利用深度学习技术预测南半球电势模式
Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques
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中文总结 AI 辅助
本研究比较三种深度学习模型预测南半球高纬度电势图，发现扩散模型在长时域和动态事件中优于确定性模型，为空间天气预测提供新方向。
中文摘要 AI 辅助
由太阳风驱动的空间天气扰动会降低卫星导航精度、干扰无线电通信并威胁电力基础设施，因此准确预测高纬度电离层的响应成为一项关键的操作需求。现有方法，包括经验气候模型和基于物理的磁流体动力学模拟，要么平滑了电离层随时间变化的非线性响应，要么计算成本过高而无法实时使用，而先前的机器学习工作大多针对标量指数而非电离层对流的完整空间结构。在此，我们训练并比较了三种深度学习架构：一种以L1拉格朗日点的多变量太阳风和行星际磁场测量为条件的概率扩散模型、一种无条件扩散消融模型，以及一种确定性U-Net基线模型，用于预测源自SuperDARN雷达观测的南半球高纬度电势图。利用与DSCOVR L1测量同步的五年（2020-2025年）SuperDARN数据，我们在单次通过模式、长达350帧的扩展自回归滚动预测，以及对训练期间未见过的2015年3月圣帕特里克节强烈风暴进行的分布外案例研究中评估了这些模型。我们发现，确定性和概率方法的相对优势并非固定不变：确定性模型在短预测时域和平静条件下具有竞争力，而扩散模型的优势随着预测时域延长和事件变得更加动态而增长并最终占据主导，这证明基于概率的样本生成模型是面向操作性的长时域空间天气预测的更有前景的方向。
英文摘要
Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure, making accurate forecasting of the high-latitude ionosphere's response a critical operational need. Existing approaches, empirical climatological models and physics-based magnetohydrodynamic simulations, either smooth out the ionosphere's time-dependent, non-linear response or are too computationally expensive for real-time use, while prior machine learning efforts have mostly targeted scalar indices rather than the full spatial structure of ionospheric convection. Here we train and compare three deep learning architectures, a probabilistic diffusion model conditioned on multi-variate solar wind and interplanetary magnetic field measurements at the L1 Lagrange point, an unconditioned diffusion ablation, and a deterministic U-Net baseline, to forecast Southern Hemisphere high-latitude electric potential maps derived from SuperDARN radar observations. Using five years (2020--2025) of SuperDARN data synchronised with DSCOVR L1 measurements, we evaluate the models under a single-pass regime, an extended autoregressive rollout of up to 350 frames, and an out-of-distribution case study on the intense March 2015 St.\ Patrick's Day storm, unseen during training. We find that the relative advantage of the deterministic and probabilistic approaches is not fixed: the deterministic model is competitive over short horizons and calm conditions, while the diffusion model's advantage grows and eventually dominates as the forecast horizon lengthens and the event becomes more dynamic, evidence that probabilistic, sample-based generative models are the more promising direction for operational, long-horizon space weather forecasting.
发表机构
- Istituto Nazionale di Geofisica e Vulcanologia（意大利国家地球物理与火山学研究所）
- Lancaster University（兰卡斯特大学）
- Italian National Institute of Astrophysics（意大利国家天体物理研究所）
- University of Applied Sciences and Arts Nortwestern Switzerland（瑞士西北应用科学与艺术大学）
- University of Geneva（日内瓦大学）
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