记录，我们的三阶段验证方法逐步去除历史偏见，以达到完全预测的运营条件。在完美知识条件下，我们实现了1年预测准确度（中位连续概率评分）为6.0天（1.6%），在估计球面系数和已知空间天气条件下为18.6天（5.1%），在完全预测条件下为45.5天（12.4%）。与ESA的标准DRAMA & DISCOS工具链相比，我们展示了对良好表征卫星的4倍精度提升，以及对NASA DAS软件的8倍提升。定制的半解析传播器提供了比Orekit快340倍、比DRAMA快55倍的速度提升，使在大规模卫星群体上进行快速蒙特卡洛分析成为可能。我们的分析表明，太阳周期预测主导了误差预算，更高精度的传播器和大气模型提供微小的收益。这些结果为支持LEO任务规划和监管合规的运营寿命预测服务建立了验证的性能基准。
英文摘要：
This paper validates Leonid Space's satellite lifetime prediction pipeline through a backtesting campaign of 1283 deorbited satellites spanning six decades of spaceflight, showing that it accurately recreates their last year of flight. The pipeline combines a custom high-speed orbit propagator, ballistic coefficient estimation from on-orbit TLE data, atmospheric density corrections from data assimilation, custom space weather forecasts, and probabilistic Monte Carlo analysis. Validation proceeds in four steps that progressively remove the benefit of hindsight: starting from a "perfect knowledge" scenario using actual space weather data and ephemerides, we gradually replace historical data with contemporary predictions to arrive at a fully predictive scenario analogous to a deorbit prediction performed for a spacecraft flying today. These forecasts are scored using the continuously ranked probability score and root mean square calibration error, yielding a median one-year prediction accuracy of 2.4 days (0.7%) with perfect knowledge and 35.3 days (9.7%) in the fully predictive scenario. Solar flux forecasts contribute 75% of the variance in our predictions, dominating all other sources of error. To our knowledge, this is the first comprehensive backtesting of long-term satellite lifetime predictions that reports quantitative results for the fully predictive case, and it establishes a new framework for scoring solar cycle forecasts by grounding forecast accuracy in operationally-relevant satellite lifetime predictions.
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