发表机构
Rutgers, The State University of New Jersey（新泽西州立罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AETHER-P3 Nowcast v1.0是一种基于深度证据回归的全球热层密度模型，通过状态感知采样构建训练集，在多种条件下性能优于或媲美现有模型，为CCMC提供可靠预测。
AI 中文摘要
AETHER-P3 Nowcast v1.0是一个基于机器学习的全球热层中性密度模型，专为低地球轨道应用而开发，并准备接入美国国家航空航天局的社区协调建模中心（CCMC）。该模型利用深度证据回归框架，在因果太阳、太阳风、地磁、空间、时间和经验模型输入的驱动下，提供逐点中性密度估计以及预测不确定性。本报告记录了已发布模型的配置、训练数据构建、软件可追溯性、输出产品、验证策略、基准性能和已知局限性。训练档案结合了来自CHAMP、GRACE-A、GOCE、Swarm-C和GRACE-FO的加速度计和任务衍生的密度观测数据，时间跨度为2000年至2023年。超过4000万个符合条件的30秒观测数据可用，但该档案主要由连续的静默期测量数据主导。为了保留物理上重要状态的覆盖范围，最终构建了包含167万个样本的训练集，采用确定性的状态感知采样方法，逐步对静默条件进行下采样，同时保留所有可用的极端条件观测数据。验证使用时间上不重叠的按时间顺序分块，并设置排除保护以减少信息泄漏。在静默、中等和极端条件下的评估显示，相对于HASDM、JB2008、NRLMSISE-00和可用的WAM-IPE案例，该模型具有竞争力的性能，同时也识别了与稀疏训练覆盖、任务相关密度产品和条件相关不确定性校准相关的局限性。本报告提供了AETHER-P3 Nowcast v1.0研究版本及其当前CCMC接入配置的可复现技术描述。
英文摘要
AETHER-P\textsuperscript{3} Nowcast v1.0 is a machine-learning-based global thermospheric neutral-density model developed for low-Earth-orbit applications and prepared for onboarding to NASA's Community Coordinated Modeling Center (CCMC). The model provides pointwise neutral-density estimates together with predictive uncertainty using a deep evidential regression framework driven by causal solar, solar-wind, geomagnetic, spatial, temporal, and empirical-model inputs. This report documents the released model configuration, training-data construction, software traceability, output products, validation strategy, benchmark performance, and known limitations. The training archive combines accelerometer- and mission-derived density observations from CHAMP, GRACE-A, GOCE, Swarm-C, and GRACE-FO spanning 2000--2023. More than 40 million eligible 30-s observations are available, but the archive is strongly dominated by consecutive quiet-time measurements. To preserve coverage of physically important regimes, the final 1.67-million-sample training set is constructed using deterministic regime-aware sampling that progressively subsamples quiet conditions while retaining all available extreme-condition observations. Validation uses temporally disjoint chronological blocks with exclusion guards to reduce information leakage. Evaluation across quiet, moderate, and extreme conditions shows competitive performance relative to HASDM, JB2008, NRLMSISE-00, and available WAM-IPE cases, while also identifying limitations associated with sparse training coverage, mission-dependent density products, and condition-dependent uncertainty calibration. The report provides a reproducible technical description of the AETHER-P\textsuperscript{3} Nowcast v1.0 research release and its current CCMC onboarding configuration.
