AI 中文总结
本研究提出基于机器学习的AETHER-P3模型，以序列到序列回归实现全球热层密度6小时内多步预测，在不同地磁条件下表现出高预测技能，可支持近地轨道相关业务决策。
AI 中文摘要
热层质量密度决定近地轨道的空气动力阻力，是轨道预测和 conjunction assessment（ conjunction assessment 译为“碰撞风险评估”）的主要不确定性来源，尤其在地磁扰动期间。我们提出 AETHER-P3（Accelerometer-driven Estimation of THERmospheric density-A Physics-Informed Probabilistic Prediction Platform，即加速度计驱动的热层密度估计——物理启发概率预测平台），这是一种基于机器学习的全球热层密度预测模型，采用3小时输入窗口，提供最长6小时的多步预测，并输出预测不确定性估计。AETHER-P3将热层密度预测建模为序列到序列回归任务，其条件为近期空间天气演变及用户指定的未来时间与位置序列。为增强物理一致性和泛化能力，AETHER-P3纳入在未来位置评估的 JB2008 和 NRLMSISE-00 密度估计，以及太阳、地磁和太阳风驱动因素。该网络采用双循环编码器和证据正态-伽马输出头，联合估计预测均值和不确定性。模型使用涵盖地磁平静、中等和极端条件的独立卫星测试案例进行评估：平静期，AETHER-P3 达到高预测技能（R=0.95）；中等活动下，仍保持强技能（R=0.93），且比经验基线模型的物理域误差更小；极端风暴条件下，确定性预测技能如预期下降但仍稳健（R=0.89-0.90）。预测不确定性在所有工况下均保持良好校准。这些结果表明，AETHER-P3 是一种实用、低延迟、感知不确定性的热层密度预测能力，支持轨道预测、阻力风险评估和业务决策，其验证高度范围约为300-520公里，在数据丰富的400-520公里区域置信度最高。
英文摘要
Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER-P3 (Accelerometer-driven Estimation of THERmospheric density-A Physics-Informed Probabilistic Prediction Platform), a machine-learning-based global thermospheric density forecasting model that provides multi-step forecasts up to 6 hr ahead using a 3-hr input window, with predictive uncertainty estimates. AETHER-P3 formulates thermospheric density forecasting as a sequence-to-sequence regression task conditioned on recent space weather evolution and a user-specified sequence of future times and locations. To enhance physical consistency and generalization, AETHER-P3 incorporates JB2008 and NRLMSISE-00 density estimates evaluated at future locations, along with solar, geomagnetic, and solar-wind drivers. The network employs dual recurrent encoders and an evidential Normal-Gamma output head to jointly estimate forecast mean and uncertainty. The model is evaluated using independent satellite test cases spanning quiet, moderate, and extreme geomagnetic conditions. During quiet periods, AETHER-P3 achieves high forecast skill (R=0.95). Under moderate activity, strong skill is retained (R=0.93), with reduced physical-domain errors than empirical baseline models. During extreme storm conditions, deterministic forecast skill degrades as expected yet remains robust (R=0.89-0.90). Predictive uncertainty remains well calibrated across all regimes. These results establish AETHER-P3 as a practical, low-latency, uncertainty-aware capability for thermospheric density forecasting that supports orbit prediction, drag-risk assessment, and operational decision-making over its validated altitude range of approximately 300-520 km, with highest confidence in the data-rich 400-520 km region.
Journal refSpace Weather, 24(6), e2026SW000000 (2026)
