P2D - 一种二维多航天器太阳风持续性模型

中文摘要 AI 辅助

背景太阳风是空间天气预测中的关键组成部分，因为它包含对地球有效的高速流，并为日冕物质抛射在行星际空间中的传播和演化提供介质。由于太阳自转和大尺度太阳风结构的缓慢演化，太阳风特性表现出约27天的自相关周期，尤其是在太阳活动极小期。我们利用这一特性开发了一种太阳风持续性模型，其输入来自多个航天器（太阳轨道器、帕克太阳探测器、STEREO-A、STEREO-B和OMNI数据库）。该模型将原位数据从其测量位置径向远离太阳进行弹道传播，同时以太阳自转速率纵向传播，生成太阳风参数的二维图。这些图可以在日球层的任意位置提取，用于太阳风重建。从地球的视角来看，该重建在速度和密度方面的平均绝对误差（MAE）分别为65.41 km/s和3.51/cm^3。在高速流期间，速度和密度的峰值命中率分别为50%和48%。当航天器位于地球与拉格朗日点L5之间时，该模型表现最佳。在这些时期，预测太阳风速度的平均绝对误差与基准的27天持续性模型相比降低了约35%。因此，未来的L5任务（如Vigil）预计将为可靠的持续性预测提供坚实基础。

英文摘要

The background solar wind is a key component in space weather forecasting, as it contains geoeffective high-speed streams and provides the medium through which coronal mass ejections propagate and evolve in interplanetary space. Due to the solar rotation and the slow evolution of large-scale solar wind structures, solar wind properties exhibit an autocorrelation with a period of roughly 27 days, particularly at solar minimum. We made use of this property to develop a solar wind persistence model with input from multiple spacecraft (Solar Orbiter, Parker Solar Probe, STEREO-A, STEREO-B and the OMNI database). The model ballistically propagates in-situ data from the position of their measurement radially away from the Sun, as well as longitudinally with the solar rotation rate, producing 2D maps of solar wind parameters. These can be extracted at any point in the heliosphere for a solar wind reconstruction. From Earth's perspective, the reconstruction performs with an MAE of 65.41 km/s and 3.51/cm^3 for speed and density, respectively. During high-speed streams, the peak hit rate is 50% and 48% for speed and density. It performs best during times when there are spacecraft located between Earth and Lagrange point L5. During these times, the mean absolute error of the predicted solar wind speed decreases by roughly 35% in comparison to the benchmark 27-day persistence model. Therefore, future L5 missions like Vigil are expected to provide a robust basis for reliable persistence forecasts.

发表机构

• University of Graz（格拉茨大学）
• University of Helsinki（赫尔辛基大学）
• University of Reading（雷丁大学）
• Columbia University（哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。