PreMevE-MEO：利用 GPS 卫星观测预测超相对论电子
PreMevE-MEO: Predicting Ultra-relativistic Electrons Using Observations from GPS Satellites
AI总结：
本文提出 PreMevE-MEO 模型，结合 GPS 等卫星的 MEO 观测数据与创新的 SmileNet 网络，实现外辐射带超相对论电子通量的高精度每小时现报，为空间天气预警提供了实用工具。
摘要：
能量大于或等于 2 兆电子伏特（MeV）的超相对论电子对星载电子设备构成重大辐射威胁，因此确定这些高能电子对空间天气界具有重要意义。本文报告了我们在开发外辐射带 MeV 电子预测模型方面的最新进展。新版本主要由中地球轨道（MEO）上的电子测量驱动，称为 PREdictive MEV Electron (PreMevE)-MEO 模型，可对整个外辐射带的超相对论电子通量分布进行现报。模型输入包括由一组 GPS 卫星在 MEO 观测到的 2 MeV 以上电子通量，以及一颗 Los Alamos 卫星在地球同步轨道上测量的电子数据。我们开发了一种创新的 Sparse Multi-Inputs Latent Ensemble NETwork (SmileNet)，它将卷积神经网络与 transformers 相结合，并使用来自 NASA Van Allen Probes 任务的长期原位电子数据来训练、验证、优化和测试该模型。结果表明，PreMevE-MEO 能够提供每小时现报，具有高模型性能效率且与观测结果高度相关。这一 PreMevE-MEO 原型模型证明了利用 MEO 长期空间基础设施观测进行高保真预测的可行性，因此具有成长为宝贵空间天气业务预警工具的巨大潜力。
英文摘要：
Ultra-relativistic electrons with energies greater than or equal to two megaelectron-volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium-Earth-orbits (MEO), is called PREdictive MEV Electron (PreMevE)-MEO model that nowcasts ultra-relativistic electron flux distributions across the whole outer belt. Model inputs include above 2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi-Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long-term in-situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model.It is shown that PreMevE-MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations.This prototype PreMevE-MEO model demonstrates the feasibility of making high-fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.
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